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"Fast 4D Mesh Generation by Spatio-Temporal Attention Chains" TL;DR: training-free spatio-temporal attention chains generate topology-consistent 4D meshes from video 13× faster while improving temporal correspondence quality

Alexandre Morgand

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13,743 views • 2 months ago •via X (Twitter)

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We’ve upgraded Stable Video Diffusion 4D to Stable Video 4D 2.0 (SV4D 2.0), improving the quality of 4D outputs generated from a single object-centric video. While 3D provides a static view of an object’s shape and size; 4D extends this by including time, showing how the object moves. This multi-view video diffusion model generates a 4D output in three steps: 1️⃣ Starts with an input video of a moving person or object 2️⃣ Generates novel views of the subject from unseen angles 3️⃣ Constructs a single dynamic 4D video output with spatial and temporal consistency You can learn more here: (1/4)

We’ve upgraded Stable Video Diffusion 4D to Stable Video 4D 2.0 (SV4D 2.0), improving the quality of 4D outputs generated from a single object-centric video. While 3D provides a static view of an object’s shape and size; 4D extends this by including time, showing how the object moves. This multi-view video diffusion model generates a 4D output in three steps: 1️⃣ Starts with an input video of a moving person or object 2️⃣ Generates novel views of the subject from unseen angles 3️⃣ Constructs a single dynamic 4D video output with spatial and temporal consistency You can learn more here: (1/4)

Stability AI

36,041 views • 1 year ago

[NeurIPS '24] DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation Abstract (excerpt) We introduce DreamMesh4D, a novel framework that combines mesh representation with sparse-controlled deformation technique to generate high-quality 4D object from a monocular video. To overcome the limitation of classical texture representation, we bind Gaussian splats to the surface of the triangular mesh for differentiable optimization of both the texture and mesh vertices. In particular, DreamMesh4D begins with a coarse mesh provided by a single image based 3D generation method. Sparse points are then uniformly sampled across the surface of the mesh, and are used to build a deformation graph to drive the motion of the 3D object for the sake of computational efficiency and providing additional constraint. For each step, transformations of sparse control points are predicted using a deformation network, and the mesh vertices as well as the bound surface Gaussians are deformed via a geometric skinning algorithm. The skinning algorithm is a hybrid approach combining LBS (linear blending skinning) and DQS (dual-quaternion skinning), mitigating drawbacks associated with both approaches. The static surface Gaussians and mesh vertices as well as the dynamic deformation network are learned via reference view photometric loss, score distillation loss as well as other regularization losses in a two-stage manner. Extensive experiments demonstrate that our method outperforms prior video-to-4D generation methods in terms of rendering quality and spatial-temporal consistency.

[NeurIPS '24] DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation Abstract (excerpt) We introduce DreamMesh4D, a novel framework that combines mesh representation with sparse-controlled deformation technique to generate high-quality 4D object from a monocular video. To overcome the limitation of classical texture representation, we bind Gaussian splats to the surface of the triangular mesh for differentiable optimization of both the texture and mesh vertices. In particular, DreamMesh4D begins with a coarse mesh provided by a single image based 3D generation method. Sparse points are then uniformly sampled across the surface of the mesh, and are used to build a deformation graph to drive the motion of the 3D object for the sake of computational efficiency and providing additional constraint. For each step, transformations of sparse control points are predicted using a deformation network, and the mesh vertices as well as the bound surface Gaussians are deformed via a geometric skinning algorithm. The skinning algorithm is a hybrid approach combining LBS (linear blending skinning) and DQS (dual-quaternion skinning), mitigating drawbacks associated with both approaches. The static surface Gaussians and mesh vertices as well as the dynamic deformation network are learned via reference view photometric loss, score distillation loss as well as other regularization losses in a two-stage manner. Extensive experiments demonstrate that our method outperforms prior video-to-4D generation methods in terms of rendering quality and spatial-temporal consistency.

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12,323 views • 1 year ago

Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution paper page: Text-based diffusion models have exhibited remarkable success in generation and editing, showing great promise for enhancing visual content with their generative prior. However, applying these models to video super-resolution remains challenging due to the high demands for output fidelity and temporal consistency, which is complicated by the inherent randomness in diffusion models. Our study introduces Upscale-A-Video, a text-guided latent diffusion framework for video upscaling. This framework ensures temporal coherence through two key mechanisms: locally, it integrates temporal layers into U-Net and VAE-Decoder, maintaining consistency within short sequences; globally, without training, a flow-guided recurrent latent propagation module is introduced to enhance overall video stability by propagating and fusing latent across the entire sequences. Thanks to the diffusion paradigm, our model also offers greater flexibility by allowing text prompts to guide texture creation and adjustable noise levels to balance restoration and generation, enabling a trade-off between fidelity and quality. Extensive experiments show that Upscale-A-Video surpasses existing methods in both synthetic and real-world benchmarks, as well as in AI-generated videos, showcasing impressive visual realism and temporal consistency.

Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution paper page: Text-based diffusion models have exhibited remarkable success in generation and editing, showing great promise for enhancing visual content with their generative prior. However, applying these models to video super-resolution remains challenging due to the high demands for output fidelity and temporal consistency, which is complicated by the inherent randomness in diffusion models. Our study introduces Upscale-A-Video, a text-guided latent diffusion framework for video upscaling. This framework ensures temporal coherence through two key mechanisms: locally, it integrates temporal layers into U-Net and VAE-Decoder, maintaining consistency within short sequences; globally, without training, a flow-guided recurrent latent propagation module is introduced to enhance overall video stability by propagating and fusing latent across the entire sequences. Thanks to the diffusion paradigm, our model also offers greater flexibility by allowing text prompts to guide texture creation and adjustable noise levels to balance restoration and generation, enabling a trade-off between fidelity and quality. Extensive experiments show that Upscale-A-Video surpasses existing methods in both synthetic and real-world benchmarks, as well as in AI-generated videos, showcasing impressive visual realism and temporal consistency.

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32,849 views • 2 years ago

New research with Tsinghua University: Spatial-TTT. A framework for streaming visual-based spatial intelligence with test-time training (TTT). Spatial-TTT adapts fast weights to capture and organize spatial evidence from long video streams, enabling models to build structured 3D spatial memory over time. Highlights: 🔹Efficient streaming memory. Fast weights act as compact spatial memory with sublinear memory growth over 7000+ frames and more than 40% lower compute. 🔹Spatial-predictive mechanism. TTT layers with 3D spatiotemporal convolution capture geometric correspondence and temporal continuity. 🔹SOTA results on long-horizon video spatial understanding (VSI-Bench). The paper ranked #1 on Hugging Face Daily Papers on March 13. Project page: GitHub: Paper: Model & Data:

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Tencent Hy

20,792 views • 5 months ago

V3D Video Diffusion Models are Effective 3D Generators Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency

V3D Video Diffusion Models are Effective 3D Generators Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency

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Fast View Synthesis of Casual Videos paper page: Novel view synthesis from an in-the-wild video is difficult due to challenges like scene dynamics and lack of parallax. While existing methods have shown promising results with implicit neural radiance fields, they are slow to train and render. This paper revisits explicit video representations to synthesize high-quality novel views from a monocular video efficiently. We treat static and dynamic video content separately. Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video. Our plane-based scene representation is augmented with spherical harmonics and displacement maps to capture view-dependent effects and model non-planar complex surface geometry. We opt to represent the dynamic content as per-frame point clouds for efficiency. While such representations are inconsistency-prone, minor temporal inconsistencies are perceptually masked due to motion. We develop a method to quickly estimate such a hybrid video representation and render novel views in real time. Our experiments show that our method can render high-quality novel views from an in-the-wild video with comparable quality to state-of-the-art methods while being 100x faster in training and enabling real-time rendering.

Fast View Synthesis of Casual Videos paper page: Novel view synthesis from an in-the-wild video is difficult due to challenges like scene dynamics and lack of parallax. While existing methods have shown promising results with implicit neural radiance fields, they are slow to train and render. This paper revisits explicit video representations to synthesize high-quality novel views from a monocular video efficiently. We treat static and dynamic video content separately. Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video. Our plane-based scene representation is augmented with spherical harmonics and displacement maps to capture view-dependent effects and model non-planar complex surface geometry. We opt to represent the dynamic content as per-frame point clouds for efficiency. While such representations are inconsistency-prone, minor temporal inconsistencies are perceptually masked due to motion. We develop a method to quickly estimate such a hybrid video representation and render novel views in real time. Our experiments show that our method can render high-quality novel views from an in-the-wild video with comparable quality to state-of-the-art methods while being 100x faster in training and enabling real-time rendering.

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20,668 views • 2 years ago

🚀 SANA-Video 2.0 is here! A full-stack optimized video model designed for efficiency — while still delivering high quality. We combine a hybrid architecture closely related to the recent Kimi K3 design, adopt Self-Flow from FLUX 3, and further accelerate it with our own Sol-Engine. Key technical ingredients: 🧠 Hybrid Linear–Softmax Attention 3 gated linear layers + 1 gated-softmax anchor (75% linear / 25% softmax) 🧱 Block Attention Residuals (AttnRes) Boosts deep-layer effective rank by ~12% 🏗️ Unified 5B & 14B models Trained from scratch under very limited resources: • 5B → only 16 nodes of H100 • 14B → 48 nodes of B200 Results 📊 • 84.30 VBench Total • 3.2× faster DiT forward than matched full-softmax at 720p/60s • 720p/5s in 13.06s on a single H100 with Sol-Engine • 120× faster than Wan 2.2-A14B under the same one-H100 setup SANA-Video 2.0 shows that high-quality 720p video generation can be both efficient and practical on a single GPU. 🎬 Project: 📄 Paper: 💻 Code: Proud of the team! 🎉 More details below 🧵

🚀 SANA-Video 2.0 is here! A full-stack optimized video model designed for efficiency — while still delivering high quality. We combine a hybrid architecture closely related to the recent Kimi K3 design, adopt Self-Flow from FLUX 3, and further accelerate it with our own Sol-Engine. Key technical ingredients: 🧠 Hybrid Linear–Softmax Attention 3 gated linear layers + 1 gated-softmax anchor (75% linear / 25% softmax) 🧱 Block Attention Residuals (AttnRes) Boosts deep-layer effective rank by ~12% 🏗️ Unified 5B & 14B models Trained from scratch under very limited resources: • 5B → only 16 nodes of H100 • 14B → 48 nodes of B200 Results 📊 • 84.30 VBench Total • 3.2× faster DiT forward than matched full-softmax at 720p/60s • 720p/5s in 13.06s on a single H100 with Sol-Engine • 120× faster than Wan 2.2-A14B under the same one-H100 setup SANA-Video 2.0 shows that high-quality 720p video generation can be both efficient and practical on a single GPU. 🎬 Project: 📄 Paper: 💻 Code: Proud of the team! 🎉 More details below 🧵

Enze Xie

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Generative video models are rapidly improving in quality. Meet Replay, a new AI model that can generate stunning videos from text. Replay v0.1 is designed to create ultrasmooth HD videos with a new interface. Available today for everyone. What's New? 1. Replay understands plain English prompts without prompt engineering. Try "rugged surfer" or "mermaid". 2. What's a movie without actors? Replay can crisply render close-ups of people and animals. 3. Free and fast generation from our homepage with no waiting list (link in bio). Behind the scenes, Replay is powered by aligning our new diffusion model for videos with the LLM behind Genmo Chat. We’re so excited to see what you create! Share your creations below👇

Generative video models are rapidly improving in quality. Meet Replay, a new AI model that can generate stunning videos from text. Replay v0.1 is designed to create ultrasmooth HD videos with a new interface. Available today for everyone. What's New? 1. Replay understands plain English prompts without prompt engineering. Try "rugged surfer" or "mermaid". 2. What's a movie without actors? Replay can crisply render close-ups of people and animals. 3. Free and fast generation from our homepage with no waiting list (link in bio). Behind the scenes, Replay is powered by aligning our new diffusion model for videos with the LLM behind Genmo Chat. We’re so excited to see what you create! Share your creations below👇

Genmo

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Today we're announcing the open-source release of HunyuanVideo-Foley, our new end-to-end Text-Video-to-Audio (TV2A) framework for generating high-fidelity audio.🚀 This tool empowers creators in video production, filmmaking, and game development to generate professional-grade audio that precisely aligns with visual dynamics and semantic context, addressing key challenges in V2A generation.🔊 Key Innovations: 🔹Exceptional Generalization: Trained on a massive 100k-hour multimodal dataset, the model generates contextually-aware soundscapes for a wide range of scenes, from natural landscapes to animated shorts. 🔹Balanced Multimodal Response: Our innovative multimodal diffusion transformer (MMDiT) architecture ensures the model balances video and text cues, generating rich, layered sound effects that capture every detail—from the main subject to subtle background elements. 🔹High-Fidelity Audio: Using a Representation Alignment (REPA) loss function and a powerful Audio VAE, we've improved generation stability and producing professional-grade audio, free of noise and inconsistencies. HunyuanVideo-Foley achieves SOTA on multiple benchmarks, surpassing all open-source models in audio quality, visual-semantic alignment, and temporal alignment. 👉Try it now: 🌐Project Page: 🔗Code: 📄Technical Report: 🤗Hugging Face:

Today we're announcing the open-source release of HunyuanVideo-Foley, our new end-to-end Text-Video-to-Audio (TV2A) framework for generating high-fidelity audio.🚀 This tool empowers creators in video production, filmmaking, and game development to generate professional-grade audio that precisely aligns with visual dynamics and semantic context, addressing key challenges in V2A generation.🔊 Key Innovations: 🔹Exceptional Generalization: Trained on a massive 100k-hour multimodal dataset, the model generates contextually-aware soundscapes for a wide range of scenes, from natural landscapes to animated shorts. 🔹Balanced Multimodal Response: Our innovative multimodal diffusion transformer (MMDiT) architecture ensures the model balances video and text cues, generating rich, layered sound effects that capture every detail—from the main subject to subtle background elements. 🔹High-Fidelity Audio: Using a Representation Alignment (REPA) loss function and a powerful Audio VAE, we've improved generation stability and producing professional-grade audio, free of noise and inconsistencies. HunyuanVideo-Foley achieves SOTA on multiple benchmarks, surpassing all open-source models in audio quality, visual-semantic alignment, and temporal alignment. 👉Try it now: 🌐Project Page: 🔗Code: 📄Technical Report: 🤗Hugging Face:

Tencent Hy

122,706 views • 1 year ago

VideoRF: Rendering Dynamic Radiance Fields as 2D Feature Video Streams paper page: Neural Radiance Fields (NeRFs) excel in photorealistically rendering static scenes. However, rendering dynamic, long-duration radiance fields on ubiquitous devices remains challenging, due to data storage and computational constraints. In this paper, we introduce VideoRF, the first approach to enable real-time streaming and rendering of dynamic radiance fields on mobile platforms. At the core is a serialized 2D feature image stream representing the 4D radiance field all in one. We introduce a tailored training scheme directly applied to this 2D domain to impose the temporal and spatial redundancy of the feature image stream. By leveraging the redundancy, we show that the feature image stream can be efficiently compressed by 2D video codecs, which allows us to exploit video hardware accelerators to achieve real-time decoding. On the other hand, based on the feature image stream, we propose a novel rendering pipeline for VideoRF, which has specialized space mappings to query radiance properties efficiently. Paired with a deferred shading model, VideoRF has the capability of real-time rendering on mobile devices thanks to its efficiency. We have developed a real-time interactive player that enables online streaming and rendering of dynamic scenes, offering a seamless and immersive free-viewpoint experience across a range of devices, from desktops to mobile phones.

VideoRF: Rendering Dynamic Radiance Fields as 2D Feature Video Streams paper page: Neural Radiance Fields (NeRFs) excel in photorealistically rendering static scenes. However, rendering dynamic, long-duration radiance fields on ubiquitous devices remains challenging, due to data storage and computational constraints. In this paper, we introduce VideoRF, the first approach to enable real-time streaming and rendering of dynamic radiance fields on mobile platforms. At the core is a serialized 2D feature image stream representing the 4D radiance field all in one. We introduce a tailored training scheme directly applied to this 2D domain to impose the temporal and spatial redundancy of the feature image stream. By leveraging the redundancy, we show that the feature image stream can be efficiently compressed by 2D video codecs, which allows us to exploit video hardware accelerators to achieve real-time decoding. On the other hand, based on the feature image stream, we propose a novel rendering pipeline for VideoRF, which has specialized space mappings to query radiance properties efficiently. Paired with a deferred shading model, VideoRF has the capability of real-time rendering on mobile devices thanks to its efficiency. We have developed a real-time interactive player that enables online streaming and rendering of dynamic scenes, offering a seamless and immersive free-viewpoint experience across a range of devices, from desktops to mobile phones.

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38,686 views • 2 years ago

Type a sentence, get any sound - from talking cats to singing saxophones. Brilliant release by NVIDIA ✨ NVIDIA just unveiled Fugatto, a groundbreaking 2.5B parameter audio AI model that can generate and transform any combination of music, voices, and sounds using text prompts and audio inputs Fugatto could ultimately allow developers and creators to bring sounds to life simply by inputting text prompts, → The model demonstrates unique capabilities like creating hybrid sounds (trumpet barking), changing accents/emotions in voices, and allowing fine-grained control over sound transitions - trained on millions of audio samples using 32 NVIDIA H100 GPUs 👨‍🔧 Architecture Built as a foundational generative transformer model leveraging NVIDIA's previous work in speech modeling and audio understanding. The training process involved creating a specialized blended dataset containing millions of audio samples → ComposableART's Innovation in Audio Control Introduces a novel technique allowing combination of instructions that were only seen separately during training. Users can blend different audio attributes and control their intensity → Temporal Interpolation Capabilities Enables generation of evolving soundscapes with precise control over transitions. Can create dynamic audio sequences like rainstorms fading into birdsong at dawn → Processes both text and audio inputs flexibly, enabling tasks like removing instruments from songs or modifying specific audio characteristics while preserving others → Shows capabilities beyond its training data, creating entirely new sound combinations through interaction between different trained abilities 🔍 Real-world Applications → Allows rapid prototyping of musical ideas, style experimentation, and real-time sound creation during studio sessions → Enables dynamic audio asset generation matching gameplay situations, reducing pre-recorded audio requirements → Can modify voice characteristics for language learning applications, allowing content delivery in familiar voices NVIDIA AI Developer

Type a sentence, get any sound - from talking cats to singing saxophones. Brilliant release by NVIDIA ✨ NVIDIA just unveiled Fugatto, a groundbreaking 2.5B parameter audio AI model that can generate and transform any combination of music, voices, and sounds using text prompts and audio inputs Fugatto could ultimately allow developers and creators to bring sounds to life simply by inputting text prompts, → The model demonstrates unique capabilities like creating hybrid sounds (trumpet barking), changing accents/emotions in voices, and allowing fine-grained control over sound transitions - trained on millions of audio samples using 32 NVIDIA H100 GPUs 👨‍🔧 Architecture Built as a foundational generative transformer model leveraging NVIDIA's previous work in speech modeling and audio understanding. The training process involved creating a specialized blended dataset containing millions of audio samples → ComposableART's Innovation in Audio Control Introduces a novel technique allowing combination of instructions that were only seen separately during training. Users can blend different audio attributes and control their intensity → Temporal Interpolation Capabilities Enables generation of evolving soundscapes with precise control over transitions. Can create dynamic audio sequences like rainstorms fading into birdsong at dawn → Processes both text and audio inputs flexibly, enabling tasks like removing instruments from songs or modifying specific audio characteristics while preserving others → Shows capabilities beyond its training data, creating entirely new sound combinations through interaction between different trained abilities 🔍 Real-world Applications → Allows rapid prototyping of musical ideas, style experimentation, and real-time sound creation during studio sessions → Enables dynamic audio asset generation matching gameplay situations, reducing pre-recorded audio requirements → Can modify voice characteristics for language learning applications, allowing content delivery in familiar voices NVIDIA AI Developer

Rohan Paul

96,354 views • 1 year ago

SORA by Hand ✍️ OpenAI’s #SORA took over the Internet when it was announced earlier this year. The technology behind Sora is the Diffusion Transformer (DiT) developed by William Peebles and Shining Xie. How does DiT work? 𝗚𝗼𝗮𝗹: Generate a video conditioned by a text prompt and a series of diffusion steps [1] Given ↳ Video ↳ Prompt: "sora is sky" ↳ Diffusion step: t = 3 [2] Video → Patches ↳ Divide all pixels in all frames into 4 spacetime patches [3] Visual Encoder: Pixels 🟨 → Latent 🟩 ↳ Multiply the patches with weights and biases, followed by ReLU ↳ The result is a latent feature vector per patch ↳ The purpose is dimension reduction from 4 (2x2x1) to 2 (2x1). ↳ In the paper, the reduction is 196,608 (256x256x3)→ 4096 (32x32x4) [4] ⬛ Add Noise ↳ Sample a noise according to the diffusion time step t. Typically, the larger the t, the smaller the noise. ↳ Add the Sampled Noise to latent features to obtain Noised Latent. ↳ The goal is to purposely add noise to a video and ask the model to guess what that noise is. ↳ This is analogous to training a language model by purposely deleting a word in a sentence and ask the model to guess what the deleted word was. [5-7] 🟪 Conditioning by Adaptive Layer Norm [5] Encode Conditions ↳ Encode "sora is sky" into a text embedding vector [0,1,-1]. ↳ Encode t = 3 to as a binary vector [1,1]. ↳ Concatenate the two vectors in to a 5D column vector. [6] Estimate Scale/Shift ↳ Multiply the combined vector with weights and biases ↳ The goal is to estimate the scale [2,-1] and shift [-1,5]. ↳ Copy the result to (X) and (+) [7] Apply Scale/Sift ↳ Scale the noised latent by [2,-1] ↳ Shifted the scaled noised latent by [-1, 5] ↳ The result is "conditioned" noise latent. [8-10] Transformer [8] Self-Attention ↳ Feed the conditioned noised latent to Query-Key function to obtain a self-attention matrix ↳ Value is omitted for simplicity [9] Attention Pooling ↳ Multiply the conditioned noised latent with the self-attention matrix ↳ The result are attention weighted features [10] Pointwise Feed Forward Network ↳ Multiply the attention weighted features with weights and biases ↳ The result is the Predicted Noise 🏋️‍♂️ 𝗧𝗿𝗮𝗶𝗻 [11] ↳ Calculate MSE loss gradients by taking the different between the Predicted Noise and the Sampled Noise (ground truth). ↳ Use the loss gradients to kick off backpropagation to update all learnable parameters (red borders) ↳ Note the visual encoder and decoder's parameters are frozen (blue borders) 🎨 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 (𝗦𝗮𝗺𝗽𝗹𝗲) [12] Denoise ↳ Subtract the predicted noise from the noised latent to obtain the noise-free latent [13] Visual Decoder: Latent 🟩 → Pixels 🟨 ↳ Multiply the patches with weights and biases, followed by ReLU [14] Patches → Video ↳ Rearrange patches into a sequence of video frames.

SORA by Hand ✍️ OpenAI’s #SORA took over the Internet when it was announced earlier this year. The technology behind Sora is the Diffusion Transformer (DiT) developed by William Peebles and Shining Xie. How does DiT work? 𝗚𝗼𝗮𝗹: Generate a video conditioned by a text prompt and a series of diffusion steps [1] Given ↳ Video ↳ Prompt: "sora is sky" ↳ Diffusion step: t = 3 [2] Video → Patches ↳ Divide all pixels in all frames into 4 spacetime patches [3] Visual Encoder: Pixels 🟨 → Latent 🟩 ↳ Multiply the patches with weights and biases, followed by ReLU ↳ The result is a latent feature vector per patch ↳ The purpose is dimension reduction from 4 (2x2x1) to 2 (2x1). ↳ In the paper, the reduction is 196,608 (256x256x3)→ 4096 (32x32x4) [4] ⬛ Add Noise ↳ Sample a noise according to the diffusion time step t. Typically, the larger the t, the smaller the noise. ↳ Add the Sampled Noise to latent features to obtain Noised Latent. ↳ The goal is to purposely add noise to a video and ask the model to guess what that noise is. ↳ This is analogous to training a language model by purposely deleting a word in a sentence and ask the model to guess what the deleted word was. [5-7] 🟪 Conditioning by Adaptive Layer Norm [5] Encode Conditions ↳ Encode "sora is sky" into a text embedding vector [0,1,-1]. ↳ Encode t = 3 to as a binary vector [1,1]. ↳ Concatenate the two vectors in to a 5D column vector. [6] Estimate Scale/Shift ↳ Multiply the combined vector with weights and biases ↳ The goal is to estimate the scale [2,-1] and shift [-1,5]. ↳ Copy the result to (X) and (+) [7] Apply Scale/Sift ↳ Scale the noised latent by [2,-1] ↳ Shifted the scaled noised latent by [-1, 5] ↳ The result is "conditioned" noise latent. [8-10] Transformer [8] Self-Attention ↳ Feed the conditioned noised latent to Query-Key function to obtain a self-attention matrix ↳ Value is omitted for simplicity [9] Attention Pooling ↳ Multiply the conditioned noised latent with the self-attention matrix ↳ The result are attention weighted features [10] Pointwise Feed Forward Network ↳ Multiply the attention weighted features with weights and biases ↳ The result is the Predicted Noise 🏋️‍♂️ 𝗧𝗿𝗮𝗶𝗻 [11] ↳ Calculate MSE loss gradients by taking the different between the Predicted Noise and the Sampled Noise (ground truth). ↳ Use the loss gradients to kick off backpropagation to update all learnable parameters (red borders) ↳ Note the visual encoder and decoder's parameters are frozen (blue borders) 🎨 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 (𝗦𝗮𝗺𝗽𝗹𝗲) [12] Denoise ↳ Subtract the predicted noise from the noised latent to obtain the noise-free latent [13] Visual Decoder: Latent 🟩 → Pixels 🟨 ↳ Multiply the patches with weights and biases, followed by ReLU [14] Patches → Video ↳ Rearrange patches into a sequence of video frames.

Tom Yeh

238,303 views • 2 years ago

XAI EXTENDS GROK IMAGINE VIDEO GENERATION TO 10 SECONDS WITH QUALITY ENHANCEMENTS xAI has updated its Grok Imagine tool to produce videos lasting 10 seconds, doubling the prior limit. This change, along with refinements in visual and audio elements, expands the tool's utility for short-form content creation. xAI released the upgrade to Grok Imagine in early 2026. The company, founded by Elon Musk, announced the feature through posts on the X platform. This follows previous iterations where videos were capped at shorter durations, typically 5 seconds. Grok Imagine allows users to generate videos from text prompts, building on its image creation capabilities. The update addresses constraints in video length that limited expressive potential. Users can now input descriptions to create clips, such as animations or scenes, without needing initial images. This positions the tool within the broader landscape of AI-driven multimodal generation, where text-to-video systems are increasingly common. The core adjustment doubles the maximum video duration from 5 seconds to 10 seconds. Accompanying this are upgrades to video quality, including more stable visuals, richer details, and improved clarity. Audio has also been enhanced for better output, making the generated content more immersive. These changes were described as "big improvements across the board" in the announcement. No specific benchmarks or quantitative metrics for the quality improvements were detailed in the release statements. The feature rollout appears gradual, with some users accessing it via the Grok app or web interface. xAI has not introduced user controls for exact timing, though such options are mentioned as future possibilities. This development highlights xAI's emphasis on iterative enhancements in generative AI tools. By extending duration while refining output fidelity, it reflects engineering priorities aimed at balancing computational efficiency with user needs. The focus on audio and visual stability suggests attention to common pitfalls in early text-to-video models, such as inconsistencies or artifacts. The sources do not specify the underlying model architecture changes or training data adjustments enabling this upgrade. Performance in real-world scenarios, like handling complex prompts or maintaining consistency across clips, remains unquantified in the announcements. Interpretations of broader implications for AI video generation would require additional evidence beyond what's provided.

XAI EXTENDS GROK IMAGINE VIDEO GENERATION TO 10 SECONDS WITH QUALITY ENHANCEMENTS xAI has updated its Grok Imagine tool to produce videos lasting 10 seconds, doubling the prior limit. This change, along with refinements in visual and audio elements, expands the tool's utility for short-form content creation. xAI released the upgrade to Grok Imagine in early 2026. The company, founded by Elon Musk, announced the feature through posts on the X platform. This follows previous iterations where videos were capped at shorter durations, typically 5 seconds. Grok Imagine allows users to generate videos from text prompts, building on its image creation capabilities. The update addresses constraints in video length that limited expressive potential. Users can now input descriptions to create clips, such as animations or scenes, without needing initial images. This positions the tool within the broader landscape of AI-driven multimodal generation, where text-to-video systems are increasingly common. The core adjustment doubles the maximum video duration from 5 seconds to 10 seconds. Accompanying this are upgrades to video quality, including more stable visuals, richer details, and improved clarity. Audio has also been enhanced for better output, making the generated content more immersive. These changes were described as "big improvements across the board" in the announcement. No specific benchmarks or quantitative metrics for the quality improvements were detailed in the release statements. The feature rollout appears gradual, with some users accessing it via the Grok app or web interface. xAI has not introduced user controls for exact timing, though such options are mentioned as future possibilities. This development highlights xAI's emphasis on iterative enhancements in generative AI tools. By extending duration while refining output fidelity, it reflects engineering priorities aimed at balancing computational efficiency with user needs. The focus on audio and visual stability suggests attention to common pitfalls in early text-to-video models, such as inconsistencies or artifacts. The sources do not specify the underlying model architecture changes or training data adjustments enabling this upgrade. Performance in real-world scenarios, like handling complex prompts or maintaining consistency across clips, remains unquantified in the announcements. Interpretations of broader implications for AI video generation would require additional evidence beyond what's provided.

Lacey

28,881 views • 7 months ago

A tricky LLM interview question: You're serving a reasoning model on vLLM, and it keeps running out of GPU memory on long traces. So you add KV cache compression and evict 90% of the cached tokens. VRAM usage stays as is and GPU still runs out of memory. Why? (answer below) Evicting 90% of the KV cache can free almost none of the memory it was using. This sounds counterintuitive, but it follows directly from how production servers store the cache today. The KV cache grows with every token a model generates. Each token appends its key and value vectors across every layer, and nothing is freed while generation continues. This is the dominant memory cost for reasoning models. If a 32K-token CoT caches ~32K tokens of KV vectors, a Qwen3-32B with 4-bit weights will run out-of-memory around 24K tokens on a 24GB GPU. One obvious solution is to keep the important tokens and drop the rest, since attention is sparse enough to allow it. But this does not solve the memory problem yet. The reason is paged attention, which is the memory manager behind vLLM and most production servers. Under the hood, it splits GPU memory into fixed physical blocks, each one holds the KV for about 16 tokens. This block returns to the allocator only when every slot inside it is empty. Since the eviction logic selects tokens by importance, and such tokens are scattered across blocks... ...so despite eviction, almost every block is left with at least some survivor tokens. For instance, if the logic evicts 14k of 16k tokens across 1,000 blocks, most likely every block will still have a token. This means the allocator frees almost nothing. Placing the new tokens into those freed slots is not ideal because it breaks the cache's layout. Say token 16,001 arrives, and it's placed in the slot the 40th token used to hold. The cache now reads position 38, then 16,001, then 41, so the cache is no longer in token order. Attention can still compute the right answer from that, but only if every slot now carries a separate note recording which position it actually holds. This introduces another bookkeeping cost that an in-order layout inherently avoids. So the cache is logically 90% smaller and still physically the same size. Many compression results miss this because they measure on pre-allocated contiguous tensors rather than a paged server. There's another problem. Eviction methods pick which tokens to keep by looking at the attention scores themselves (as expected). But fast attention kernels used in production, like FlashAttention, never save those scores. They compute attention in small pieces and throw the full score grid away as they go, which is also why they're fast. So the exact signal eviction methods need isn't available in memory. The workaround is to fall back to eager attention and build the full matrix, which gives up the speed FlashAttention was there to provide. NVIDIA published a method called TriAttention to solve both these problems. It never needs attention scores. Instead, it scores tokens from the geometry of the model's key and query vectors before RoPE is applied, where those vectors sit in stable clusters. For the memory problem, it runs a compaction pass every 128 decoded tokens. The surviving tokens slide forward to close the holes eviction creates, so whole blocks empty out and return to the allocator while the cache stays in token order. On long reasoning traces, the approach matches full-attention accuracy while decoding 2.5x faster and using 10.7x less KV memory. KV cache compression is a big infrastructure problem. The number that decides whether it works is the count of freed blocks, not the count of evicted tokens. You can find the NVIDIA write-up here: I wrote a first-principles breakdown of how the KV cache works. It walks through why the model stores keys and values at all, why the cache grows with every token, and a comparison of LLM generation speed with and without KV caching. Read it below.

A tricky LLM interview question: You're serving a reasoning model on vLLM, and it keeps running out of GPU memory on long traces. So you add KV cache compression and evict 90% of the cached tokens. VRAM usage stays as is and GPU still runs out of memory. Why? (answer below) Evicting 90% of the KV cache can free almost none of the memory it was using. This sounds counterintuitive, but it follows directly from how production servers store the cache today. The KV cache grows with every token a model generates. Each token appends its key and value vectors across every layer, and nothing is freed while generation continues. This is the dominant memory cost for reasoning models. If a 32K-token CoT caches ~32K tokens of KV vectors, a Qwen3-32B with 4-bit weights will run out-of-memory around 24K tokens on a 24GB GPU. One obvious solution is to keep the important tokens and drop the rest, since attention is sparse enough to allow it. But this does not solve the memory problem yet. The reason is paged attention, which is the memory manager behind vLLM and most production servers. Under the hood, it splits GPU memory into fixed physical blocks, each one holds the KV for about 16 tokens. This block returns to the allocator only when every slot inside it is empty. Since the eviction logic selects tokens by importance, and such tokens are scattered across blocks... ...so despite eviction, almost every block is left with at least some survivor tokens. For instance, if the logic evicts 14k of 16k tokens across 1,000 blocks, most likely every block will still have a token. This means the allocator frees almost nothing. Placing the new tokens into those freed slots is not ideal because it breaks the cache's layout. Say token 16,001 arrives, and it's placed in the slot the 40th token used to hold. The cache now reads position 38, then 16,001, then 41, so the cache is no longer in token order. Attention can still compute the right answer from that, but only if every slot now carries a separate note recording which position it actually holds. This introduces another bookkeeping cost that an in-order layout inherently avoids. So the cache is logically 90% smaller and still physically the same size. Many compression results miss this because they measure on pre-allocated contiguous tensors rather than a paged server. There's another problem. Eviction methods pick which tokens to keep by looking at the attention scores themselves (as expected). But fast attention kernels used in production, like FlashAttention, never save those scores. They compute attention in small pieces and throw the full score grid away as they go, which is also why they're fast. So the exact signal eviction methods need isn't available in memory. The workaround is to fall back to eager attention and build the full matrix, which gives up the speed FlashAttention was there to provide. NVIDIA published a method called TriAttention to solve both these problems. It never needs attention scores. Instead, it scores tokens from the geometry of the model's key and query vectors before RoPE is applied, where those vectors sit in stable clusters. For the memory problem, it runs a compaction pass every 128 decoded tokens. The surviving tokens slide forward to close the holes eviction creates, so whole blocks empty out and return to the allocator while the cache stays in token order. On long reasoning traces, the approach matches full-attention accuracy while decoding 2.5x faster and using 10.7x less KV memory. KV cache compression is a big infrastructure problem. The number that decides whether it works is the count of freed blocks, not the count of evicted tokens. You can find the NVIDIA write-up here: I wrote a first-principles breakdown of how the KV cache works. It walks through why the model stores keys and values at all, why the cache grows with every token, and a comparison of LLM generation speed with and without KV caching. Read it below.

Avi Chawla

271,839 views • 1 month ago

🚨 Paper Alert 🚨 ➡️Paper Title: Articulate3D: Zero-Shot Text-Driven 3D Object Posing 🌟Few pointers from the paper 🎯Authors of this paper proposed a training-free method, “Articulate3D”, to pose a 3D asset through language control. 🎯Despite advances in vision and language models, this task remains surprisingly challenging. 🎯To achieve this goal, they decomposed the problem into two steps. 🎯They modified a powerful image-generator to create target images conditioned on the input image and a text instruction. 🎯They then align the mesh to the target images through a multi-view pose optimisation step. 🎯 In detail, they introduced a self-attention rewiring mechanism (RSActrl) that decouples the source structure from pose within an image generative model, allowing it to maintain a consistent structure across varying poses. 🎯They observed that differentiable rendering is an unreliable signal for articulation optimisation; instead, they used keypoints to establish correspondences between input and target images. 🎯The effectiveness of Articulate3D is demonstrated across a diverse range of 3D objects and free-form text prompts, successfully manipulating poses while maintaining the original identity of the mesh. 🎯Quantitative evaluations and a comparative user study, in which their method was preferred over 85% of the time, confirm its superiority over existing approaches. 🏢Organization: University of Oxford , Google DeepMind 🧙Paper Authors: Oishi Deb, Anjun Hu, Ashkan Khakzar, Philip Torr, Christian Rupprecht 📝 Read the Full Paper here: 🗂️ Project Page: 🎥 Be sure to watch the attached Demo Video - Sound on 🔊🔊 Find this Valuable 💎 ? ♻️QT and teach your network something new Follow me 👣, naveen manwani , for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements.

🚨 Paper Alert 🚨 ➡️Paper Title: Articulate3D: Zero-Shot Text-Driven 3D Object Posing 🌟Few pointers from the paper 🎯Authors of this paper proposed a training-free method, “Articulate3D”, to pose a 3D asset through language control. 🎯Despite advances in vision and language models, this task remains surprisingly challenging. 🎯To achieve this goal, they decomposed the problem into two steps. 🎯They modified a powerful image-generator to create target images conditioned on the input image and a text instruction. 🎯They then align the mesh to the target images through a multi-view pose optimisation step. 🎯 In detail, they introduced a self-attention rewiring mechanism (RSActrl) that decouples the source structure from pose within an image generative model, allowing it to maintain a consistent structure across varying poses. 🎯They observed that differentiable rendering is an unreliable signal for articulation optimisation; instead, they used keypoints to establish correspondences between input and target images. 🎯The effectiveness of Articulate3D is demonstrated across a diverse range of 3D objects and free-form text prompts, successfully manipulating poses while maintaining the original identity of the mesh. 🎯Quantitative evaluations and a comparative user study, in which their method was preferred over 85% of the time, confirm its superiority over existing approaches. 🏢Organization: University of Oxford , Google DeepMind 🧙Paper Authors: Oishi Deb, Anjun Hu, Ashkan Khakzar, Philip Torr, Christian Rupprecht 📝 Read the Full Paper here: 🗂️ Project Page: 🎥 Be sure to watch the attached Demo Video - Sound on 🔊🔊 Find this Valuable 💎 ? ♻️QT and teach your network something new Follow me 👣, naveen manwani , for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements.

naveen manwani

14,334 views • 1 year ago

first impressions of qwen 3.5 27B dense on a single RTX 3090. 35 tok/s. from 4K all the way to 300K+ context. no speed drop. hermes 4.3 started at 35 and degraded to 15 as context filled. qwen dense holds. MoE held 112 flat. 3x faster but only 3B of 35B active per token. architecture tradeoff. Q4_K_M on 16.7GB. native context 262K. pushed past training limit to 376K before VRAM ceiling on 24GB. tried q8 KV cache at 262K, speed collapsed to 11 tok/s. q4_0 KV is the sweet spot. flash attention mandatory. built in reasoning mode. the model thinks step by step before it answers. full chain of thought surviving Q4 quant. 1,799+ token thinking chains with self correction loops. on a single consumer GPU. gave it one prompt: "build a realtime particle galaxy simulation in one HTML file." 3,340 tokens. 95 seconds. one shot. ran on first load. full reasoning and coding in the video below. optimal config if you want to skip the hours of testing: llama-server -ngl 99 -c 262144 -fa on --cache-type-k q4_0 --cache-type-v q4_0 this is just the warmup. octopus invaders is next: 10 files, 3,400+ lines, zero steering. the prompt hermes quit at 22%. already more impressed than expected. full results coming soon.

first impressions of qwen 3.5 27B dense on a single RTX 3090. 35 tok/s. from 4K all the way to 300K+ context. no speed drop. hermes 4.3 started at 35 and degraded to 15 as context filled. qwen dense holds. MoE held 112 flat. 3x faster but only 3B of 35B active per token. architecture tradeoff. Q4_K_M on 16.7GB. native context 262K. pushed past training limit to 376K before VRAM ceiling on 24GB. tried q8 KV cache at 262K, speed collapsed to 11 tok/s. q4_0 KV is the sweet spot. flash attention mandatory. built in reasoning mode. the model thinks step by step before it answers. full chain of thought surviving Q4 quant. 1,799+ token thinking chains with self correction loops. on a single consumer GPU. gave it one prompt: "build a realtime particle galaxy simulation in one HTML file." 3,340 tokens. 95 seconds. one shot. ran on first load. full reasoning and coding in the video below. optimal config if you want to skip the hours of testing: llama-server -ngl 99 -c 262144 -fa on --cache-type-k q4_0 --cache-type-v q4_0 this is just the warmup. octopus invaders is next: 10 files, 3,400+ lines, zero steering. the prompt hermes quit at 22%. already more impressed than expected. full results coming soon.

Sudo su

122,051 views • 5 months ago

I’ve tested dozens of AI video tools for ecommerce ads. Most still look obviously fake. They either distort the product, break character consistency, or completely miss the pacing that makes viral content actually work. Wizstar was the first one that genuinely surprised me. I tested Wizstar’s Wizstar_official Video Reference workflow using a viral-style ad, and honestly… I wasn’t ready for how accurate it would be. It didn’t just “generate a similar video.” It recreated the entire feel of a high-performing ad — the pacing, the transitions, the camera movements, even that subtle “social-first” storytelling rhythm that usually takes real creative teams to get right. And the wild part is… it still kept the product completely stable across brand-new scenes. No distortions. No weird AI shifts. Just clean, consistent visuals from start to finish. What really impressed me is that Video Reference isn’t just copying visuals at all. It actually analyzes the structure behind viral ecommerce content — things like: - how the hook is built, - how attention is retained across cuts, - and how emotion is paced through the video. Then it rebuilds that logic into a completely new ad, optimized for product testing and fast campaign iteration. And it feels like it understands performance content. The workflow also handled synchronized audiovisual timing surprisingly well — cuts land where they should, motion matches the sound, and the storytelling flow feels intentionally designed, not randomly generated. Combined with stable product consistency and influencer-style framing, the final output honestly felt closer to a real paid social campaign than anything I’d expect from an AI tool. What surprised me is that Wizstar isn’t powered by just one model—it orchestrates multiple top-tier AI models, including Seedance 2.0, and even supports face input out of the box. If you’re curious to try it yourself, you can test it here: New users get free credits, and the first membership is only $19 — plus a complimentary 30-second E-commerce Agent experience included. #Wizstar

Doreen

153,944 views • 2 months ago

You have to really give it to OpenAI because Sora 2 is very impressive on a lot of fronts: - high quality video model with great physics - high quality audio in each video - high character consistency - multiple characters in one scene - accurate characters voice - social platform attached to it Before today the best AI video models were dominated by Chinese companies like ByteDance and Kuaishou and Google with Veo3. ByteDance makes TikTok, Kuaishou makes Kwai (similar app) and Google has YouTube to train on But none of these models had great character consistency, if it was a feature at all, let alone multiple characters in one scene. Generally you'd make a video and the face would slowly change into someone else, just not good On top of that Google was struggling with allowing people to upload characters scared it'd get abused for deep fakes, and just generally nerfing their model so you can't really use it for anything OpenAI solved that by re-thinking ownership over your characters smartly with Cameo, which is essentially "train yourself as a AI model" which we've all been doing in our apps for years, but in a more smart way, where you can control if only you make content with your appearance, or others too They've also added voice training to it immediately, which people would have to do separate on for ex ElevenLabs before On top of that the social platform aspect: Google's Veo 3 didn't have ANY community at all, while the Chinese video models did, but it was all more like weekly themed contests to win free credits, they never really managed to make it more than that, and it kinda stayed in this nerdy AI hacking vibe This vibe fits how hard it was/is to simply make a video featuring you or your friends with proper voice and audio and everything that Sora 2 does for you. You'd have to go to ElevenLabs to train your voices, then go to for ex Photo AI to train yourself as a person, then make videos, then add audio and voices, then edit them together, a lot of work! We don't know if Sora 2's social platform features will actually be used or take off, but it's a real cool experiment in trying to find a way to build a community around AI in a more Instagram-like way Being able to tag your friends and then add them as multiple characters is innovative in both the social and technical aspect So TL;DR OpenAI essentially took a lot of stuff that was already technically possible, then added new things that weren't possible yet, and then put it all together in a very friendly interface that even my mom can use, with generation times of just a few minutes which is extremely fast if you think of the pipeline behind it (multiple video generation + voice + audio etc.) And also importantly, it doesn't look like they nerfed it much for safety which is also very cool considering the legal risks So yes very very very impressive

You have to really give it to OpenAI because Sora 2 is very impressive on a lot of fronts: - high quality video model with great physics - high quality audio in each video - high character consistency - multiple characters in one scene - accurate characters voice - social platform attached to it Before today the best AI video models were dominated by Chinese companies like ByteDance and Kuaishou and Google with Veo3. ByteDance makes TikTok, Kuaishou makes Kwai (similar app) and Google has YouTube to train on But none of these models had great character consistency, if it was a feature at all, let alone multiple characters in one scene. Generally you'd make a video and the face would slowly change into someone else, just not good On top of that Google was struggling with allowing people to upload characters scared it'd get abused for deep fakes, and just generally nerfing their model so you can't really use it for anything OpenAI solved that by re-thinking ownership over your characters smartly with Cameo, which is essentially "train yourself as a AI model" which we've all been doing in our apps for years, but in a more smart way, where you can control if only you make content with your appearance, or others too They've also added voice training to it immediately, which people would have to do separate on for ex ElevenLabs before On top of that the social platform aspect: Google's Veo 3 didn't have ANY community at all, while the Chinese video models did, but it was all more like weekly themed contests to win free credits, they never really managed to make it more than that, and it kinda stayed in this nerdy AI hacking vibe This vibe fits how hard it was/is to simply make a video featuring you or your friends with proper voice and audio and everything that Sora 2 does for you. You'd have to go to ElevenLabs to train your voices, then go to for ex Photo AI to train yourself as a person, then make videos, then add audio and voices, then edit them together, a lot of work! We don't know if Sora 2's social platform features will actually be used or take off, but it's a real cool experiment in trying to find a way to build a community around AI in a more Instagram-like way Being able to tag your friends and then add them as multiple characters is innovative in both the social and technical aspect So TL;DR OpenAI essentially took a lot of stuff that was already technically possible, then added new things that weren't possible yet, and then put it all together in a very friendly interface that even my mom can use, with generation times of just a few minutes which is extremely fast if you think of the pipeline behind it (multiple video generation + voice + audio etc.) And also importantly, it doesn't look like they nerfed it much for safety which is also very cool considering the legal risks So yes very very very impressive

@levelsio

178,100 views • 10 months ago

** MEGA Parodius Scaling Effects Part 2 ** Well - this is the BIG one - literally !! Huge thanks to my team Pyron & Vector Orbitex for their efforts. Pyron has provided all the source frames for Puyon and his spikes / explosion and a lot of analysis video on how the spikes behave / move which was really helpful. Pyron also used his CRT setup for this video as we felt an emulator video would not do it justice - running on 100% real MD hardware FTW. Vector has provided the catchy boss music and its sounding great - as always ! The coding on this has been a bit insane - things done since part 1 post previous: Implemented dual buffering - Last video was single buffered - so VRAM is very tight now , we only have about 40/2048 tiles free. For the longest time I didn't think it would fit - I found a vram jigsaw puzzle that made it work in the end. Double buffering has cleaned up the stability of the animation and matches the arcade scaling effect now albiet costing 2x more VRAM . Vertical Scaling Implemented - the vertical scaler was taken from Lufthoheit ( my other shooter ) and its heavily optimised to reduce cpu usage. During the scaling the vertical scaler partitions the 68k processor registers into 2 sets, 4 registers are allocated to feeding the fast horizontal interrupt (h-int) that drives the vertical scaling , remaining 12 registers are for the horizontal scaler running in the background. This setup is much faster than normal backup / restore register methods, as we do not need to backup / restore registers in the h-int which would double CPU costs. The catch is the momment any background routine tries to use the H-ints registers it would break things so it has to be carefully timed. Added the spike projectiles - this was very tricky to get close to the arcade, they are semi heat seeking missiles basically and hence needed code that worked out angle differences to player at speed theres no time for arc-tan or similar so it uses faster lookup tables to work out the angles . They speed up over time and get larger and whats more we can't keep all the scales in VRAM - we have room for 2 spike buffers only. Also what was a real pain was working out scaled coordinates for the circular launch of the spikes . Added Fish Damage - Shock frame and Explosion frames from Arcade . Very proud of the fact we have the full arcade quality explosion is which is fully scaled also. Added Temporal Masking - which is fancy wording for don't draw nothing to buffer if nothing is there already there for the scaling . So empty Corners and edges can be optimised out to lower cpu costs and rom costs. I had to make some scripting for this and work out what areas did not need drawing at all in the frame, which should be force cleared by cpu and which areas should just be copied from rom. This reduced rom size by 30 kb and with a bit more work we could extend that to 60 kb & get a bit more speedup even doing so. Added a frame limiter . In the last video update we let the 68k burn hot and just pump out frames as fast as it could - here we match the arcade animation rate which does leave the cpu idling at times , particularly in the smaller frames - even at large though we could be running the animation 25 % faster , issue is though that would speed up the game logic and make it less arcade accurate. We had some real bullet / Spike hell simulations going without the limiter but yeah we had to tone it down a bit. Maybe in a hardcore mode we could let it run wild though ! Code is 95% 68k assembly with about 5% C code (v-int as its cold path ) driving things . This sort of thing needs all the speed it can get !! Well now after all that I can return to finish off Level 1 haha - just a wee sidetrack there . No doubt we will polish stage 8 boss some more in time too !! #SegaMegadrive #SegaGenesis #Parodius #SGDK

MEGA Parodius Scaling Effects Part 2 Well - this is the BIG one - literally !! Huge thanks to my team Pyron & Vector Orbitex for their efforts. Pyron has provided all the source frames for Puyon and his spikes / explosion and a lot of analysis video on how the spikes behave / move which was really helpful. Pyron also used his CRT setup for this video as we felt an emulator video would not do it justice - running on 100% real MD hardware FTW. Vector has provided the catchy boss music and its sounding great - as always ! The coding on this has been a bit insane - things done since part 1 post previous: Implemented dual buffering - Last video was single buffered - so VRAM is very tight now , we only have about 40/2048 tiles free. For the longest time I didn't think it would fit - I found a vram jigsaw puzzle that made it work in the end. Double buffering has cleaned up the stability of the animation and matches the arcade scaling effect now albiet costing 2x more VRAM . Vertical Scaling Implemented - the vertical scaler was taken from Lufthoheit ( my other shooter ) and its heavily optimised to reduce cpu usage. During the scaling the vertical scaler partitions the 68k processor registers into 2 sets, 4 registers are allocated to feeding the fast horizontal interrupt (h-int) that drives the vertical scaling , remaining 12 registers are for the horizontal scaler running in the background. This setup is much faster than normal backup / restore register methods, as we do not need to backup / restore registers in the h-int which would double CPU costs. The catch is the momment any background routine tries to use the H-ints registers it would break things so it has to be carefully timed. Added the spike projectiles - this was very tricky to get close to the arcade, they are semi heat seeking missiles basically and hence needed code that worked out angle differences to player at speed theres no time for arc-tan or similar so it uses faster lookup tables to work out the angles . They speed up over time and get larger and whats more we can't keep all the scales in VRAM - we have room for 2 spike buffers only. Also what was a real pain was working out scaled coordinates for the circular launch of the spikes . Added Fish Damage - Shock frame and Explosion frames from Arcade . Very proud of the fact we have the full arcade quality explosion is which is fully scaled also. Added Temporal Masking - which is fancy wording for don't draw nothing to buffer if nothing is there already there for the scaling . So empty Corners and edges can be optimised out to lower cpu costs and rom costs. I had to make some scripting for this and work out what areas did not need drawing at all in the frame, which should be force cleared by cpu and which areas should just be copied from rom. This reduced rom size by 30 kb and with a bit more work we could extend that to 60 kb & get a bit more speedup even doing so. Added a frame limiter . In the last video update we let the 68k burn hot and just pump out frames as fast as it could - here we match the arcade animation rate which does leave the cpu idling at times , particularly in the smaller frames - even at large though we could be running the animation 25 % faster , issue is though that would speed up the game logic and make it less arcade accurate. We had some real bullet / Spike hell simulations going without the limiter but yeah we had to tone it down a bit. Maybe in a hardcore mode we could let it run wild though ! Code is 95% 68k assembly with about 5% C code (v-int as its cold path ) driving things . This sort of thing needs all the speed it can get !! Well now after all that I can return to finish off Level 1 haha - just a wee sidetrack there . No doubt we will polish stage 8 boss some more in time too !! #SegaMegadrive #SegaGenesis #Parodius #SGDK

Shannon Birt

26,877 views • 6 months ago