正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

🤯 𝐒𝐥𝐢𝐜𝐞𝐝𝐢𝐭 : A revolutionary approach to zero-shot video editing✏️🎥 using text-to-image diffusion models 🎞️Slicedit achieves great temporal consistency by leveraging spatio-temporal "slices"🔀

Gradio

56,929 subscribers

19,586 次观看 • 2 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation paper page: Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.

Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation paper page: Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.

AK

375,160 次观看 • 3 年前

Zero-shot video reasoning (chain-of-frames) isn’t just for Veo3 — open-source models can understand and edit too! 🕹️ ChronoEdit brings temporal reasoning to image editing. 🔗

Zero-shot video reasoning (chain-of-frames) isn’t just for Veo3 — open-source models can understand and edit too! 🕹️ ChronoEdit brings temporal reasoning to image editing. 🔗

Xuanchi Ren

22,661 次观看 • 10 个月前

Switti -- a new scale-wise transformer for text-to-image generation 🦾 🔥 Improved generation of fine-grained details. Outperforms existing T2I AR models and competes with state-of-the-art T2I diffusion models while being up to 7x faster.

Switti -- a new scale-wise transformer for text-to-image generation 🦾 🔥 Improved generation of fine-grained details. Outperforms existing T2I AR models and competes with state-of-the-art T2I diffusion models while being up to 7x faster.

Gradio

29,314 次观看 • 1 年前

NVIDIA just released a very impressive text-to-video paper. Video Latent Diffusion Models (Video LDMs) use a diffusion model in a compressed latent space to generate high-resolution videos. Here's a brief overview of how it works: 1. Pre-train image LDM on a dataset of images. 2. Turn the image LDM into a Video LDM by adding temporal layers to model video frames. 3. Fine-tune the Video LDM on encoded video sequences to create a video generator. 4. Temporally align diffusion model upsamplers to generate high-resolution videos. 5. Validate Video LDM on real driving videos of 512x1024 resolution, achieving state-of-the-art performance. 6. Apply the approach in creative content creation with text-to-video modeling. Paper: Project:

NVIDIA just released a very impressive text-to-video paper. Video Latent Diffusion Models (Video LDMs) use a diffusion model in a compressed latent space to generate high-resolution videos. Here's a brief overview of how it works: 1. Pre-train image LDM on a dataset of images. 2. Turn the image LDM into a Video LDM by adding temporal layers to model video frames. 3. Fine-tune the Video LDM on encoded video sequences to create a video generator. 4. Temporally align diffusion model upsamplers to generate high-resolution videos. 5. Validate Video LDM on real driving videos of 512x1024 resolution, achieving state-of-the-art performance. 6. Apply the approach in creative content creation with text-to-video modeling. Paper: Project:

Lior Alexander

158,565 次观看 • 3 年前

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.

MrNeRF

17,062 次观看 • 1 年前

🤔Applying Depth Estimation models directly to videos can result in inconsistency between frames. 💪Well, not anymore. 🔥ChronoDepth is a new approach to video depth estimation that focuses on achieving both accuracy within each frame and consistency across frames. 📜More👇

🤔Applying Depth Estimation models directly to videos can result in inconsistency between frames. 💪Well, not anymore. 🔥ChronoDepth is a new approach to video depth estimation that focuses on achieving both accuracy within each frame and consistency across frames. 📜More👇

Gradio

19,992 次观看 • 2 年前

1/ Happy to share UniDisc - Unified Multimodal Discrete Diffusion – We train a 1.5 billion parameter transformer model from scratch on 250 million image/caption pairs using a **discrete diffusion objective**. Our model has all the benefits of diffusion models but now in multimodal space! - flexible compute-quality tradeoff, zero-shot inpainting and editing, better control via classifier-free guidance and lower latency! We open source everything - our code, weights and the training dataset.

1/ Happy to share UniDisc - Unified Multimodal Discrete Diffusion – We train a 1.5 billion parameter transformer model from scratch on 250 million image/caption pairs using a discrete diffusion objective. Our model has all the benefits of diffusion models but now in multimodal space! - flexible compute-quality tradeoff, zero-shot inpainting and editing, better control via classifier-free guidance and lower latency! We open source everything - our code, weights and the training dataset.

Mihir Prabhudesai

104,934 次观看 • 1 年前

Hi3DGen 🔥 High-fidelity 3D geometry generation from a single image by leveraging normal maps as an intermediate representation

Hi3DGen 🔥 High-fidelity 3D geometry generation from a single image by leveraging normal maps as an intermediate representation

Gradio

50,996 次观看 • 1 年前

MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers paper page: Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets for training, and therefore challenging the deployment in practical applications. This study addresses this challenge by breaking down the text-based video editing process into two separate stages. In the first stage, we leverage an existing text-to-image diffusion model to simultaneously edit a few keyframes without additional fine-tuning. In the second stage, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the keyframes, benefiting from structural guidance provided by intermediate frames. Our comprehensive set of experiments illustrates the efficacy and efficiency of MaskINT when compared to other diffusion-based methodologies. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.

MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers paper page: Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets for training, and therefore challenging the deployment in practical applications. This study addresses this challenge by breaking down the text-based video editing process into two separate stages. In the first stage, we leverage an existing text-to-image diffusion model to simultaneously edit a few keyframes without additional fine-tuning. In the second stage, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the keyframes, benefiting from structural guidance provided by intermediate frames. Our comprehensive set of experiments illustrates the efficacy and efficiency of MaskINT when compared to other diffusion-based methodologies. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.

AK

25,449 次观看 • 2 年前

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

MrNeRF

52,849 次观看 • 1 年前

🆕 Image super-resolution model just dropped! Superior results even with a single sampling step. 🔥InvSR: Arbitrary-steps Image Super-resolution via Diffusion Inversion.

🆕 Image super-resolution model just dropped! Superior results even with a single sampling step. 🔥InvSR: Arbitrary-steps Image Super-resolution via Diffusion Inversion.

Gradio

53,331 次观看 • 1 年前

🏆 We're thrilled to announce that Meta FAIR’s Brain & AI team won 1st place at the prestigious Algonauts 2025 brain modeling competition. Their 1B parameter model, TRIBE (Trimodal Brain Encoder), is the first deep neural network trained to predict brain responses to stimuli across multiple modalities, cortical areas, and individuals. The approach combines pretrained representations of several foundational models from Meta – text (Llama 3.2), audio (Wav2Vec2-BERT from Seamless) and video (V-JEPA 2) – to predict a very large amount (80 hours per subject) of spatio-temporal fMRI brain responses to movies acquired by the Courtois NeuroMod project Download the code: Read the paper: Learn about the challenge: Download the data:

🏆 We're thrilled to announce that Meta FAIR’s Brain & AI team won 1st place at the prestigious Algonauts 2025 brain modeling competition. Their 1B parameter model, TRIBE (Trimodal Brain Encoder), is the first deep neural network trained to predict brain responses to stimuli across multiple modalities, cortical areas, and individuals. The approach combines pretrained representations of several foundational models from Meta – text (Llama 3.2), audio (Wav2Vec2-BERT from Seamless) and video (V-JEPA 2) – to predict a very large amount (80 hours per subject) of spatio-temporal fMRI brain responses to movies acquired by the Courtois NeuroMod project Download the code: Read the paper: Learn about the challenge: Download the data:

AI at Meta

1,093,641 次观看 • 1 年前

🔬 Built with an entirely new model architecture, our diffusion-based approach uses 6B+ parameters and leverages the latest NVIDIA hardware. This is the most dynamic and wide-ranging video enhancing method we’ve ever created, setting a new standard for AI video restoration. Videos degrade due to compression artifacts, blurring, aliasing, noise, atmospheric distortion, missing pixels, etc. Each frame suffers from unique types of corruption, making AI video restoration a highly challenging task. Our technology solves this complexity by analyzing hundreds of frames to accurately restore details, delivering unmatched detail recovery combined with unparalleled temporal consistency.

🔬 Built with an entirely new model architecture, our diffusion-based approach uses 6B+ parameters and leverages the latest NVIDIA hardware. This is the most dynamic and wide-ranging video enhancing method we’ve ever created, setting a new standard for AI video restoration. Videos degrade due to compression artifacts, blurring, aliasing, noise, atmospheric distortion, missing pixels, etc. Each frame suffers from unique types of corruption, making AI video restoration a highly challenging task. Our technology solves this complexity by analyzing hundreds of frames to accurately restore details, delivering unmatched detail recovery combined with unparalleled temporal consistency.

Topaz Labs

23,199 次观看 • 1 年前

Dreamina Seedance 2.0 is Officially here! Dreamina Seedance 2.0, ByteDance’s AI-powered creative platform, lets creators easily transform ideas into high-quality videos. You can now edit videos like images, using up to 4 reference modalities (video, image, audio, and text) with precise control over visual effects, camera movements, and more. Why is this revolutionary? - 🔥 One-Prompt Video Editing: Edit videos seamlessly as easily as editing images. - 🎬 Multimodal Creativity: Combine images, video, audio, and text—up to 12 files at once. - 🎥 Remake Viral Content: Create high quality, professional-level videos with intelligent AI. - 🔧 Full Creative Control: Keep consistency in shots, typography, camera flow, and more. This isn’t just another video editing tool, it’s a one-stop AI workspace for all your creative needs. Ready to level up your content? Explore Dreamina Seedance 2.0 and start creating today. 🔗 #dreamina #seedance2 #seedream5 #dreaminatutorial #ai #aitools #aidesign #ecommercedesign #digitalmarketing #startupbusiness

Dreamina Seedance 2.0 is Officially here! Dreamina Seedance 2.0, ByteDance’s AI-powered creative platform, lets creators easily transform ideas into high-quality videos. You can now edit videos like images, using up to 4 reference modalities (video, image, audio, and text) with precise control over visual effects, camera movements, and more. Why is this revolutionary? - 🔥 One-Prompt Video Editing: Edit videos seamlessly as easily as editing images. - 🎬 Multimodal Creativity: Combine images, video, audio, and text—up to 12 files at once. - 🎥 Remake Viral Content: Create high quality, professional-level videos with intelligent AI. - 🔧 Full Creative Control: Keep consistency in shots, typography, camera flow, and more. This isn’t just another video editing tool, it’s a one-stop AI workspace for all your creative needs. Ready to level up your content? Explore Dreamina Seedance 2.0 and start creating today. 🔗 #dreamina #seedance2 #seedream5 #dreaminatutorial #ai #aitools #aidesign #ecommercedesign #digitalmarketing #startupbusiness

GitHub Projects Community

18,440 次观看 • 4 个月前

Character Consistency with Google Veo 3 now in Gemini API! 🤯 Use Images as starting frame to keep character consistency! Here is an python script on how to make consistent viral videos, like you see on TikTok or Youtube shorts: 1. Based on an idea, it generates a series of scene prompts using Gemini 2.5. 2. Generates a Image based on the first scene using Imagen 3 3. For each scene prompt Veo 3 (fast) generates a video clip. 4. Uses Gemini 2.0 image editing to make sure the starting images fits the scenes 5. Combine the individual video clips into a single final video using MoviePy Veo 3 starts at $0.75 / second and Veo 3 at $0.40 / second with audio. ! 📹 🔉 Prompt: “A realistic energy drink commercial for athletes.”

Character Consistency with Google Veo 3 now in Gemini API! 🤯 Use Images as starting frame to keep character consistency! Here is an python script on how to make consistent viral videos, like you see on TikTok or Youtube shorts: 1. Based on an idea, it generates a series of scene prompts using Gemini 2.5. 2. Generates a Image based on the first scene using Imagen 3 3. For each scene prompt Veo 3 (fast) generates a video clip. 4. Uses Gemini 2.0 image editing to make sure the starting images fits the scenes 5. Combine the individual video clips into a single final video using MoviePy Veo 3 starts at $0.75 / second and Veo 3 at $0.40 / second with audio. ! 📹 🔉 Prompt: “A realistic energy drink commercial for athletes.”

Philipp Schmid

15,863 次观看 • 1 年前