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Coin3D Controllable and Interactive 3D Assets Generation with Proxy-Guided Conditioning As humans, we aspire to create media content that is both freely willed and readily controlled. Thanks to the prominent development of generative techniques, we now can easily utilize

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23,055 просмотров • 2 лет назад •via X (Twitter)

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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.
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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

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Portmore becoming a parish is a significant step for both the residents and Jamaica in general. As we grow, our society must be reflective of that growth as well as of the future we want to create. Parish status for Portmore is a positive development that we should embrace as a mode for bringing greater organization and progress.
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Portmore becoming a parish is a significant step for both the residents and Jamaica in general. As we grow, our society must be reflective of that growth as well as of the future we want to create. Parish status for Portmore is a positive development that we should embrace as a mode for bringing greater organization and progress.

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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.
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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.

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Now I understand the mistake of Prometheus: instead of humans becoming what they truly are and part of God, the Promethean and Luciferian mistake is that humans want to create outside of God or mimic God. With technology we mimic nature, and everything technology mimics is an inversion and a lesser copy. But this is a mistake we need to make in order to grow in the future. We are creating the artificial spirit from the ancient Christian texts, now referred to as Gnostic texts.
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Now I understand the mistake of Prometheus: instead of humans becoming what they truly are and part of God, the Promethean and Luciferian mistake is that humans want to create outside of God or mimic God. With technology we mimic nature, and everything technology mimics is an inversion and a lesser copy. But this is a mistake we need to make in order to grow in the future. We are creating the artificial spirit from the ancient Christian texts, now referred to as Gnostic texts.

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now supports OBJ generation with optimized, game-ready meshes. Quads or Tris. FBX support coming. Best platform to access all the latest generative 3D models and make game-ready assets at scale.
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now supports OBJ generation with optimized, game-ready meshes. Quads or Tris. FBX support coming. Best platform to access all the latest generative 3D models and make game-ready assets at scale.

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We are entering a new era of digital content for the sports industry. This 3D animation was generated from 2D videos with Move AI. Now you can easily capture athlete movements to create engaging digital experiences for fans.
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As we move more and more into 3D, texturing 3D assets will be essential ☝️ Repainting 3D Assets is a new AI method that can take any 3D asset and paint it with a given text prompt. The results, while low-resolution, are pretty impressive.

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Soon, thanks to AI, we’ll be able to turn any 2D character we create in Midjourney into fully customizable 3D assets!
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Soon, thanks to AI, we’ll be able to turn any 2D character we create in Midjourney into fully customizable 3D assets!

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. Runway just added a new feature that you may not have noticed and we wanted to compare. You can now upscale your generation to 4K! Our favorite has typically been Topaz Labs, so we wanted to compare the out of both. Here is the original video from Runway
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. Runway just added a new feature that you may not have noticed and we wanted to compare. You can now upscale your generation to 4K! Our favorite has typically been Topaz Labs, so we wanted to compare the out of both. Here is the original video from Runway

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Blades of the Guardians episode two is crazy. There are many battles, but I would like to draw attention to this one. We also have hand to hand combat, and it's just as good. Strong choreography with great use of the 3D camera, and the variety of techniques is impressive.

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What do we say to gas? Not today. 👀 You can now freely swap and interact with Monad in MetaMask. The gas is on us!
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What do we say to gas? Not today. 👀 You can now freely swap and interact with Monad in MetaMask. The gas is on us!

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Ambient has launched its next-generation Multiplayer Game Engine. Game developers can now create games in an open-source style, and easily add tools, assets, and mods to each other's games, unlocking limitless creative potential: #WebGPU #WebAssembly
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Ambient has launched its next-generation Multiplayer Game Engine. Game developers can now create games in an open-source style, and easily add tools, assets, and mods to each other's games, unlocking limitless creative potential: #WebGPU #WebAssembly

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this is.. huge revolution AI now can create 3D interactive website and mobile apps that blow everyone's mind, no code needed, just talk with Emergent AI agents 10 examples with test link:
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this is.. huge revolution AI now can create 3D interactive website and mobile apps that blow everyone's mind, no code needed, just talk with Emergent AI agents 10 examples with test link:

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Human Hair Reconstruction with Strand-Aligned 3D Gaussians Contributions (cited): – We propose a new 3D line lifting scheme that uses a modified 3DGS reconstruction technique to lift 2D orientation maps into a 3D field while also providing refinement of the camera parameters; – We introduce a dual representation of hair strand polylines and 3D Gaussians to achieve differentiable rasterization of hair strands and leverage photometric constraints for strand-based hair reconstruction; – Based on these components, we propose a coarse-to-fine optimization method for prior-guided hair reconstruction that leverages both latent and explicit representations of the hairstyle.
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Human Hair Reconstruction with Strand-Aligned 3D Gaussians Contributions (cited): – We propose a new 3D line lifting scheme that uses a modified 3DGS reconstruction technique to lift 2D orientation maps into a 3D field while also providing refinement of the camera parameters; – We introduce a dual representation of hair strand polylines and 3D Gaussians to achieve differentiable rasterization of hair strands and leverage photometric constraints for strand-based hair reconstruction; – Based on these components, we propose a coarse-to-fine optimization method for prior-guided hair reconstruction that leverages both latent and explicit representations of the hairstyle.

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