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3D Gaussian Splats are cool, but they're static (Part 25). DG-Mesh can reconstruct high-quality and time-consistent 3D meshes from a single video and track mesh vertices over time, which enables texture editing on dynamic objects.

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Dreaming Tulpa 🥓👑

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79,033 görüntüleme • 2 yıl önce •via X (Twitter)

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3D Gaussian Splats are cool, but they're static (Part 33). GGHead can generate and render 3D heads at 1K resolution in real-time.
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3D Gaussian Splats are cool, but they're static (Part 33). GGHead can generate and render 3D heads at 1K resolution in real-time.

Dreaming Tulpa 🥓👑

104,552 görüntüleme • 2 yıl önce

3D Gaussian Splats are cool, but they're static (Part 13). VR-GS makes it possible for users to interact with 3D Gaussian kernels in VR and can generate realistic dynamic responses and illumination in real-time.
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3D Gaussian Splats are cool, but they're static (Part 13). VR-GS makes it possible for users to interact with 3D Gaussian kernels in VR and can generate realistic dynamic responses and illumination in real-time.

Dreaming Tulpa 🥓👑

75,185 görüntüleme • 2 yıl önce

Instant Skinned Gaussian Avatars for Web, Mobile and VR Applications Short summary: In our system, we animate a background 3D mesh and have the Gaussian splats follow the mesh’s vertices. During preprocessing, splats are assigned to mesh vertices, and their relative transformations are stored. Once this data is saved, you can instantly use it in your applications without further preprocessing. At runtime, we animate the background 3D mesh, update the Gaussian splats in parallel, and resort all Gaussian splats every frame based on the viewer’s perspective.
1:10

Instant Skinned Gaussian Avatars for Web, Mobile and VR Applications Short summary: In our system, we animate a background 3D mesh and have the Gaussian splats follow the mesh’s vertices. During preprocessing, splats are assigned to mesh vertices, and their relative transformations are stored. Once this data is saved, you can instantly use it in your applications without further preprocessing. At runtime, we animate the background 3D mesh, update the Gaussian splats in parallel, and resort all Gaussian splats every frame based on the viewer’s perspective.

MrNeRF

66,016 görüntüleme • 11 ay önce

3D Gaussian Splats are cool, but they're static (Part 12). Spacetime Gaussian Feature Splatting is able to capture dynamic, and transient content within a scene and render them at 8K resolution and 60 FPS on an RTX 4090.
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3D Gaussian Splats are cool, but they're static (Part 12). Spacetime Gaussian Feature Splatting is able to capture dynamic, and transient content within a scene and render them at 8K resolution and 60 FPS on an RTX 4090.

Dreaming Tulpa 🥓👑

37,339 görüntüleme • 2 yıl önce

By popular demand, we're sharing more examples that showcase CSM Cube's ability to generate high-quality 3D mesh topology and distinct parts. The results shown were achieved using our single-click Image-to-3D feature, but users can obtain even better quality with careful prompting and editing.
0:46

By popular demand, we're sharing more examples that showcase CSM Cube's ability to generate high-quality 3D mesh topology and distinct parts. The results shown were achieved using our single-click Image-to-3D feature, but users can obtain even better quality with careful prompting and editing.

Common Sense Machines

16,967 görüntüleme • 1 yıl önce

Working on a tool to generate low poly collision meshes from gaussian splats. It uses marching cubes and quadric error metric (QEM) decimation. Results are pretty good, it can get a usable sub 10Mb collision mesh from a 50Mb splat.
0:44

Working on a tool to generate low poly collision meshes from gaussian splats. It uses marching cubes and quadric error metric (QEM) decimation. Results are pretty good, it can get a usable sub 10Mb collision mesh from a 50Mb splat.

martin_casado

49,759 görüntüleme • 8 ay önce

We are thrilled to release the next leap in art-grade 3D generative models. Our single-click model pipeline gives unprecedented mesh outputs, with mesh parts-based topology. It is available now for all Cube tiers to start for free. ☑️ Our multi-stage hierarchical AI models produce a fully assembled 3D mesh with adaptive poly-counts, providing the clean, separated topology you need. ✅ A parts-based approach enables high-resolution meshes. 🌐 Quad and Triangle mesh support. Access now:
1:02

We are thrilled to release the next leap in art-grade 3D generative models. Our single-click model pipeline gives unprecedented mesh outputs, with mesh parts-based topology. It is available now for all Cube tiers to start for free. ☑️ Our multi-stage hierarchical AI models produce a fully assembled 3D mesh with adaptive poly-counts, providing the clean, separated topology you need. ✅ A parts-based approach enables high-resolution meshes. 🌐 Quad and Triangle mesh support. Access now:

Common Sense Machines

420,455 görüntüleme • 1 yıl önce

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.
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Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

AK

305,729 görüntüleme • 3 yıl önce

Nvidia presents Edify 3D! The method can generate high-quality 3D assets from text descriptions. It uses a diffusion model to create detailed quad-mesh topologies and high-resolution textures in under 2 minutes.
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Nvidia presents Edify 3D! The method can generate high-quality 3D assets from text descriptions. It uses a diffusion model to create detailed quad-mesh topologies and high-resolution textures in under 2 minutes.

Dreaming Tulpa 🥓👑

39,523 görüntüleme • 1 yıl önce

3D Alignment doesn't stop at geometry. This time, texture. Meshy 7 carries style, color and brushwork straight from your input image. Not a mesh that resembles your art. Your art, in 3D.❤️‍🔥
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3D Alignment doesn't stop at geometry. This time, texture. Meshy 7 carries style, color and brushwork straight from your input image. Not a mesh that resembles your art. Your art, in 3D.❤️‍🔥

MeshyAI

85,388 görüntüleme • 1 ay önce

Nvidia announces GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning paper page: Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions, addressing the limitations (e.g., flexibility and efficiency) imposed by mesh or NeRF-based representations. However, a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems, we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animation. Second, to stabilize and amortize the learning of millions of Gaussians, we propose to use neural implicit fields to predict the Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries and extract detailed meshes, we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method, GAvatar, enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality, and achieves extremely fast rendering (100 fps) at 1K resolution.
5:36

Nvidia announces GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning paper page: Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions, addressing the limitations (e.g., flexibility and efficiency) imposed by mesh or NeRF-based representations. However, a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems, we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animation. Second, to stabilize and amortize the learning of millions of Gaussians, we propose to use neural implicit fields to predict the Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries and extract detailed meshes, we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method, GAvatar, enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality, and achieves extremely fast rendering (100 fps) at 1K resolution.

AK

141,058 görüntüleme • 2 yıl önce

Adobe is entering the image-to-3D game! Their new method, LRM, can create high-fidelity 3D meshes from a single image in just 5 seconds 🔥 It's trained on 1 million objects and is able to generate objects from real-world images and generative AI models.
0:28

Adobe is entering the image-to-3D game! Their new method, LRM, can create high-fidelity 3D meshes from a single image in just 5 seconds 🔥 It's trained on 1 million objects and is able to generate objects from real-world images and generative AI models.

Dreaming Tulpa 🥓👑

270,942 görüntüleme • 2 yıl önce

VAST AI releases Triplane Meets Gaussian Splatting on Hugging Face Fast and Generalizable Single-View 3D Reconstruction with Transformers demo: TGS enables fast reconstruction from single-view image. It builds the 3D representation upon a hybrid Triplane-Gaussian representation by evaluating a transformer-based framework, from which 3D Gaussians would be decoded
0:23

VAST AI releases Triplane Meets Gaussian Splatting on Hugging Face Fast and Generalizable Single-View 3D Reconstruction with Transformers demo: TGS enables fast reconstruction from single-view image. It builds the 3D representation upon a hybrid Triplane-Gaussian representation by evaluating a transformer-based framework, from which 3D Gaussians would be decoded

AK

113,242 görüntüleme • 2 yıl önce

We just released HaMeR, our latest work for Hand Mesh Recovery! We reconstruct hands in 3D from a single image with big improvements in accuracy and robustness. Code and models are available. Also colab and Hugging Face demo!
0:52

We just released HaMeR, our latest work for Hand Mesh Recovery! We reconstruct hands in 3D from a single image with big improvements in accuracy and robustness. Code and models are available. Also colab and Hugging Face demo!

Georgios Pavlakos

38,336 görüntüleme • 2 yıl önce

MonST3R can estimate 3D shapes from videos over time, creating a dynamic point cloud and tracking camera positions! This method improves video depth estimation and separates moving from still objects more effectively than previous techniques. Links ⬇️
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MonST3R can estimate 3D shapes from videos over time, creating a dynamic point cloud and tracking camera positions! This method improves video depth estimation and separates moving from still objects more effectively than previous techniques. Links ⬇️

Dreaming Tulpa 🥓👑

13,484 görüntüleme • 1 yıl önce

Volumetrics are amazing but historically heavy, dense, and painful to art direct. 3D Gaussian Splatting changes that by turning volumetric simulations into real-time, lightweight assets. With sufficient temporal data, these can evolve into true 4D Gaussian Splats. Even from a single static 3DGS, artists can create pseudo-4D behavior using simple deformers and procedural effects - bending, twisting, reshaping, and re-timing volumetric forms at will. This is a massive shift in how volumetrics can be captured, manipulated, and directed and also redefines them as swift real-time modular assets.
1:05

Volumetrics are amazing but historically heavy, dense, and painful to art direct. 3D Gaussian Splatting changes that by turning volumetric simulations into real-time, lightweight assets. With sufficient temporal data, these can evolve into true 4D Gaussian Splats. Even from a single static 3DGS, artists can create pseudo-4D behavior using simple deformers and procedural effects - bending, twisting, reshaping, and re-timing volumetric forms at will. This is a massive shift in how volumetrics can be captured, manipulated, and directed and also redefines them as swift real-time modular assets.

Spenser Dickerson

13,023 görüntüleme • 9 ay önce

Over the weekend, I added multiplayer "paint ball" dogfight to Tiny Skies, so players can fly around pelting each other with colourful paint balls, just for fun. I tried meshy and tripo to generate some 3D models for the game and the mesh vertices count are really high and lags the game a lot (probably because my game is a globe with thousands of objects - albeit mostly instanced). To my surprise, I found Gemini 3.1 Pro to be really quite good at generating good looking 3D models that are low in poly count. It generated 3D models at a much higher quality than Opus 4.6, Sonnet 4.6 and GPT 5.4. You can see the upgraded biplane models and some objects in the video below. #vibejam
0:23

Over the weekend, I added multiplayer "paint ball" dogfight to Tiny Skies, so players can fly around pelting each other with colourful paint balls, just for fun. I tried meshy and tripo to generate some 3D models for the game and the mesh vertices count are really high and lags the game a lot (probably because my game is a globe with thousands of objects - albeit mostly instanced). To my surprise, I found Gemini 3.1 Pro to be really quite good at generating good looking 3D models that are low in poly count. It generated 3D models at a much higher quality than Opus 4.6, Sonnet 4.6 and GPT 5.4. You can see the upgraded biplane models and some objects in the video below. #vibejam

Danny Limanseta

36,419 görüntüleme • 5 ay önce

Meta presents Garment3DGen! A method that can stylize the geometry and textures from 2D image and 3D mesh garments, which can be fitted on top of parametric bodies and be simulated 🔥 More examples ⬇️
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Meta presents Garment3DGen! A method that can stylize the geometry and textures from 2D image and 3D mesh garments, which can be fitted on top of parametric bodies and be simulated 🔥 More examples ⬇️

Dreaming Tulpa 🥓👑

157,980 görüntüleme • 2 yıl önce