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MeshSplatting: Differentiable Rendering with Opaque Meshes Contributions: (i) An end-to-end optimization of mesh-based scene representations retains visual quality while training 2× faster than current state-of-the-art methods. (ii) Rather than a polygon soup, we generate a connected mesh by refining the vertex locations of a restricted Delaunay triangulation. (iii) Triangles... show more

MrNeRF

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15,044 views • 6 months ago •via X (Twitter)

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OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering Contributions: • We propose an occlusion-aware scene division strategy that considers the scene layout and camera co-visibilities. The resulting regions barely contain occlusions, and the corresponding training cameras have a higher average contribution, leading to improved reconstruction results. • We present a region-based rendering technique that accelerates 3D Gaussian splatting in large scenes. It eliminates much of the time-consuming processing of invisible 3D Gaussians, boosting rendering speeds without noticeable quality degradation. • We conduct extensive experiments on several large-scene datasets and demonstrate that OccluGaussian achieves superior rendering quality and faster rendering speed compared to previous state-of-the-art methods.

OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering Contributions: • We propose an occlusion-aware scene division strategy that considers the scene layout and camera co-visibilities. The resulting regions barely contain occlusions, and the corresponding training cameras have a higher average contribution, leading to improved reconstruction results. • We present a region-based rendering technique that accelerates 3D Gaussian splatting in large scenes. It eliminates much of the time-consuming processing of invisible 3D Gaussians, boosting rendering speeds without noticeable quality degradation. • We conduct extensive experiments on several large-scene datasets and demonstrate that OccluGaussian achieves superior rendering quality and faster rendering speed compared to previous state-of-the-art methods.

MrNeRF

10,718 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.

MrNeRF

12,323 views • 1 year ago

[SIGGRAPH '25] TeGA: Texture Space Gaussian Avatars for High-Resolution Dynamic Head Modeling Note: On the left that's a 3DGS rendering! Contributions: 1. We propose a simple approach for rigging 3D Gaussians within the continuous tangent space of 3DMM face models, allowing Gaussians to move freely across mesh triangles. 2. We propose a novel CNN-based deformation model that is agnostic to the number of 3D Gaussians, naturally enabling adaptively densification of the representation to improve detail where most needed, with expression-dependent shading. 3. We show significant improvements over baseline SOTA methods and demonstrate the ability to render even extreme close-up images at high quality.

[SIGGRAPH '25] TeGA: Texture Space Gaussian Avatars for High-Resolution Dynamic Head Modeling Note: On the left that's a 3DGS rendering! Contributions: 1. We propose a simple approach for rigging 3D Gaussians within the continuous tangent space of 3DMM face models, allowing Gaussians to move freely across mesh triangles. 2. We propose a novel CNN-based deformation model that is agnostic to the number of 3D Gaussians, naturally enabling adaptively densification of the representation to improve detail where most needed, with expression-dependent shading. 3. We show significant improvements over baseline SOTA methods and demonstrate the ability to render even extreme close-up images at high quality.

MrNeRF

28,434 views • 1 year ago

VGGT: Visual Geometry Grounded Transformer TL;DR: Is DUSt3R facing a formidable new rival? Contributions: (1) We introduce VGGT, a large feed-forward transformer that can, given one, a few, or even hundreds of images of a scene, predict all its key 3D attributes - including camera intrinsics and extrinsics, point maps, depth maps, and 3D point tracks - in seconds. (2) We demonstrate that VGGT’s predictions are directly usable, being highly competitive and usually better than those of state-of-the-art methods that use slow post-processing optimization techniques. (3) We also show that when further combined with BA post-processing, VGGT achieves state-of-the-art results across the board, even when compared to methods that specialize in a subset of 3D tasks, often improving quality substantially.

VGGT: Visual Geometry Grounded Transformer TL;DR: Is DUSt3R facing a formidable new rival? Contributions: (1) We introduce VGGT, a large feed-forward transformer that can, given one, a few, or even hundreds of images of a scene, predict all its key 3D attributes - including camera intrinsics and extrinsics, point maps, depth maps, and 3D point tracks - in seconds. (2) We demonstrate that VGGT’s predictions are directly usable, being highly competitive and usually better than those of state-of-the-art methods that use slow post-processing optimization techniques. (3) We also show that when further combined with BA post-processing, VGGT achieves state-of-the-art results across the board, even when compared to methods that specialize in a subset of 3D tasks, often improving quality substantially.

MrNeRF

29,461 views • 1 year ago

Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives paper page: Given a set of calibrated images of a scene, we present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. While many approaches focus on recovering high-fidelity 3D scenes, we focus on parsing a scene into mid-level 3D representations made of a small set of textured primitives. Such representations are interpretable, easy to manipulate and suited for physics-based simulations. Moreover, unlike existing primitive decomposition methods that rely on 3D input data, our approach operates directly on images through differentiable rendering. Specifically, we model primitives as textured superquadric meshes and optimize their parameters from scratch with an image rendering loss. We highlight the importance of modeling transparency for each primitive, which is critical for optimization and also enables handling varying numbers of primitives. We show that the resulting textured primitives faithfully reconstruct the input images and accurately model the visible 3D points, while providing amodal shape completions of unseen object regions. We compare our approach to the state of the art on diverse scenes from DTU, and demonstrate its robustness on real-life captures from BlendedMVS and Nerfstudio. We also showcase how our results can be used to effortlessly edit a scene or perform physical simulations.

Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives paper page: Given a set of calibrated images of a scene, we present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. While many approaches focus on recovering high-fidelity 3D scenes, we focus on parsing a scene into mid-level 3D representations made of a small set of textured primitives. Such representations are interpretable, easy to manipulate and suited for physics-based simulations. Moreover, unlike existing primitive decomposition methods that rely on 3D input data, our approach operates directly on images through differentiable rendering. Specifically, we model primitives as textured superquadric meshes and optimize their parameters from scratch with an image rendering loss. We highlight the importance of modeling transparency for each primitive, which is critical for optimization and also enables handling varying numbers of primitives. We show that the resulting textured primitives faithfully reconstruct the input images and accurately model the visible 3D points, while providing amodal shape completions of unseen object regions. We compare our approach to the state of the art on diverse scenes from DTU, and demonstrate its robustness on real-life captures from BlendedMVS and Nerfstudio. We also showcase how our results can be used to effortlessly edit a scene or perform physical simulations.

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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.

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140,960 views • 2 years ago

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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.

AK

20,651 views • 2 years ago

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

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

35,955 views • 2 months ago

Batched mesh test. 26 characters, 200 clones each. 5,700,000 triangles. Found that the tornado has the shape of a Torus. created with #threejs live demo (all source code is in html of the page) ->

Batched mesh test. 26 characters, 200 clones each. 5,700,000 triangles. Found that the tornado has the shape of a Torus. created with #threejs live demo (all source code is in html of the page) ->

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"#AI will never create clean meshes" AI 👇 This is the new #AI model from NVIDIA called Meshtron (jeez what a name 😅). It generates meshes of complex 3D objects closely resembling those created by professional artists. It opens the door to a more realistic generation of detailed 3D assets for animation, gaming, and virtual environments. The current version accepts the following control inputs: - Point cloud: Determines the shape of the output mesh. - Face count: Determines the density of the output mesh. - Quad ratio: Switches between quad and triangle tessellation. - Creativity: Can be adjusted to generate extra details in the model. Meshtron can either serve as a stand-alone remesher to improve the quality of existing models, or be used in tandem with a text-to-3D or image-to-3D model to generate artist-grade meshes from scratch.

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Gabriele Romagnoli

2,176,551 views • 1 year ago

The low poly meshes I created are much, much faster with physics / collision than those directly from the splat to mesh translation. Amazing. I now have an end to end workflow for generated 3D worlds with usable collisions. Works great in VR and mobile too ...

The low poly meshes I created are much, much faster with physics / collision than those directly from the splat to mesh translation. Amazing. I now have an end to end workflow for generated 3D worlds with usable collisions. Works great in VR and mobile too ...

martin_casado

54,452 views • 4 months ago

300 completely unique meshes ~2k triangles each, each mesh has a completely unique 512x512 texture, each mesh cloned 5 times and placed randomly. 1800 objects in total. This is running on an iPhone 12 Pro that is 5 years old.

300 completely unique meshes ~2k triangles each, each mesh has a completely unique 512x512 texture, each mesh cloned 5 times and placed randomly. 1800 objects in total. This is running on an iPhone 12 Pro that is 5 years old.

Ashxn

12,487 views • 1 year ago

Want high-quality 3D meshes with sharp geometric details? Try our newly released MeshFormer! It only takes 8 GPUs for two days of training, outperforming state-of-the-art models that use over a hundred GPUs! With 3D-native input guidance, representations, supervision, and post-processing, we significantly improve the training efficiency and geometric quality of feed-forward reconstruction models! Project page: Chong Zeng

Want high-quality 3D meshes with sharp geometric details? Try our newly released MeshFormer! It only takes 8 GPUs for two days of training, outperforming state-of-the-art models that use over a hundred GPUs! With 3D-native input guidance, representations, supervision, and post-processing, we significantly improve the training efficiency and geometric quality of feed-forward reconstruction models! Project page: Chong Zeng

Minghua Liu @ SIGGRAPHASIA25

16,320 views • 1 year ago

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NEW: Furikake. Furikake is a lightweight and high-performance particle generation plugin for After Effects. With a high-speed rendering engine that fully leverages the power of modern CPUs and a graphical user interface, it can smoothly handle a massive number of particles. It also supports Multi-Frame Rendering (MFR), Depth of Field (DOF), and 32-bit color for high-quality visual expression. #aftereffects #aescripts #particles #particlegenerator

aescripts+aeplugins

13,291 views • 7 months ago

In case you missed it, we recently launched "Post-training of LLMs," a short course where you'll: ✅ Understand when and why to use post-training methods like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning. ✅ Learn the concepts underlying the three post-training methods of SFT, DPO, and Online RL, their common use-cases, and how to curate high-quality data to effectively train a model using each method. ✅ Download a pre-trained model and implement post-training pipelines to turn a base model into an instruct model, change the identity of a chat assistant, and improve a model’s math capabilities. Learn more and enroll for free:

In case you missed it, we recently launched "Post-training of LLMs," a short course where you'll: ✅ Understand when and why to use post-training methods like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning. ✅ Learn the concepts underlying the three post-training methods of SFT, DPO, and Online RL, their common use-cases, and how to curate high-quality data to effectively train a model using each method. ✅ Download a pre-trained model and implement post-training pipelines to turn a base model into an instruct model, change the identity of a chat assistant, and improve a model’s math capabilities. Learn more and enroll for free:

DeepLearning.AI

16,746 views • 11 months ago

Big news I found a way to convert Gaussian Splatting into a standard mesh while preserving all the visual advantages of Gaussian Splatting, such as reflections, shadows, and lighting that changes depending on the viewing angle. The result is excellent performance, even on an old laptop or a VR headset. And of course, the mesh can be animated like any regular 3D mesh, while still keeping the visual benefits of GS. I’ll soon show a full-body character animated using this technique.

Big news I found a way to convert Gaussian Splatting into a standard mesh while preserving all the visual advantages of Gaussian Splatting, such as reflections, shadows, and lighting that changes depending on the viewing angle. The result is excellent performance, even on an old laptop or a VR headset. And of course, the mesh can be animated like any regular 3D mesh, while still keeping the visual benefits of GS. I’ll soon show a full-body character animated using this technique.

Alex

36,836 views • 24 days ago

$I never believed A.I. could replicate the quality of 3D channels like ZackDFilms. But that is coming to an end. It was always so far off, required so much prompting and you just faced too much bs. However, the gap between A.I and high quality 3D animations really is closing faster than I thought. A person who has barely any editing experience can now make a video like the one I linked below by using Nano Banana Pro + Kling A.I. Yes, it may not be quite there yet. Yes, it still requires a lot of prompting. But this is not slop content. And it's improving rapidly. Those who believe these A.I. videos won't reach the top standard we see in 3D Shorts are just coping imo. Paired with voiceover + sound design, you have a high quality video done for a fraction of the cost and time. The need for advanced skills is disappearing, which has big pros and cons. I'm very curious to see what will be considered as 'high barrier to entry' in the short-form space by the end of next year. The opportunities are endless with A.I. - you have to use it to your advantage.$

I never believed A.I. could replicate the quality of 3D channels like ZackDFilms. But that is coming to an end. It was always so far off, required so much prompting and you just faced too much bs. However, the gap between A.I and high quality 3D animations really is closing faster than I thought. A person who has barely any editing experience can now make a video like the one I linked below by using Nano Banana Pro + Kling A.I. Yes, it may not be quite there yet. Yes, it still requires a lot of prompting. But this is not slop content. And it's improving rapidly. Those who believe these A.I. videos won't reach the top standard we see in 3D Shorts are just coping imo. Paired with voiceover + sound design, you have a high quality video done for a fraction of the cost and time. The need for advanced skills is disappearing, which has big pros and cons. I'm very curious to see what will be considered as 'high barrier to entry' in the short-form space by the end of next year. The opportunities are endless with A.I. - you have to use it to your advantage.

igorm

202,383 views • 6 months ago

DeepMesh is out on Hugging Face Auto-Regressive Artist-mesh Creation with Reinforcement Learning Conditioned on point clouds and images, DeepMesh generates meshes with intricate details and precise topology, outperforming state-of-the-art methods in both precision and quality.

DeepMesh is out on Hugging Face Auto-Regressive Artist-mesh Creation with Reinforcement Learning Conditioned on point clouds and images, DeepMesh generates meshes with intricate details and precise topology, outperforming state-of-the-art methods in both precision and quality.

AK

109,125 views • 1 year ago