New diffusion models enhance 3D generation and mesh creation
ByPulseAugur Editorial·[13 sources]·
Researchers are developing new methods for 3D generation using diffusion models and voxel-based approaches. SymTRELLIS enforces symmetry in 3D models by learning linear transformations on voxel latents, improving physical usability. MeshWeaver uses a multi-level sparse-voxel encoder for autoregressive mesh generation, enhancing geometric context and compression. Discrete Voxel Diffusion (DVD) offers a framework for generating, assessing, and editing sparse voxels, providing interpretable dynamics and uncertainty estimation. MeshFlow generates artistic 3D meshes efficiently using a VAE and a Rectified Flow transformer, achieving faster generation times. PatchScene employs a patch-based voxel diffusion paradigm for large-scale LiDAR scene completion, ensuring coherent reconstruction and temporal consistency.
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These papers introduce novel techniques for 3D generation, potentially improving efficiency, fidelity, and applicability in areas like autonomous driving and artistic creation.
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Multiple research papers introducing novel methods for 3D generation and mesh creation.
arXiv:2606.04108v1 Announce Type: cross Abstract: Single-view 3D generative models have achieved impressive visual quality, yet they are not designed to satisfy structural or functional requirements, and in practice, often fall short. Symmetry is one such requirement: violations,…
MeshWeaver introduces an autoregressive mesh generation framework that predicts vertices directly rather than coordinates, utilizing a multi-level sparse-voxel encoder to enhance geometric context and achieve superior compression and fidelity.
arXiv:2605.07971v2 Announce Type: replace-cross Abstract: We introduce Discrete Voxel Diffusion (DVD), a discrete diffusion framework to generate, assess, and edit sparse voxels for SLat (Structured LATent) based 3D generative pipelines. Although discrete diffusion has not genera…
arXiv cs.CV
TIER_1English(EN)·Ziyang Yu, Xiang Li, Qiong Chang, Jun Miyazaki·
arXiv:2606.06255v1 Announce Type: cross Abstract: Point clouds are a primary sensory representation for robotic perception, underpinning LiDAR-based autonomous driving, simultaneous localization and mapping (SLAM), and navigation. Within these pipelines, Farthest Point Sampling (…
Point clouds are a primary sensory representation for robotic perception, underpinning LiDAR-based autonomous driving, simultaneous localization and mapping (SLAM), and navigation. Within these pipelines, Farthest Point Sampling (FPS) is the most well-known downsampling operator,…
arXiv cs.CV
TIER_1English(EN)·Jiale Xu, Wang Zhao, Ying Shan·
arXiv:2606.04688v1 Announce Type: new Abstract: Autoregressive mesh generation has gained attention by tokenizing meshes into sequences and training models in a language-modeling fashion. However, existing approaches suffer from two fundamental limitations: (i) low tokenization e…
arXiv cs.CV
TIER_1English(EN)·Weiyu Li, Antoine Toisoul, Tom Monnier, Roman Shapovalov, Rakesh Ranjan, Ping Tan, Andrea Vedaldi·
arXiv:2606.04621v1 Announce Type: new Abstract: We present MeshFlow, a new method for generating artist-like 3D meshes. Current mesh generators often adopt Auto-Regressive (AR) next-token prediction, a natural choice given the discrete nature of mesh topology. However, AR methods…
arXiv:2512.14099v3 Announce Type: replace Abstract: Motivated by discrete diffusion's success in language-vision modeling, we explore its potential for multi-view generation, a task dominated by continuous approaches. We introduce ViewMask-1-to-3, formulating multi-view generatio…
Autoregressive mesh generation has gained attention by tokenizing meshes into sequences and training models in a language-modeling fashion. However, existing approaches suffer from two fundamental limitations: (i) low tokenization efficiency, which yields long token sequences and…
We present MeshFlow, a new method for generating artist-like 3D meshes. Current mesh generators often adopt Auto-Regressive (AR) next-token prediction, a natural choice given the discrete nature of mesh topology. However, AR methods scale poorly because the inference cost is quad…
arXiv:2606.03915v1 Announce Type: new Abstract: We propose PatchScene, a novel diffusion-based framework for large-scale LiDAR scene completion. Unlike existing methods that rely on global latent representations or dense voxel grids, PatchScene adopts a patch-based voxel diffusio…
We propose PatchScene, a novel diffusion-based framework for large-scale LiDAR scene completion. Unlike existing methods that rely on global latent representations or dense voxel grids, PatchScene adopts a patch-based voxel diffusion paradigm that explicitly generates fine-graine…
<table> <tr><td> <a href="https://www.reddit.com/r/StableDiffusion/comments/1txwks9/cubepart_an_openvocabulary_partcontrollable_3d/"> <img alt="CubePart: An Open-Vocabulary Part-Controllable 3D Generator (local modal, extract and re-generate parts of a 3D mesh)" src="https://exte…
