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20242026
most citedHunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

9 citations · 9 across the 4 of their papers we have counts for

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cs.CV2026

Mesh BDF: Barycentric Dominance Field for 3D Native Mesh Generation

Gaochao Song, Haohan Weng, Luo Zhang +2

Autoregressive (AR) modeling has recently achieved remarkable progress in native 3D mesh generation, largely due to its natural ability to handle variable-length, discrete data str…

cs.CV20269 cited

Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

Zibo Zhao, Zeqiang Lai, Qingxiang Lin +71

We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two foundation components: a large-sca…

cs.CV2026

Topology-Preserved Auto-regressive Mesh Generation in the Manner of Weaving Silk

Gaochao Song, Zibo Zhao, Haohan Weng +3

Existing auto-regressive mesh generation approaches suffer from ineffective topology preservation, which is crucial for practical applications. This limitation stems from previous…

cs.CV2026

Mesh-Pro: Asynchronous Advantage-guided Ranking Preference Optimization for Artist-style Quadrilateral Mesh Generation

Zhen Zhou, Jian Liu, Biwen Lei +10

Reinforcement learning (RL) has demonstrated remarkable success in text and image generation, yet its potential in 3D generation remains largely unexplored. Existing attempts typic…

cs.CV2026

QuadGPT: Native Quadrilateral Mesh Generation with Autoregressive Models

Jian Liu, Chunshi Wang, Song Guo +9

The generation of quadrilateral-dominant meshes is a cornerstone of professional 3D content creation. However, existing generative models generate quad meshes by first generating t…

cs.CV2025

Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning

Jian Liu, Jing Xu, Song Guo +10

Existing pretrained models for 3D mesh generation often suffer from data biases and produce low-quality results, while global reinforcement learning (RL) methods rely on object-lev…