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DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning

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arxiv 2503.15265 v1 pith:OS55SSUN submitted 2025-03-19 cs.CV

Ruowen Zhao , Junliang Ye , Zhengyi Wang , Guangce Liu , Yiwen Chen , Yikai Wang , Jun Zhu This is my paper · ORCID
classification cs.CV
keywords deepmeshmeshmeshespreferenceauto-regressiveefficientgenerationhuman
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Triangle meshes play a crucial role in 3D applications for efficient manipulation and rendering. While auto-regressive methods generate structured meshes by predicting discrete vertex tokens, they are often constrained by limited face counts and mesh incompleteness. To address these challenges, we propose DeepMesh, a framework that optimizes mesh generation through two key innovations: (1) an efficient pre-training strategy incorporating a novel tokenization algorithm, along with improvements in data curation and processing, and (2) the introduction of Reinforcement Learning (RL) into 3D mesh generation to achieve human preference alignment via Direct Preference Optimization (DPO). We design a scoring standard that combines human evaluation with 3D metrics to collect preference pairs for DPO, ensuring both visual appeal and geometric accuracy. 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. Project page: https://zhaorw02.github.io/DeepMesh/

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Cited by 5 Pith papers

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  3. PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers

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    PartCrafter generates several separable 3D part meshes at once from a single image by fine-tuning a pretrained 3D diffusion transformer with part identity tokens and local-global attention.

  4. XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding

    cs.GR 2025-07 conditional novelty 5.0 of 10

    XSpecMesh speeds up auto-regressive mesh generation by about 1.7x using multi-head speculative decoding with cross-attention heads and a probability threshold verification, while keeping output quality close to the ba...

  5. ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding

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