Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917034858643456 |
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| author | Liu, Jian Xu, Jing Guo, Song Li, Jing Guo, Jingfeng Yu, Jiaao Weng, Haohan Lei, Biwen Yang, Xianghui Chen, Zhuo Zhu, Fangqi Han, Tao Guo, Chunchao |
| author_facet | Liu, Jian Xu, Jing Guo, Song Li, Jing Guo, Jingfeng Yu, Jiaao Weng, Haohan Lei, Biwen Yang, Xianghui Chen, Zhuo Zhu, Fangqi Han, Tao Guo, Chunchao |
| contents | 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-level rewards that struggle to capture local structure details. To address these challenges, we present Mesh-RFT, a novel fine-grained reinforcement fine-tuning framework that employs Masked Direct Preference Optimization (M-DPO) to enable localized refinement via quality-aware face masking. To facilitate efficient quality evaluation, we introduce an objective topology-aware scoring system to evaluate geometric integrity and topological regularity at both object and face levels through two metrics: Boundary Edge Ratio (BER) and Topology Score (TS). By integrating these metrics into a fine-grained RL strategy, Mesh-RFT becomes the first method to optimize mesh quality at the granularity of individual faces, resolving localized errors while preserving global coherence. Experiment results show that our M-DPO approach reduces Hausdorff Distance (HD) by 24.6% and improves Topology Score (TS) by 3.8% over pre-trained models, while outperforming global DPO methods with a 17.4% HD reduction and 4.9% TS gain. These results demonstrate Mesh-RFT's ability to improve geometric integrity and topological regularity, achieving new state-of-the-art performance in production-ready mesh generation. Project Page: https://hitcslj.github.io/mesh-rft/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16761 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning Liu, Jian Xu, Jing Guo, Song Li, Jing Guo, Jingfeng Yu, Jiaao Weng, Haohan Lei, Biwen Yang, Xianghui Chen, Zhuo Zhu, Fangqi Han, Tao Guo, Chunchao Computer Vision and Pattern Recognition 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-level rewards that struggle to capture local structure details. To address these challenges, we present Mesh-RFT, a novel fine-grained reinforcement fine-tuning framework that employs Masked Direct Preference Optimization (M-DPO) to enable localized refinement via quality-aware face masking. To facilitate efficient quality evaluation, we introduce an objective topology-aware scoring system to evaluate geometric integrity and topological regularity at both object and face levels through two metrics: Boundary Edge Ratio (BER) and Topology Score (TS). By integrating these metrics into a fine-grained RL strategy, Mesh-RFT becomes the first method to optimize mesh quality at the granularity of individual faces, resolving localized errors while preserving global coherence. Experiment results show that our M-DPO approach reduces Hausdorff Distance (HD) by 24.6% and improves Topology Score (TS) by 3.8% over pre-trained models, while outperforming global DPO methods with a 17.4% HD reduction and 4.9% TS gain. These results demonstrate Mesh-RFT's ability to improve geometric integrity and topological regularity, achieving new state-of-the-art performance in production-ready mesh generation. Project Page: https://hitcslj.github.io/mesh-rft/. |
| title | Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.16761 |