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About this paper
Jian Liu, Jing Xu, Song Guo, Jing Li, Jingfeng Guo, Jiaao Yu, Haohan Weng, Biwen Lei and 5 more
Researchers from Tencent Hunyuan and Hong Kong University of Science and Technology developed Mesh-RFT, a framework that employs Masked Direct Preference Optimization (M-DPO) and an objective, topology-aware scoring system to enhance 3D mesh generation. This approach reduced Hausdorff Distance by 24.6% and improved Topology Score by 3.8% compared to pre-trained models, yielding meshes with superior geometric accuracy and topological fidelity.
Key insights
- Global reinforcement signals are insufficient for 3D mesh quality, which often varies locally; fine-grained, localized optimization is crucial for addressing specific topological and geometric imperfections.
- An objective, automated multi-metric scoring system (BER, TS, HD) can reliably quantify mesh quality and construct preference datasets, overcoming the limitations of subjective and resource-intensive manual annotations.
- Applying local masks in Direct Preference Optimization enables the model to focus refinement efforts on identified low-quality regions while preserving well-formed parts, leading to more targeted and effective optimization.
Summary by alphaXiv.
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