DeepMesh
Researchers from Tsinghua University, Nanyang Technological University, and ShengShu propose DeepMesh, a transformer-based system that generates high-quality 3D meshes with artist-like topology. Key ideas include:
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Efficient mesh tokenization – They introduce a new algorithm that compresses mesh sequences by ~72% while preserving geometric detail, enabling higher-resolution mesh generation at scale.
Artist-like topology – Unlike dense or incomplete meshes from existing approaches, DeepMesh predicts structured triangle layouts that are aesthetic and easy to edit, thanks to a refined pre-training process and better data curation.
Reinforcement Learning with human feedback – The authors adopt Direct Preference Optimization (DPO) to align mesh generation with human preferences. They collect pairwise user labels on geometry quality and aesthetics, then fine-tune the model to produce more appealing, complete meshes.
Scalable generation – DeepMesh can handle large meshes (tens of thousands of faces) and supports both point cloud- and image-based conditioning, outperforming baselines like MeshAnythingv2 and BPT in geometric accuracy and user ratings.