用扩散模型生成可动3D角色，让衣服头发自然动起来
DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters
- 用3D高斯表示+扩散模型预测关节位置，实现精准绑定
- 在动漫角色数据集AnimeRig上达到更真实的衣服头发动态效果
- 适合做角色动画生成的开发者和研究者参考
近期生成模型已能从多模态输入高质量重建3D角色，但复杂部件如衣物和头发的动画仍面临挑战，主要因缺乏大规模数据集和有效绑定方法。为此，我们构建了AnimeRig——一个包含详细骨骼与蒙皮标注的大规模数据集。基于此，提出DRiVE框架，用于生成并绑定具有复杂结构的3D人物。不同于现有方法，DRiVE采用3D高斯表示，实现高效动画与高质量渲染。进一步引入GSDiff——一种基于3D高斯的扩散模块，将关节位置建模为空间分布，克服回归方法局限。大量实验表明，DRiVE在绑定精度、衣物与头发动态表现上均优于现有方法，兼具高质量与高多样性。代码与数据集将在论文接受后公开供学术使用。
原文摘要 · Abstract (English)
Recent advances in generative models have enabled high-quality 3D character reconstruction from multi-modal. However, animating these generated characters remains a challenging task, especially for complex elements like garments and hair, due to the lack of large-scale datasets and effective rigging methods. To address this gap, we curate AnimeRig, a large-scale dataset with detailed skeleton and skinning annotations. Building upon this, we propose DRiVE, a novel framework for generating and rigging 3D human characters with intricate structures. Unlike existing methods, DRiVE utilizes a 3D Gaussian representation, facilitating efficient animation and high-quality rendering. We further introduce GSDiff, a 3D Gaussian-based diffusion module that predicts joint positions as spatial distributions, overcoming the limitations of regression-based approaches. Extensive experiments demonstrate that DRiVE achieves precise rigging results, enabling realistic dynamics for clothing and hair, and surpassing previous methods in both quality and versatility. The code and dataset will be made public for academic use upon acceptance.
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