提出新型自交惩罚损失，显著减少人体动作生成中的身体穿插问题。
Self-Intersection-Aware 3D Human Motion Generation Using an Efficient Human Sphere Proxy
- 用人体球体代理模型计算自交损失，速度提升98%，内存降低83%
- 在MDM和MoMask上应用后，自交现象减少最多49%且其他指标提升
- 方法通用性强，适合追求高质量动作生成的研究者与开发者
近年来人体动作生成取得显著进展，顶尖方法在主流评估基准上甚至超过真实数据表现。然而视觉检查显示，当前先进方法仍频繁生成身体部件相互穿插的自交动作，严重影响运动质量。本文提出一种新型自交惩罚损失，基于人体几何的球体代理模型构建，相比基于三角网格的方法，计算速度提升98%，内存使用减少83%。该损失与具体生成方法无关，已成功应用于最近的人体动作扩散模型（MDM）和MoMask。大量实验表明，生成动作中自交现象最多减少49%，同时其他评估指标也得到改善。代码已开源：https://github.com/boschresearch/humansphereproxy。
原文摘要 · Abstract (English)
Human motion generation has made tremendous progress in recent years, with state-of-the-art approaches surpassing ground truth data in leading evaluation benchmarks. However, visual inspection of the generated motions paints a different picture. Even state-of-the-art approaches generate motions frequently containing self-intersections, i.e., body parts interpenetrating, which are strong artifacts, severely limiting the perceived motion quality. We introduce a novel loss, which explicitly penalizes self-intersections, to the training of human motion generation methods. We base our loss on a sphere proxy of human geometry, which allows us to calculate a self-intersection loss 98% faster and uses 83% less memory than comparable methods based on triangular meshes. The loss is agnostic to the specific approach, and we add it to the training of the recent human motion generation methods human motion diffusion model (MDM) and MoMask. Our extensive experiments show a reduction of self-intersections in generated motions of up to 49% while improving other evaluation metrics. The code is available at https://github.com/boschresearch/humansphereproxy .
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