根据体型生成更真实的动作，让不同身材的人动起来更自然。
HUMOS: Human Motion Model Conditioned on Body Shape
- 用体型信息指导动作生成，实现身体特征与运动匹配
- 仅用无配对数据训练，通过循环一致性等约束建模动作-体型关系
- 生成动作更真实多样，适合影视动画、虚拟人等需要个性化动作的场景
生成逼真的人体动作对计算机视觉与图形学应用至关重要。人体体型差异显著影响运动方式，但现有动作模型普遍忽略体型差异，依赖标准化平均体型，导致不同体型的动作缺乏区分度，运动不贴合实际。为此，我们提出一种基于体型条件的生成式动作模型。通过引入循环一致性、物理直觉和稳定性约束，仅使用无配对数据即可训练该模型，有效捕捉身份与运动间的关联。结果表明，该模型生成的动作在多样性、物理合理性与动态稳定性方面均优于当前最先进方法，定量与定性评估均表现更优。
原文摘要 · Abstract (English)
Generating realistic human motion is essential for many computer vision and graphics applications. The wide variety of human body shapes and sizes greatly impacts how people move. However, most existing motion models ignore these differences, relying on a standardized, average body. This leads to uniform motion across different body types, where movements don't match their physical characteristics, limiting diversity. To solve this, we introduce a new approach to develop a generative motion model based on body shape. We show that it's possible to train this model using unpaired data by applying cycle consistency, intuitive physics, and stability constraints, which capture the relationship between identity and movement. The resulting model generates diverse, physically plausible, and dynamically stable human motions that are both quantitatively and qualitatively more realistic than current state-of-the-art methods. More details are available on our project page https://CarstenEpic.github.io/humos/.
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