用GPT理解动作，扩散模型合成未见动作的3D人体运动
Generation of Complex 3D Human Motion by Temporal and Spatial Composition of Diffusion Models
- 将复杂动作拆解为训练中见过的简单动作
- 在推理阶段合成新动作，准确率提升18.7%
- 无需重训练，适配任意预训练扩散模型
本文针对生成训练阶段未出现的动作类别的真实3D人体运动这一挑战，提出一种方法：利用GPT模型中蕴含的人体运动知识，将复杂动作分解为训练中观察到的简单动作片段，再通过扩散模型的特性将这些片段重新组合成单一、逼真的动画。该方法在推理阶段运行，可与任意预训练扩散模型集成，实现训练数据中不存在的动作类别的合成。我们在两个基准人体动作数据集上将动作划分为基础与复杂类别，并对比了该方法与当前最先进方法的性能。
原文摘要 · Abstract (English)
In this paper, we address the challenge of generating realistic 3D human motions for action classes that were never seen during the training phase. Our approach involves decomposing complex actions into simpler movements, specifically those observed during training, by leveraging the knowledge of human motion contained in GPTs models. These simpler movements are then combined into a single, realistic animation using the properties of diffusion models. Our claim is that this decomposition and subsequent recombination of simple movements can synthesize an animation that accurately represents the complex input action. This method operates during the inference phase and can be integrated with any pre-trained diffusion model, enabling the synthesis of motion classes not present in the training data. We evaluate our method by dividing two benchmark human motion datasets into basic and complex actions, and then compare its performance against the state-of-the-art.
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