EquiFusion：通过等变潜扩散实现与运动学无关的人体运动预测
EquiFusion: Kinematics-Agnostic Human Motion Prediction via Equivariant Latent Diffusion
- Technical University of Munich（慕尼黑工业大学）
- Munich Center for Machine Learning（慕尼黑机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对现有3D人体运动预测模型受骨骼运动学硬编码限制的问题，提出EquiFusion模型，通过排列等变架构实现潜扩散，对未见运动学有跨数据集泛化能力，在基准测试中成果领先，建立了新的人体运动预测标准。
AI中文摘要：
现有的随机3D人体运动预测模型从根本上受到骨骼运动学硬编码的限制，严重限制了泛化能力，阻碍跨数据集训练，且需要复杂的数据重新定位。我们引入了EquiFusion，首个解决此瓶颈的与运动学无关的模型，它实现了具有排列等变架构的潜扩散模型。EquiFusion将运动学的连通性视为显式输入参数，确保其内部计算对关节顺序和图结构不可知。该新颖设计实现了对未见运动学的真正跨数据集泛化，并开启了新的零样本方向。EquiFusion在主要基准测试中取得了领先成果，比之前特定于运动学的方法紧凑75%，同时训练和推理更快，为稳健的人体运动预测建立了新的灵活标准。
英文摘要：
Existing Stochastic 3D Human Motion Prediction models are fundamentally constrained by hard-coding the skeleton kinematics, severely limiting generalization, preventing cross-dataset training, and requiring complex data retargeting. We introduce EquiFusion, the first kinematics-agnostic model to solve this bottleneck, implementing a latent diffusion model with a permutation equivariant architecture. EquiFusion treats the kinematics' connectivity as an explicit input parameter, ensuring its internal computations are inherently agnostic to joint ordering and graph structure. This novel design enables truly cross-dataset generalization to unseen kinematics and unlocks novel zero-shot directions, such as motion prediction from partial or occluded observations and targeted limb generation. EquiFusion achieves state-of-the-art results on major benchmarks, being up to 75% more compact than previous kinematics-specific methods, while achieving faster training and inference. EquiFusion thus establishes a new, flexible standard for robust human motion prediction. Model and training code are available at https://ceveloper.github.io/publications/equifusion/.
