用函数空间建模运动相位，实现任意分辨率的自然动作生成
FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds
- 用相位流形替代离散时间解码，支持任意采样精度
- 相比基线模型重建误差降低45%以上，且跨骨架泛化能力强
- 适合动作生成、补全、超分辨率等任务，可解释性高
由于空间几何与时间动态强耦合，学习自然身体运动仍具挑战。将运动嵌入捕捉局部周期性的相位流形，已被证明对运动预测有效；但现有方法受限于固定骨骼和窄运动分布，应用范围有限。我们提出FunPhase，一种功能型周期自编码器，学习运动的相位流形，并以函数空间形式替代离散时间解码，实现可任意时间分辨率采样的平滑轨迹。FunPhase在统一的可解释相位流形中融合运动预测与生成，支持通过隐空间扩散生成动作，具备跨骨骼与跨数据集的泛化能力，并适用于动作超分辨率、部分肢体补全等下游任务。模型在所有指标上均比先前周期自编码器基线降低至少45%的重建误差，同时性能达到当前先进动作生成方法水平。
原文摘要 · Abstract (English)
Learning natural body motion remains challenging due to the strong coupling between spatial geometry and temporal dynamics. Embedding motion in phase manifolds, latent spaces that capture local periodicity, has proven effective for motion prediction; however, existing approaches are tied to fixed skeletons and narrow motion distributions, limiting their applicability across diverse settings. We introduce FunPhase, a functional periodic autoencoder that learns a phase manifold for motion and replaces discrete temporal decoding with a function-space formulation, enabling smooth trajectories that can be sampled at arbitrary temporal resolutions. FunPhase unifies motion prediction and generation within a single interpretable phase manifold, enabling motion generation via latent diffusion, generalizes across skeletons and datasets, and supports downstream tasks such as motion super-resolution and partial-body completion. Our model achieves substantially lower reconstruction error than prior periodic autoencoder baselines, achieving uniform improvements of at least $45\%$ across all metrics, while enabling a broader range of applications and performing on par with state-of-the-art motion generation methods.
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