Abstract
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 lack scalability and remain confined to specific 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 supports downstream tasks such as super-resolution and partial-body motion completion, generalizes across skeletons and datasets, and unifies motion prediction and generation within a single interpretable manifold. Our model achieves substantially lower reconstruction error than prior periodic autoencoder baselines while enabling a broader range of applications and performing on par with state-of-the-art motion generation methods.
Abstract (translated)
学习自然身体动作仍然具有挑战性，因为空间几何和时间动态之间存在强烈的耦合。将运动嵌入到相位流形中（即捕获局部周期性的潜在空间）已被证明对于运动预测非常有效；然而，现有的方法缺乏可扩展性，并且仍局限于特定的场景设置内。我们引入了FunPhase，这是一种功能性周期自动编码器，它为动作学习了一个相位流形，并用函数空间形式代替了离散的时间解码，从而实现了可以在任意时间分辨率下采样的平滑轨迹。FunPhase支持下游任务，如超分辨率和部分身体运动完成，能够在不同的骨骼结构和数据集中泛化，并且将运动预测与生成统一在一个单一的可解释流形内。我们的模型在重建误差方面显著低于先前的周期自动编码器基准线，同时实现了更广泛的应用范围，并且其性能与最先进的运动生成方法相当。
URL
https://arxiv.org/abs/2512.09423
