Physical motions are inherently continuous, and higher camera frame rates typically contribute to improved smoothness and temporal coherence. For the first time, we explore continuous representations of human motion sequences, featuring the ability to interpolate, inbetween, and even extrapolate any input motion sequences at arbitrary frame rates. To achieve this, we propose a novel parametric activation-induced hierarchical implicit representation framework, referred to as NAME, based on Implicit Neural Representations (INRs). Our method introduces a hierarchical temporal encoding mechanism that extracts features from motion sequences at multiple temporal scales, enabling effective capture of intricate temporal patterns. Additionally, we integrate a custom parametric activation function, powered by Fourier transformations, into the MLP-based decoder to enhance the expressiveness of the continuous representation. This parametric formulation significantly augments the model's ability to represent complex motion behaviors with high accuracy. Extensive evaluations across several benchmark datasets demonstrate the effectiveness and robustness of our proposed approach.

翻译：物理运动本质上是连续的，更高的相机帧率通常有助于提升平滑度与时间一致性。本文首次探索人体运动序列的连续表示方法，该表示具备在任意帧率下对输入运动序列进行插值、中间帧生成甚至外推的能力。为实现这一目标，我们基于隐式神经表示（INRs）提出了一种新颖的参数化激活诱导分层隐式表示框架，简称NAME。该方法引入了分层时序编码机制，从多时间尺度提取运动序列特征，从而有效捕捉复杂的时序模式。此外，我们在基于MLP的解码器中集成了由傅里叶变换驱动的自定义参数化激活函数，以增强连续表示的表达能力。这种参数化形式显著提升了模型高精度表征复杂运动行为的能力。在多个基准数据集上的广泛评估验证了所提方法的有效性与鲁棒性。