标题： 基于逆处理的时间动态解耦以增强人体运动预测 显示英文标题

标题： Temporal Dynamics Decoupling with Inverse Processing for Enhancing Human Motion Prediction

摘要： 探索历史和未来运动行为之间的桥梁仍然是人类运动预测中的一个核心挑战。 虽然大多数现有方法将重建任务作为辅助任务整合到解码器中，从而提高了时空依赖性的建模，但它们忽略了重建和预测任务之间的潜在冲突。 在本文中，我们提出了一种新方法：逆向处理的时间解耦解码（\textbf{$TD^2IP$}）。 我们的方法有策略地分离了重建和预测解码过程，使用不同的解码器将共享的运动特征解码为历史或未来序列。 此外，逆向处理在时间维度上反转运动信息并将其重新引入模型，利用了人类运动行为的双向时间相关性。 通过缓解重建和预测任务之间的冲突并增强历史和未来信息的关联性，\textbf{$TD^2IP$}促进了对运动模式的更深入理解。 大量实验证明了我们的方法在现有方法中的适应性。 显示英文摘要

摘要： Exploring the bridge between historical and future motion behaviors remains a central challenge in human motion prediction. While most existing methods incorporate a reconstruction task as an auxiliary task into the decoder, thereby improving the modeling of spatio-temporal dependencies, they overlook the potential conflicts between reconstruction and prediction tasks. In this paper, we propose a novel approach: Temporal Decoupling Decoding with Inverse Processing (\textbf{$TD^2IP$}). Our method strategically separates reconstruction and prediction decoding processes, employing distinct decoders to decode the shared motion features into historical or future sequences. Additionally, inverse processing reverses motion information in the temporal dimension and reintroduces it into the model, leveraging the bidirectional temporal correlation of human motion behaviors. By alleviating the conflicts between reconstruction and prediction tasks and enhancing the association of historical and future information, \textbf{$TD^2IP$} fosters a deeper understanding of motion patterns. Extensive experiments demonstrate the adaptability of our method within existing methods.