分离重建与预测解码，提升人体运动预测精度
Temporal Dynamics Decoupling with Inverse Processing for Enhancing Human Motion Prediction
- 用不同解码器分别处理历史和未来动作，避免任务冲突
- 通过逆向时间处理强化前后动作关联性，提升预测效果
- 可无缝集成到现有模型中，适合动作预测研究者
人体运动预测中，历史与未来行为之间的关联仍是一大挑战。现有方法多在解码器中加入重建任务以建模时空依赖，但忽略了重建与预测任务间的潜在冲突。本文提出新方法：逆向处理驱动的时序解耦解码（TD²IP）。该方法将重建与预测解码过程分离，使用不同解码器从共享运动特征中分别生成历史或未来序列。同时，逆向处理沿时间维度反转运动信息并重新引入模型，利用人体运动的双向时序相关性。通过缓解任务冲突并增强历史-未来信息关联，TD²IP深化了对运动模式的理解。大量实验表明，该方法可有效适配现有模型，提升预测性能。
原文摘要 · Abstract (English)
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.
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