Inferring past human motion from RGB images is challenging due to the inherent uncertainty of the prediction problem. Thermal images, on the other hand, encode traces of past human-object interactions left in the environment via thermal radiation measurement. Based on this observation, we collect the first RGB-Thermal dataset for human motion analysis, dubbed Thermal-IM. Then we develop a three-stage neural network model for accurate past human pose estimation. Comprehensive experiments show that thermal cues significantly reduce the ambiguities of this task, and the proposed model achieves remarkable performance. The dataset is available at https://github.com/ZitianTang/Thermal-IM.

翻译：从RGB图像中推断过去的人体运动因预测问题固有的不确定性而极具挑战性。相比之下，热图像通过测量热辐射编码了环境中遗留的过去人-物交互痕迹。基于这一观察，我们收集了首个用于人体运动分析的RGB-热成像数据集，命名为Thermal-IM。随后，我们开发了一个三阶段神经网络模型，用于精确估计过去的人体姿态。综合实验表明，热线索显著降低了该任务的歧义性，且所提模型取得了卓越的性能。该数据集发布于https://github.com/ZitianTang/Thermal-IM。