  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.
