Learning Motion Priors for 4D Human Body Capture in 3D Scenes

Understand

Recovering high-quality 3D human motion in complex scenes from monocular videos is important for many applications, ranging from AR/VR to robotics.

• However, capturing realistic human-scene interactions, while dealing with occlusions and partial views, is challenging; current approaches are still far from achieving compelling results.
• We address this problem by proposing LEMO: LEarning human MOtion priors for 4D human body capture.
• By leveraging the large-scale motion capture dataset AMASS, we introduce a novel motion smoothness prior, which strongly reduces the jitters exhibited by poses recovered over a sequence.