Ssman: self-supervised masked adaptive network for 3D human pose estimation

The modern deep learning-based models for 3D human pose estimation from monocular images always lack the adaption ability between occlusion and non-occlusion scenarios, which might restrict the performance of current methods when faced with various scales of occluded conditions. In an attempt to tackle this problem, we propose a novel network called self-supervised masked adaptive network (SSMAN). Firstly, we leverage different levels of masks to cover the richness of occlusion in fully in-the-wild environment. Then, we design a multi-line adaptive network, which could be trained with various scales of masked images in parallel. Based on this masked adaptive network, we train it with self-supervised learning to enforce the consistency across the outputs under different mask ratios. Furthermore, a global refinement module is proposed to leverage global features of the human body to refine the human pose estimated solely by local features. We perform extensive experiments both on the occlusion datasets like 3DPW-OCC and OCHuman and general datasets such as Human3.6M and 3DPW. The results show that SSMAN achieves new state-of-the-art performance on both lightly and heavily occluded benchmarks and is highly competitive with significant improvement on standard benchmarks.