Human Pose Estimation in Low-Light Condition With Decomposition and Modulation

Human pose estimation (HPE) is a fundamental problem in computer vision, aiming to locate anatomical keypoints of the human body in a given picture. Benefiting from recent progress in deep learning, dominant HPE methods can achieve more advanced performance. Unfortunately, these methods rely heavily on large-scale, high-quality datasets captured expensively, resulting in limited learning capabilities in data-constrained low-light situations. Existing methods enhance the model's ability for low-light scenarios by performing intermediate feature alignment between low-light image and its well-lit counterpart. However, these methods fall short in fully exploiting explicit semantic feature exploitation that is independent of lighting conditions, resulting in sub-optimal performance. In this paper, we propose a Progressive Decomposition-Modulation network (PDMNet) for human pose estimation in extremely low-light condition. In particular, PDMNet mainly consists of 1) a semantic-specific decomposition module (SDM) for decomposing reflectance component with rich semantic information, and 2) a semantic-specific modulation mechanism (SMM) that enables the reflectance component to modulate the representation learning of human body parts in a tailored manner. Two closely-related components cooperate with each other to achieve more effective content-specific feature learning in low-light conditions. We further equip them progressively into different scales to enhance the feature learning. Experimental results demonstrate the superiority of PDMNet over state-of-the-art models on publicly available datasets. Our code will be released soon.