Cross-view and Cross-pose Completion for 3D Human Understanding

Abstract

Human perception and understanding is a major domain of computer vision which like many other vision subdomains recently stands to gain from the use of large models pre-trained on large datasets. We hypothesize that the most common pre-training strategy of relying on general purpose object-centric image datasets such as ImageNet is limited by an important domain shift. On the other hand collecting domain-specific ground truth such as 2D or 3D labels does not scale well. Therefore we propose a pre-training approach based on self-supervised learning that works on human-centric data using only images. Our method uses pairs of images of humans: the first is partially masked and the model is trained to reconstruct the masked parts given the visible ones and a second image. It relies on both stereoscopic (cross-view) pairs and temporal (cross-pose) pairs taken from videos in order to learn priors about 3D as well as human motion. We pre-train a model for body-centric tasks and one for hand-centric tasks. With a generic transformer architecture these models outperform existing self-supervised pre-training methods on a wide set of human-centric downstream tasks and obtain state-of-the-art performance for instance when fine-tuning for model-based and model-free human mesh recovery.