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Human perception and understanding is a major domain of computer visionwhich, like many other vision subdomains recently, stands to gain from the useof large models pre-trained on large datasets. We hypothesize that the mostcommon pre-training strategy of relying on general purpose, object-centricimage datasets such as ImageNet, is limited by an important domain shift. Onthe other hand, collecting domain specific ground truth such as 2D or 3D labelsdoes not scale well. Therefore, we propose a pre-training approach based onself-supervised learning that works on human-centric data using only images.Our method uses pairs of images of humans: the first is partially masked andthe model is trained to reconstruct the masked parts given the visible ones anda 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 wellas human motion. We pre-train a model for body-centric tasks and one forhand-centric tasks. With a generic transformer architecture, these modelsoutperform existing self-supervised pre-training methods on a wide set ofhuman-centric downstream tasks, and obtain state-of-the-art performance forinstance when fine-tuning for model-based and model-free human mesh recovery.

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