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We introduce DOPE, the first method to detect and estimate whole-body 3Dhuman poses, including bodies, hands and faces, in the wild. Achieving thislevel of details is key for a number of applications that require understandingthe interactions of the people with each other or with the environment. Themain challenge is the lack of in-the-wild data with labeled whole-body 3Dposes. In previous work, training data has been annotated or generated forsimpler tasks focusing on bodies, hands or faces separately. In this work, wepropose to take advantage of these datasets to train independent experts foreach part, namely a body, a hand and a face expert, and distill their knowledgeinto a single deep network designed for whole-body 2D-3D pose detection. Inpractice, given a training image with partial or no annotation, each partexpert detects its subset of keypoints in 2D and 3D and the resultingestimations are combined to obtain whole-body pseudo ground-truth poses. Adistillation loss encourages the whole-body predictions to mimic the experts'outputs. Our results show that this approach significantly outperforms the samewhole-body model trained without distillation while staying close to theperformance of the experts. Importantly, DOPE is computationally less demandingthan the ensemble of experts and can achieve real-time performance. Test codeand models are available athttps://europe.naverlabs.com/research/computer-vision/dope.

DOPE: Distillation Of Part Experts for whole-body 3D pose estimation in the wild