Object-Occluded Human Shape and Pose Estimation With Probabilistic Latent Consistency.

Occlusions between human and objects, especially for the activities of human-object interactions, are very common in practical applications. However, most of the existing approaches for 3D human shape and pose estimation require that human bodies are well captured without occlusions or with minor self-occlusions. In this paper, we focus on the problem of directly estimating the object-occluded human shape and pose from single color images. Our key idea is to utilize a partial UV map to represent an object-occluded human body, and the full 3D human shape estimation is ultimately converted as an image inpainting problem. We propose a novel two-branch network architecture to train an end-to-end regressor via a latent distribution consistency, which also includes a novel visible feature sub-net to extract the human information from object-occluded color images. To supervise the network training, we further build a novel dataset named as 3DOH50K. Several experiments are conducted to reveal the effectiveness of the proposed method. Experimental results demonstrate that the proposed method achieves state-of-the-art compared with previous methods. The dataset and codes are publicly available at https://www.yangangwang.com/papers/ZHANG-OOH-2020-03.html.

Pose2UV: Single-Shot Multiperson Mesh Recovery With Deep UV Prior.

In this work, we focus on the task of multi-person mesh recovery from a single color image, where the key issue is to tackle the pixel-level ambiguities caused by inter-person occlusions. Overall, there are two main technical challenges when addressing the ambiguities: how to extract valid target features under occlusions and how to reconstruct reasonable human meshes with only a handful of body cues? To deal with these problems, our key idea is to utilize the predicted 2D poses to locate and separate the target person, and reconstruct them with a novel learning-based UV prior. Specifically, we propose a visible pose-mask module to help extract valid target features, then train a dense body mesh prior to promote reconstructing natural mesh represented by the UV position map. To evaluate the performance of our proposed method under occlusions, we further build an in-the-wild 3D multi-person benchmark named as 3DMPB. Experimental results demonstrate that our method achieves state-of-the-art compared with previous methods. The dataset, codes are publicly available on our website.