Papers › Object-Occluded Human Shape and Pose Estimation From a Single Color Image

Object-Occluded Human Shape and Pose Estimation From a Single Color Image

1 Jun 2020CVPR 2020 6archive 2025-07-28

Tianshu Zhang, Buzhen Huang, Yangang Wang

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 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 the latent feature supervision, which also includes a novel saliency map 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 the state-of-the-art comparing with previous methods. The dataset, codes are publicly available at https://www.yangangwang.com.

PaperPDF

Code

No code repository is listed for this paper in the archive or in Syntology's graph.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

3D Human Pose Estimation3D Human Shape EstimationHuman-Object Interaction DetectionImage InpaintingObjectPose Estimation

Datasets

Introduced by this paper, per the archive.

3DOH50K

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Human Pose Estimation 3DOH50K OOH Average PA-MPJPE (mm) 58.5 #1 of 1 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections