Leveraging Anthropometric Measurements to Improve Human Mesh Estimation and Ensure Consistent Body Shapes

Katja Ludwig, Julian Lorenz, Daniel Kienzle, Tuan Bui, Rainer Lienhart

Keywords: Computer Vision, Human Pose Estimation, 3D Human Pose Estimation, Human Mesh Estimation, Human Mesh Recovery, Anthropometric Measurements

Abstract: The basic body shape of a person does not change within a single video. However, most SOTA human mesh estimation (HME) models output a slightly different body shape for each video frame, which results in inconsistent body shapes for the same person. In contrast, we leverage anthropometric measurements like tailors are already obtaining from humans for centuries. We create a model called A2B that converts such anthropometric measurements to body shape parameters of human mesh models. Moreover, we find that finetuned SOTA 3D human pose estimation (HPE) models outperform HME models regarding the precision of the estimated keypoints. We show that applying inverse kinematics (IK) to the results of such a 3D HPE model and combining the resulting body pose with the A2B body shape leads to superior and consistent human meshes for challenging datasets like ASPset or fit3D, where we can lower the MPJPE by over 30 mm compared to SOTA HME models. Further, replacing HME models estimates of the body shape parameters with A2B model results not only increases the performance of these HME models, but also leads to consistent body shapes.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 3757