Related papers: KAMA: 3D Keypoint Aware Body Mesh Articulation

KAMA: 3D Keypoint Aware Body Mesh Articulation

URL: http://arxiv.org/abs/2104.13502v1
Date: Tue, 27 Apr 2021 23:01:03 GMT
Title: KAMA: 3D Keypoint Aware Body Mesh Articulation
Authors: Umar Iqbal, Kevin Xie, Yunrong Guo, Jan Kautz, Pavlo Molchanov
Abstract summary: We propose an analytical solution to articulate a parametric body model, SMPL, via a set of straightforward geometric transformations. Our approach offers significantly better alignment to image content when compared to state-of-the-art approaches. Results on the challenging 3DPW and Human3.6M demonstrate that our approach yields state-of-the-art body mesh fittings.
Score: 79.04090630502782
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: We present KAMA, a 3D Keypoint Aware Mesh Articulation approach that allows us to estimate a human body mesh from the positions of 3D body keypoints. To this end, we learn to estimate 3D positions of 26 body keypoints and propose an analytical solution to articulate a parametric body model, SMPL, via a set of straightforward geometric transformations. Since keypoint estimation directly relies on image clues, our approach offers significantly better alignment to image content when compared to state-of-the-art approaches. Our proposed approach does not require any paired mesh annotations and is able to achieve state-of-the-art mesh fittings through 3D keypoint regression only. Results on the challenging 3DPW and Human3.6M demonstrate that our approach yields state-of-the-art body mesh fittings.

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