Towards Generalization of 3D Human Pose Estimation In The Wild
- URL: http://arxiv.org/abs/2004.09989v1
- Date: Tue, 21 Apr 2020 13:31:58 GMT
- Title: Towards Generalization of 3D Human Pose Estimation In The Wild
- Authors: Renato Baptista, Alexandre Saint, Kassem Al Ismaeil, Djamila Aouada
- Abstract summary: 3DBodyTex.Pose is a dataset that addresses the task of 3D human pose estimation in-the-wild.
3DBodyTex.Pose offers high quality and rich data containing 405 different real subjects in various clothing and poses, and 81k image samples with ground-truth 2D and 3D pose annotations.
- Score: 73.19542580408971
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose 3DBodyTex.Pose, a dataset that addresses the task
of 3D human pose estimation in-the-wild. Generalization to in-the-wild images
remains limited due to the lack of adequate datasets. Existent ones are usually
collected in indoor controlled environments where motion capture systems are
used to obtain the 3D ground-truth annotations of humans. 3DBodyTex.Pose offers
high quality and rich data containing 405 different real subjects in various
clothing and poses, and 81k image samples with ground-truth 2D and 3D pose
annotations. These images are generated from 200 viewpoints among which 70
challenging extreme viewpoints. This data was created starting from high
resolution textured 3D body scans and by incorporating various realistic
backgrounds. Retraining a state-of-the-art 3D pose estimation approach using
data augmented with 3DBodyTex.Pose showed promising improvement in the overall
performance, and a sensible decrease in the per joint position error when
testing on challenging viewpoints. The 3DBodyTex.Pose is expected to offer the
research community with new possibilities for generalizing 3D pose estimation
from monocular in-the-wild images.
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