AGORA: Avatars in Geography Optimized for Regression Analysis
CVPR 2021 While the accuracy of 3D human pose estimation from images has steadily improved on benchmark datasets, the best methods still fail in many real-world scenarios. This suggests that there is a domain gap between current datasets and common scenes containing people. To obtain ground-truth 3D...
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Zusammenfassung: | CVPR 2021 While the accuracy of 3D human pose estimation from images has steadily
improved on benchmark datasets, the best methods still fail in many real-world
scenarios. This suggests that there is a domain gap between current datasets
and common scenes containing people. To obtain ground-truth 3D pose, current
datasets limit the complexity of clothing, environmental conditions, number of
subjects, and occlusion. Moreover, current datasets evaluate sparse 3D joint
locations corresponding to the major joints of the body, ignoring the hand pose
and the face shape. To evaluate the current state-of-the-art methods on more
challenging images, and to drive the field to address new problems, we
introduce AGORA, a synthetic dataset with high realism and highly accurate
ground truth. Here we use 4240 commercially-available, high-quality, textured
human scans in diverse poses and natural clothing; this includes 257 scans of
children. We create reference 3D poses and body shapes by fitting the SMPL-X
body model (with face and hands) to the 3D scans, taking into account clothing.
We create around 14K training and 3K test images by rendering between 5 and 15
people per image using either image-based lighting or rendered 3D environments,
taking care to make the images physically plausible and photoreal. In total,
AGORA consists of 173K individual person crops. We evaluate existing
state-of-the-art methods for 3D human pose estimation on this dataset and find
that most methods perform poorly on images of children. Hence, we extend the
SMPL-X model to better capture the shape of children. Additionally, we
fine-tune methods on AGORA and show improved performance on both AGORA and
3DPW, confirming the realism of the dataset. We provide all the registered 3D
reference training data, rendered images, and a web-based evaluation site at
https://agora.is.tue.mpg.de/. |
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DOI: | 10.48550/arxiv.2104.14643 |