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Schlagwörter:
Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Graphics, cs.GR
Zusammenfassung:
Large datasets are the cornerstone of recent advances in computer vision
using deep learning. In contrast, existing human motion capture (mocap)
datasets are small and the motions limited, hampering progress on learning
models of human motion. While there are many different datasets available, they
each use a different parameterization of the body, making it difficult to
integrate them into a single meta dataset. To address this, we introduce AMASS,
a large and varied database of human motion that unifies 15 different optical
marker-based mocap datasets by representing them within a common framework and
parameterization. We achieve this using a new method, MoSh++, that converts
mocap data into realistic 3D human meshes represented by a rigged body model;
here we use SMPL [doi:10.1145/2816795.2818013], which is widely used and
provides a standard skeletal representation as well as a fully rigged surface
mesh. The method works for arbitrary marker sets, while recovering soft-tissue
dynamics and realistic hand motion. We evaluate MoSh++ and tune its
hyperparameters using a new dataset of 4D body scans that are jointly recorded
with marker-based mocap. The consistent representation of AMASS makes it
readily useful for animation, visualization, and generating training data for
deep learning. Our dataset is significantly richer than previous human motion
collections, having more than 40 hours of motion data, spanning over 300
subjects, more than 11,000 motions, and will be publicly available to the
research community.
