This enables more accurate global translation estimation without generalisability loss. The inputs to our system are 2D joint keypoints, which are canonicalised in a novel way so as to reduce the dependency on intrinsic camera parameters - both at train and test time. a novel optimisation layer that prevents physically implausible foot-floor penetration as a hard constraint. an explicit rigid body dynamics model and 3. a proportional-derivative controller, with gains predicted by a neural network, that reduces delays even in the presence of fast motions, 2. It combines in a fully differentiable way several key innovations, i.e., 1. Unlike most neural methods for human motion capture, our approach, which we dub physionical, is aware of physical and environmental constraints. We present a new trainable system for physically plausible markerless 3D human motion capture, which achieves state-of-the-art results in a broad range of challenging scenarios.
#BOX SHOT 3D 3.6 FULL CODE#
Both the source code and the dataset are released see. We urge the reader to watch our supplementary video. In the experiments, our approach achieves state-of-the-art accuracy in 3D human motion capture on various metrics.
#BOX SHOT 3D 3.6 FULL FREE#
We evaluate GraviCap on a new dataset with ground-truth annotations for persons and different objects undergoing free flights. The proposed human-object interaction constraints ensure geometric consistency of the 3D reconstructions and improved physical plausibility of human poses compared to the unconstrained case. Our objective function is parametrised by the object's initial velocity and position, gravity direction and focal length, and jointly optimised for one or several free flight episodes. In contrast to existing monocular methods, we can recover scale, object trajectories as well as human bone lengths in meters and the ground plane's orientation, thanks to the awareness of the gravity constraining object motions. We focus on scenes with objects partially observed during a free flight. This paper proposes GraviCap, i.e., a new approach for joint markerless 3D human motion capture and object trajectory estimation from monocular RGB videos.
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