Placing Human Animations into 3D Scenes by Learning Interaction- and Geometry-Driven Keyframes
IEEE Workshop/Winter Conference on Applications of Computer Vision(2022)
Abstract
We present a novel method for placing a 3D human animation into a 3D scene
while maintaining any human-scene interactions in the animation. We use the
notion of computing the most important meshes in the animation for the
interaction with the scene, which we call "keyframes." These keyframes allow us
to better optimize the placement of the animation into the scene such that
interactions in the animations (standing, laying, sitting, etc.) match the
affordances of the scene (e.g., standing on the floor or laying in a bed). We
compare our method, which we call PAAK, with prior approaches, including POSA,
PROX ground truth, and a motion synthesis method, and highlight the benefits of
our method with a perceptual study. Human raters preferred our PAAK method over
the PROX ground truth data 64.6% of the time. Additionally, in direct
comparisons, the raters preferred PAAK over competing methods including 61.5%
compared to POSA.
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