Wassersplines for Neural Vector Field-Controlled Animation

Zhang, Paul; Smirnov, Dmitriy; Solomon, Justin

Wassersplines for Neural Vector Field-Controlled Animation

Files

v41i8pp031-041.pdf (37.43 MB)

wassersplines_sca_2022_supplementalmaterial.mp4 (75.16 MB)

Date

2022

Authors

Zhang, Paul
Smirnov, Dmitriy
Solomon, Justin

Publisher

The Eurographics Association and John Wiley & Sons Ltd.

Abstract

Much of computer-generated animation is created by manipulating meshes with rigs. While this approach works well for animating articulated objects like animals, it has limited flexibility for animating less structured free-form objects. We introduce Wassersplines, a novel trajectory inference method for animating unstructured densities based on recent advances in continuous normalizing flows and optimal transport. The key idea is to train a neurally-parameterized velocity field that represents the motion between keyframes. Trajectories are then computed by advecting keyframes through the velocity field. We solve an additional Wasserstein barycenter interpolation problem to guarantee strict adherence to keyframes. Our tool can stylize trajectories through a variety of PDE-based regularizers to create different visual effects. We demonstrate our tool on various keyframe interpolation problems to produce temporally-coherent animations without meshing or rigging.

CCS Concepts: Computing methodologies --> Motion processing; Point-based models

        @article{10.1111:cgf.14621
,
journal = {Computer Graphics Forum},
title = {{Wassersplines for Neural Vector Field-Controlled Animation
}},
author = {Zhang, Paul and 
Smirnov, Dmitriy and 
Solomon, Justin
},
year = {2022
},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.
},
ISSN = {1467-8659
},
DOI = {10.1111/cgf.14621
}
}