Wassersplines for Neural Vector Field‐Controlled Animation
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 01, 2022
- Source ID
- 10.1111/cgf.14621
Entities
People
- Dmitriy Smirnov
- Justin Solomon
- Paul Zhang
Organizations
- Air Force Office of Scientific Research
- Army Research Office
- Massachusetts Institute of Technology
- National Science Foundation