Morig: Motion-aware rigging of character meshes from point clouds
This addresses the challenge of rigging and animating diverse characters for applications in animation and gaming, though it appears incremental as it builds on existing rigging methods by incorporating motion cues.
The paper tackles the problem of automatically rigging character meshes from single-view point cloud streams, enabling animation based on captured motion, and reports that MoRig produces more accurate rigs compared to motion-ignoring approaches.
We present MoRig, a method that automatically rigs character meshes driven by single-view point cloud streams capturing the motion of performing characters. Our method is also able to animate the 3D meshes according to the captured point cloud motion. MoRig's neural network encodes motion cues from the point clouds into features that are informative about the articulated parts of the performing character. These motion-aware features guide the inference of an appropriate skeletal rig for the input mesh, which is then animated based on the point cloud motion. Our method can rig and animate diverse characters, including humanoids, quadrupeds, and toys with varying articulation. It accounts for occluded regions in the point clouds and mismatches in the part proportions between the input mesh and captured character. Compared to other rigging approaches that ignore motion cues, MoRig produces more accurate rigs, well-suited for re-targeting motion from captured characters.