Character rigging is the process where animators fit a skeleton to a 3D model, which then allows the animators to manipulate the movement of, or animate, the model. Currently, animators must manually define the skeleton’s joints, how they’re connected, and how the model’s body parts move. This is a very time intensive process that can take hours or days just for a single character. With the rapid rise in need for animation-ready characters and avatars in the areas of games, films, mixed reality and social media, character rigging is presently a major bottleneck to scaling the creation of animated characters.
In this work, the authors have written software called RigNet, which is an end-to-end automated method for producing animation rigs from input character models. By automating character rigging through RigNet, the character rigging process time is reduced from hours to minutes. RigNet is based on a deep learning architecture, trained on a large and diverse collection of rigged models, including their mesh, skeletons and corresponding skin weights. RigNet is able to predict both a unique skeleton and a skinning that match animator expectations, which is in contrast to prior art methods that just fit pre-defined, template skeletons to the 3D models that aren’t high enough quality. Finally, because all that is needed to use RigNet is a 3D model, animators do not need to be trained experts to rig characters, as is the case today.
