DancingBox: A Lightweight MoCap System for Character Animation from Physical Proxies
This addresses the problem of high cost and expertise barriers in character animation for novice animators, representing an incremental improvement by adapting existing methods to a new application.
The paper tackles the problem of making motion capture accessible to novices by introducing DancingBox, a lightweight vision-based system that uses everyday objects as proxies to generate character animations, with a user study showing it enables intuitive animation using diverse proxies.
Creating compelling 3D character animations typically requires either expert use of professional software or expensive motion capture systems operated by skilled actors. We present DancingBox, a lightweight, vision-based system that makes motion capture accessible to novices by reimagining the process as digital puppetry. Instead of tracking precise human motions, DancingBox captures the approximate movements of everyday objects manipulated by users with a single webcam. These coarse proxy motions are then refined into realistic character animations by conditioning a generative motion model on bounding-box representations, enriched with human motion priors learned from large-scale datasets. To overcome the lack of paired proxy-animation data, we synthesize training pairs by converting existing motion capture sequences into proxy representations. A user study demonstrates that DancingBox enables intuitive and creative character animation using diverse proxies, from plush toys to bananas, lowering the barrier to entry for novice animators.