用日常物品当遥控器，一键生成角色动画
DancingBox: A Lightweight MoCap System for Character Animation from Physical Proxies
- 用单摄像头捕捉物体粗略运动，转为角色动作
- 通过生成模型和人体运动先验，还原逼真动画
- 无需专业设备，适合初学者玩创意动画
制作高质量3D角色动画通常需要专业软件或昂贵的动作捕捉系统。我们提出DancingBox，一种轻量级、基于视觉的动作捕捉系统，将捕捉过程重新构想为数字木偶戏。用户无需精确追踪身体动作，只需用单个网络摄像头捕捉日常物品（如毛绒玩具、香蕉）的粗略运动。这些代理动作通过条件化生成式运动模型，结合边界框表示和从大规模数据集学习的人体运动先验，被转化为真实感角色动画。为解决缺乏配对代理-动画数据的问题，我们通过将现有动作捕捉序列转换为代理表示来合成训练数据。用户研究显示，DancingBox支持多样化的代理物体，实现直观且富有创造力的角色动画，显著降低了新手动画师的入门门槛。
原文摘要 · Abstract (English)
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.
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