发表机构
DisneyResearch|Studios Switzerland; ETH Zürich（迪士尼研究工作室瑞士分部; 苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出Two2Four框架，采用两阶段生成扩散模型，可从人类动作生成可控逼真四足动物动作，用于动画和虚拟制作，效果优于现有重定向方法。
AI 中文摘要
虚拟制作中逼真的动物动作通常通过两种方式获得：一是对高度训练的表演者进行动作捕捉，让其精准模仿动物行为；二是使用复杂的控制设置对普通人类动作进行重定向。这两种方法都颇具挑战性，且往往无法完全复现自然动物动作的细微差别，因此催生了数据驱动的替代方案。我们提出了一种自动的人类到四足动物的操控框架，可从普通人类动作数据生成合理且可控的四足动物动作。该方法采用仅在四足动物动作数据上训练的两阶段生成扩散模型，通过引入结构化条件与修复策略，支持行走、奔跑、跳跃、坐、躺等多种动作，还实现了头部动作控制、单肢操控等细粒度直观控制。实验结果表明，与现有重定向方法相比，该方法在动作逼真度和可控性上均有提升，凸显了其作为动画与虚拟制作工具的有效性。
英文摘要
Realistic animal motion for virtual production is typically obtained either through motion capture of highly trained performers who accurately mimic animal behavior, or by retargeting ordinary human motion using complex control setups. Both approaches are challenging and often fail to fully reproduce the nuances of natural animal motion, motivating data-driven alternatives. We present an automatic human-to-quadruped puppeteering framework that produces plausible and controllable quadruped motions from ordinary human motion data. Our approach employs a two-stage generative diffusion model trained purely on quadruped motion data. By introducing a structured conditioning and inpainting strategy, our method supports a wide range of actions, including walking, running, jumping, sitting, and lying. Furthermore, we enable fine-grained intuitive control of the quadruped motion such as head movement control and individual limb puppeteering. Experimental results demonstrate improved motion realism and controllability compared to existing retargeting approaches, highlighting the effectiveness of our framework as a tool for animation and virtual production applications.
