动态运动混合用于多功能运动编辑
Dynamic Motion Blending for Versatile Motion Editing
AI总结：
本文提出MotionCutMix数据增强与MotionReFit自回归扩散模型，实现从文本指令直接进行空间和时间运动编辑，无需额外规格或大语言模型，达到最先进性能。
摘要：
文本引导的运动编辑能够实现高层级的语义控制以及超越传统关键帧动画的迭代修改。现有方法依赖于有限的预收集训练三元组，这严重阻碍了它们在不同编辑场景中的多功能性。我们引入了MotionCutMix，一种在线数据增强技术，通过基于输入文本混合身体部位运动来动态生成训练三元组。尽管MotionCutMix有效扩展了训练分布，但其组合性质引入了增加的随机性和潜在的身体部位不协调。为了建模如此丰富的分布，我们提出了MotionReFit，一个带有运动协调器的自回归扩散模型。自回归架构通过分解长序列促进学习，而运动协调器则减轻了运动组合的伪影。我们的方法直接从高层级的人类指令处理空间和时间运动编辑，无需依赖额外的规格说明或大型语言模型。通过大量实验，我们展示了MotionReFit在文本引导运动编辑中达到了最先进的性能。
英文摘要：
Text-guided motion editing enables high-level semantic control and iterative modifications beyond traditional keyframe animation. Existing methods rely on limited pre-collected training triplets, which severely hinders their versatility in diverse editing scenarios. We introduce MotionCutMix, an online data augmentation technique that dynamically generates training triplets by blending body part motions based on input text. While MotionCutMix effectively expands the training distribution, the compositional nature introduces increased randomness and potential body part incoordination. To model such a rich distribution, we present MotionReFit, an auto-regressive diffusion model with a motion coordinator. The auto-regressive architecture facilitates learning by decomposing long sequences, while the motion coordinator mitigates the artifacts of motion composition. Our method handles both spatial and temporal motion edits directly from high-level human instructions, without relying on additional specifications or Large Language Models. Through extensive experiments, we show that MotionReFit achieves state-of-the-art performance in text-guided motion editing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
