The focus of this paper is 3D motion editing. Given a 3D human motion and a textual description of the desired modification, our goal is to generate an edited motion as described by the text. The challenges include the lack of training data and the design of a model that faithfully edits the source motion. In this paper, we address both these challenges. We build a methodology to semi-automatically collect a dataset of triplets in the form of (i) a source motion, (ii) a target motion, and (iii) an edit text, and create the new MotionFix dataset. Having access to such data allows us to train a conditional diffusion model, TMED, that takes both the source motion and the edit text as input. We further build various baselines trained only on text-motion pairs datasets, and show superior performance of our model trained on triplets. We introduce new retrieval-based metrics for motion editing and establish a new benchmark on the evaluation set of MotionFix. Our results are encouraging, paving the way for further research on finegrained motion generation. Code and models will be made publicly available.

本研究解决了3D动作编辑中的数据不足和源动作忠实编辑模型设计的问题。通过建立一种方法论，半自动收集新的MotionFix数据集，利用条件扩散模型TMED实现基于文本的动作编辑。结果表明，模型在三元组数据上训练的性能优于仅基于文本和动作配对的数据，为精准动作生成的进一步研究奠定了基础。

MotionFix：基于文本的3D人类动作编辑