做3D动作生成或编辑的研究者可以关注，它解决了现有模型只关注时间编辑而忽略关节级修改的问题，直接提升编辑精度和语义一致性。
本文提出一种新架构用于文本驱动的3D人体动作编辑，核心创新在于不仅关注编辑发生的时间点，还识别哪些关节需要修改。架构包含两个轴锚定Transformer，分别提取关节和时间维度的特征，并通过跨轴融合块整合。辅助任务让关节锚定Transformer回归源与目标关节旋转的Soft-DTW距离，学习哪些关节应修改或保留。在MotionFix数据集上达到最先进效果，显著提升语义对齐和动作保真度。
Cross-Axis Feature Fusion with Joint-Wise Motion Difference Prediction for Text-Based 3D Human Motion Editing
We address text-based 3D human motion editing, where the goal is to preserve the style and structure of a source motion while applying edits described in natural language. The release of the MotionFix dataset has spurred active research into training-based diffusion models that directly generate an edited motion from a source motion and a text instruction. While previous works have focused primarily on learning when an edit should occur temporally, our goal is to create a model that understands not only this temporal aspect but also which specific joints are responsible for the change. Targeting this, we propose a novel architecture and a complementary auxiliary task to aid its training. Our architecture consists of two axis-anchored transformers, which extract distinct features along the joint and time dimensions respectively, and a cross-axis fusion block that integrates these representations. We further introduce an auxiliary task that trains the joint-anchored transformer to regress the Soft-DTW distance between source and target joint rotations. This objective teaches the module to understand which joints to modify and which to preserve. Through comprehensive experiments on the MotionFix dataset, we demonstrate that our method significantly improves semantic alignment with both the text instruction and the source motion, as well as the overall fidelity of the generated motion, achieving state-of-the-art results.