基于文本的三维人体运动编辑中的跨轴特征融合与关节运动差异预测
Cross-Axis Feature Fusion with Joint-Wise Motion Difference Prediction for Text-Based 3D Human Motion Editing
AI总结：
提出一种跨轴特征融合架构和辅助任务，通过联合锚定变换器预测关节运动差异，实现文本驱动的三维人体运动编辑，在MotionFix数据集上达到最优性能。
摘要：
我们研究基于文本的三维人体运动编辑，目标是保留源运动的风格和结构，同时应用自然语言描述的编辑。MotionFix数据集的发布推动了基于训练扩散模型的直接生成编辑运动的研究，这些模型从源运动和文本指令生成编辑运动。虽然先前的工作主要关注学习编辑在时间上何时发生，但我们的目标是创建一个不仅理解时间方面，还理解哪些特定关节负责变化的模型。为此，我们提出了一种新颖的架构和一个互补的辅助任务来辅助其训练。我们的架构由两个轴锚定变换器组成，分别沿关节和时间维度提取不同特征，以及一个跨轴融合块来整合这些表示。我们进一步引入一个辅助任务，训练关节锚定变换器回归源和目标关节旋转之间的Soft-DTW距离。该目标教会模块理解哪些关节需要修改，哪些需要保留。通过在MotionFix数据集上的全面实验，我们证明我们的方法显著提高了与文本指令和源运动的语义对齐，以及生成运动的整体保真度，达到了最先进的结果。
英文摘要：
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.
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。
