# PartMotionEdit: Fine-Grained Text-Driven 3D Human Motion Editing via Part-Level Modulation

arXiv: https://arxiv.org/abs/2512.24200 · HTML: https://mlsanity.com/paper/2512.24200/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2025.

## Built on by (most cited first, 5 of 5)

- [UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors](https://mlsanity.com/paper/2603.15975/) (2026)
- [A Unified Conditional Flow for Motion Generation, Editing, and Intra-Structural Retargeting](https://mlsanity.com/paper/2604.13427/) (2026)
- [UniMoFlow: Grounding Instruction-Driven 3D Human Motion Editing in Generation](https://mlsanity.com/paper/2608.09143/) (2026)
- [SuperMotion: Source-Preserving Denoising for Text-Driven Human Motion Editing](https://mlsanity.com/paper/2610.01517/) (2026)
- [TAME:Topology-Aware Text-Driven Motion Editing across Heterogeneous Humanoid Skeletons](https://mlsanity.com/paper/2610.04529/) (2026)

## Abstract (arXiv)

Existing text-driven 3D human motion editing methods have demonstrated significant progress, but are still difficult to precisely control over detailed, part-specific motions due to their global modeling nature. In this paper, we propose PartMotionEdit, a novel fine-grained motion editing framework that operates via part-level semantic modulation. The core of PartMotionEdit is a Part-aware Motion Modulation (PMM) module, which builds upon a predefined five-part body decomposition. PMM dynamically predicts time-varying modulation weights for each body part, enabling precise and interpretable editing of local motions. To guide the training of PMM, we also introduce a part-level similarity curve supervision mechanism enhanced with dual-layer normalization. This mechanism assists PMM in learning semantically consistent and editable distributions across all body parts. Furthermore, we design a Bidirectional Motion Interaction (BMI) module. It leverages bidirectional cross-modal attention to achieve more accurate semantic alignment between textual instructions and motion semantics. Extensive quantitative and qualitative evaluations on a well-known benchmark demonstrate that PartMotionEdit outperforms the state-of-the-art methods.