InterEdit: Navigating Text-Guided Multi-Human 3D Motion Editing

• URL: http://arxiv.org/abs/2603.13082v1
• Date: Fri, 13 Mar 2026 15:30:51 GMT
• Title: InterEdit: Navigating Text-Guided Multi-Human 3D Motion Editing
• Abstract summary: We introduce the task of multi-person 3D motion editing, where a target motion is generated from a source and a text instruction.<n>To support this, we propose InterEdit3D, a new dataset with manual two-person motion change annotations, and a Text-guided Multi-human Motion Editing (TMME) benchmark.<n>InterEdit improves text-to-motion consistency and edit fidelity, achieving state-of-the-art TMME performance.
• Score: 73.51964472028392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-guided 3D motion editing has seen success in single-person scenarios, but its extension to multi-person settings is less explored due to limited paired data and the complexity of inter-person interactions. We introduce the task of multi-person 3D motion editing, where a target motion is generated from a source and a text instruction. To support this, we propose InterEdit3D, a new dataset with manual two-person motion change annotations, and a Text-guided Multi-human Motion Editing (TMME) benchmark. We present InterEdit, a synchronized classifier-free conditional diffusion model for TMME. It introduces Semantic-Aware Plan Token Alignment with learnable tokens to capture high-level interaction cues and an Interaction-Aware Frequency Token Alignment strategy using DCT and energy pooling to model periodic motion dynamics. Experiments show that InterEdit improves text-to-motion consistency and edit fidelity, achieving state-of-the-art TMME performance. The dataset and code will be released at https://github.com/YNG916/InterEdit.

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