mlsanityData to 14 Oct 2026

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

arXiv 2603.13082 ↗First posted 13 Mar 2026cs.CV cs.RO eess.IVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 50 of its 57 references that are in the map. First posted on arXiv 13 Mar 2026.

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References · 50

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38 more
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  2. 2024InterDreamer: Zero-Shot Text to 3D Dynamic Human-Object Interaction
  3. 2025Dynamic Motion Blending for Versatile Motion Editing
  4. 2026UniMoFlow: Grounding Instruction-Driven 3D Human Motion Editing in Generation
  5. 2025HY-Motion 1.0: Scaling Flow Matching Models for Text-To-Motion Generation
  6. 2024Two-in-One: Unified Multi-Person Interactive Motion Generation by Latent Diffusion Transformer
  7. 2026MotionMERGE: A Multi-granular Framework for Human Motion Editing, Reasoning, Generation, and Explanation
  8. 2024Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation