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arXiv · 2610.04529

TAME:Topology-Aware Text-Driven Motion Editing across Heterogeneous Humanoid Skeletons

Qichen Zheng·Siyuan Yang·Chong Wang·Jun Liu·Shijian Lu·Alex Kot·Kwok-Yan Lam
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Abstract

Text-driven motion editing modifies an existing motion sequence according to a text instruction while preserving the content of the source motion. Existing methods are typically built for a single, fixed skeletal topology, which limits their use in animation pipelines where characters differ in joint count and skeletal hierarchy. We present Topology-Aware Motion Editor (TAME), a flow-matching transformer that edits motions on humanoid skeletons of varying topology. TAME represents motion as per-joint, per-frame tokens and models interactions among joints, across frames, and with the text instruction through skeletal, temporal, and text cross-attention layers. To make the skeletal attention follow each character's hierarchy, TAME replaces full joint attention with Topology-Constrained Skeletal Propagation (TCSP), which restricts attention to one-hop kinematic neighbors in the skeleton's adjacency matrix. We further introduce Edit-Focused Representation Alignment (EFRA), a self-distilled representation alignment strategy that aligns student features with cleaner EMA-teacher features exclusively on edit-relevant joint-time tokens, making edits faithful to the instruction. To make this setting trainable and comparable, we construct TopoMotionFix, a multi-topology extension of MotionFix with seen- and unseen-topology evaluation protocols. TAME outperforms previous methods in edit alignment and source preservation on MotionFix and reliably edits motions on unseen skeletons in TopoMotionFix.

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Qichen Zheng, Siyuan Yang, Chong Wang, Jun Liu, Shijian Lu, Alex Kot, Kwok-Yan Lam. 2026-10-03. TAME:Topology-Aware Text-Driven Motion Editing across Heterogeneous Humanoid Skeletons. https://arxiv.org/abs/2610.04529

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