# Regional Anchor Routing for Multi-Object Motion Transfer

**Authors:** Yuze Li, Fuchen Long, Xinlong Sun, Cheng Yan, Xinyu Zhang, Xi Chen  
**Lab:** Tianjin University · **Published:** 29 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.39478) · [PDF](https://arxiv.org/pdf/2610.39478) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.39478.html)

## Abstract

Compositional motion transfer requires a model to extract motions from multiple reference videos, and apply each motion to a user-specified object in one target scene. Existing methods normally struggle to reliably assign different reference motions to the correct target objects, thus a motion intended for one object may affect another. Meanwhile, some motion-specific tuning approaches are still facing another issue: they must run a new optimization whenever the reference video changes, making repeated transfers inefficient. To address these limitations, we propose RART, a framework that unifies object-wise motion extraction, target-region anchoring, and frame-aligned injection into a frozen video Diffusion Transformer for multi-object motion transfer. For each reference motion, RART extracts temporally ordered features from the masked reference video, and then conditions them on the target first-frame mask to form mask-anchored motion tokens. Frame-aligned motion cross-attention allows each generated frame to read only the motion tokens at the corresponding time slots, composing all object motions in one denoising process. During training, a motion-attention routing loss encourages each target region to attend to its corresponding motion tokens and reduces interference between objects. Therefore, RART can handle novel reference-target combinations without per-video optimization. Experiments show that RART achieves accurate motion preservation, efficient inference, and strong compositional generalization.

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