Search2Motion: Training-Free Object-Level Motion Control via Attention-Consensus Search

• URL: http://arxiv.org/abs/2603.16711v2
• Date: Wed, 18 Mar 2026 17:58:04 GMT
• Title: Search2Motion: Training-Free Object-Level Motion Control via Attention-Consensus Search
• Abstract summary: We present Search2Motion, a training-free framework for object-level motion editing in image-to-video generation.<n>Unlike prior methods requiring trajectories, bounding boxes, masks, or motion fields, Search2Motion adopts target-frame-based control.<n>We show that Search2Motion consistently outperforms baselines on FLF2V-obj and VBench.
• Score: 11.772064059088821
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Search2Motion, a training-free framework for object-level motion editing in image-to-video generation. Unlike prior methods requiring trajectories, bounding boxes, masks, or motion fields, Search2Motion adopts target-frame-based control, leveraging first-last-frame motion priors to realize object relocation while preserving scene stability without fine-tuning. Reliable target-frame construction is achieved through semantic-guided object insertion and robust background inpainting. We further show that early-step self-attention maps predict object and camera dynamics, offering interpretable user feedback and motivating ACE-Seed (Attention Consensus for Early-step Seed selection), a lightweight search strategy that improves motion fidelity without look-ahead sampling or external evaluators. Noting that existing benchmarks conflate object and camera motion, we introduce S2M-DAVIS and S2M-OMB for stable-camera, object-only evaluation, alongside FLF2V-obj metrics that isolate object artifacts without requiring ground-truth trajectories. Search2Motion consistently outperforms baselines on FLF2V-obj and VBench.

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