MotiMotion: Motion-Controlled Video Generation with Visual Reasoning
Summary
MotiMotion introduces a reasoning-then-generation framework for motion-controlled video generation that uses vision-language reasoning to refine trajectories and a confidence-aware control scheme to improve plausibility, outperforming existing approaches on a new benchmark.
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Paper page - MotiMotion: Motion-Controlled Video Generation with Visual Reasoning
Source: https://huggingface.co/papers/2605.22818
Abstract
MotiMotion introduces a reasoning-then-generation framework for motion-controlled video generation that improves plausibility through vision-language reasoning and confidence-aware control mechanisms.
Current motion-controlled image-to-video generation models rigidly follow user-provided trajectories that are often sparse, imprecise, and causally incomplete. Such reliance often yields unnatural or implausible outcomes, especially by missing secondary causal consequences. To address this, we introduce MotiMotion, a novel framework that reformulatesmotion controlas a reasoning-then-generation problem. To encourage causally grounded and commonsense-consistent interactions, we leverage a training-freevision-language reasonerto refineimage-space coordinatesof primary trajectories and to hallucinate plausiblesecondary motions. To further improve motion naturalness, we propose aconfidence-aware controlscheme that modulatesguidance strength, enabling the model to closely follow high-confidence plans while correcting artifacts under low-confidence inputs with its internalgenerative priors. To support systematic evaluation, we curate a newimage-to-video benchmark,MotiBench, consisting of interaction-centric scenes where new events are triggered by motion. Both VLM-based evaluation and a human study onMotiBenchdemonstrate that MotiMotion produces videos with more plausible object behaviors and interaction, and is preferred over existing approaches.
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