MotionChain: Fine-Grained Video Motion Understanding via Structured Decomposition

Recent Vision-Language Models (VLMs) have demonstrated significant progress in video understanding. However, we notice that VLMs perform coarse perception and reasoning when answering finegrained motion questions. We investigate the reasons for this phenomenon and discover that motions in video change rapidly, so fixed frame rate sampling easily misses inter-frame motion details. Moreover, when answering motion-centric questions, VLMs tend to introduce motion-irrelevant contextual information and lack a structured and precise representation of motions, making it difficult to distinguish what motion the subject performs and when. To address these problems, we propose MotionChain, a training-free method that decouples motion question answering into a sequence of ⟨time range, subject, motion, environment⟩ tuples. Concretely, MotionChain leverages optical flow variations to calibrate when motions occur and extracts motion-relevant key frames as supplementary input, effectively alleviating the problem of missing critical motion information. For fine-grained motion question answering (QA), each tuple element in MotionChain can be converted into a visually grounded claim, providing the model with visual evidence. Experiments on motion-centric video QA benchmarks demonstrate the superiority of our MotionChain.