MotionSight: Boosting Fine-Grained Motion Understanding in Multimodal LLMs

TL;DR

MotionSight introduces a zero-shot method with visual prompts and a large-scale dataset to significantly enhance fine-grained motion understanding in multimodal large language models without additional training.

Contribution

The paper presents MotionSight, a novel zero-shot approach using object-centric visual prompts and a new dataset for improved fine-grained video motion understanding in MLLMs.

Findings

Achieves state-of-the-art open-source performance.

Competitiveness with commercial models in motion understanding.

Introduces a large-scale dataset with hierarchical annotations.

Abstract

Despite advancements in Multimodal Large Language Models (MLLMs), their proficiency in fine-grained video motion understanding remains critically limited. They often lack inter-frame differencing and tend to average or ignore subtle visual cues. Furthermore, while visual prompting has shown potential in static images, its application to video's temporal complexities, particularly for fine-grained motion understanding, remains largely unexplored. We investigate whether inherent capability can be unlocked and boost MLLMs' motion perception and enable distinct visual signatures tailored to decouple object and camera motion cues. In this study, we introduce MotionSight, a novel zero-shot method pioneering object-centric visual spotlight and motion blur as visual prompts to effectively improve fine-grained motion understanding without training. To convert this into valuable data assets, we curated MotionVid-QA, the first large-scale dataset for fine-grained video motion understanding, with hierarchical annotations including SFT and preference data, {\Theta}(40K) video clips and {\Theta}(87K) QAs. Experiments show MotionSight achieves state-of-the-art open-source performance and competitiveness with commercial models. In particular, for fine-grained motion understanding we present a novel zero-shot technique and a large-scale, high-quality dataset. All the code and annotations will be publicly available.