EatVid-Bench: A Multimodal Fine-Grained Eating Behavior Video Dataset

Understanding fine-grained eating behaviors in unconstraineddaily-life videos is essential for dietary monitoring and behavioral healthassessment, yet existing video-language benchmarks lack the temporalgranularity and domain-specific grounding required to rigorously evalu-ate models in this health-critical domain. We introduce EatVid-Bench,a large-scale, multi-dimensional benchmark designed to advance fine-grained eating behavior understanding. Our dataset comprises nearly700 real-world eating sessions (around 3,000 minutes) annotated througha novel three-tier automated pipeline that integrates 12 complementarysignals, including body pose, food detection, bite events, and facial ex-pressions, with each annotation traceable to its source signal for fullinterpretability and verifiability. Building on this foundation, we con-struct a question-answering benchmark spanning seven capability di-mensions, three difficulty levels, and five question types, with rigorousprovenance tracking throughout. Comprehensive evaluation of state-of-the-art Video-LLMs exposes a substantial perception–reasoning gap, par-ticularly in sub-second temporal grounding and cross-frame counting. Tofurther validate the effectiveness of EatVid-Bench, we propose a domain-adapted fine-tuning strategy to provide a strong open-source baselineby leveraging structured training annotations as explicit reasoning su-pervision. Code and benchmark are publicly available at GitHub andHuggingFace.