EgoActionBench: Benchmarking Dense Action Captioning for Egocentric Human Videos

We present EgoAction, a unified benchmark and model family that directly infers and evaluates all key dense actions in first-person video, including semantic interactions, hand assignments, and temporal boundaries, from continuous egocentric clips. This approach is a step forward in egocentric video understanding, where models and metrics have typically been constrained to recognizing coarse activities or generating holistic summaries that conflate distinct failure modes. The benchmark, EgoActionBench, is highly structured and diagnostic, representing actions as parallel hand tracks of atomic events, and effectively disentangling discovery, recognition, localization, and laterality errors that traditional caption-similarity metrics obscure. The EgoAction model family achieves significant performance gains in multiple dense action tasks, including semantic F1, atomic-action recall, and precise temporal boundary localization. We also show that adapting standard vision-language models, such as Qwen3-VL and LLaVA-OneVision-2, with our curated dense-action corpus significantly enhances their structured-output capabilities, raising semantic recall to over 20% on human-annotated sets and reducing median boundary error by 66% on held-out sets.