EgoAVU: Egocentric Audio-Visual Understanding

Understanding egocentric videos plays a vital role for embodied intelligence. Recent multi-modal large language models (MLLMs) can accept both visual and audio inputs. However, due to the challenge of obtaining text labels with coherent joint modality information, whether MLLMs can jointly understand both modalities in egocentric videos remains under-explored. To address this problem, we introduce EgoAVU, a scalable data engine to automatically generate egocentric audio-visual narrations, questions and answers. EgoAVU enriches human narrations with multimodal context and generates audio-visual narrations through cross-modal correlation modeling. Token-based video filtering and modular, graph based curation ensure both the data diversity and quality. Leveraging EgoAVU, we construct EgoAVU-Instruct — a large scale training dataset of 3M samples, and EgoAVU-Bench — a manually verified evaluation split of 3K samples. EgoAVU-Bench clearly reveals the limitation of existing MLLMs: they bias heavily towards visual signals, often neglecting audio cues or failing to correspond audio with the visual source. Finetuning MLLMs on EgoAVU-Instruct effectively solves this issue, enabling up to 113% performance improvement on EgoAVU-Bench. Such benefit can also transfer to other benchmarks such as EgoTempo and EgoIllusion, achieving up to 28% relative performance gain. Code will be released to the community.