Do You See What I Am Pointing At? Gesture-Based Egocentric Video Question Answering

Understanding and answering questions based on a user's pointing gesture is essential for next-generation egocentric AI assistants. However, current Multimodal Large Language Models (MLLMs) struggle with such tasks due to the lack of gesture-rich data and their limited ability to perform fine-grained spatial reasoning from egocentric video. To address this, we introduce EgoPointVQA, a dataset and benchmark for gesture-grounded egocentric question answering, comprising 4,000 synthetic and 400 real-world videos across multiple deictic reasoning tasks. Built upon it, we further propose Hand Intent Tokens (HINT), which encode tokens derived from 3D hand keypoints using an off-the-shelf reconstruction model and interleave them with the model input to provide explicit spatial and temporal context for interpreting pointing intent. We show that our model outperforms others across different backbones and model sizes. In particular, HINT-14B achieves 68.1% accuracy on average over 6 tasks, surpassing the state-of-the-art InternVL3-14B by 8.6%.