{"ID":5438671,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T06:43:51.290176615Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31211","arxiv_id":"2606.31211","title":"AA: A Multi-view Multimodal Dataset for Screen-based Gaze Estimation","abstract":"We present AA, a multi-view multimodal dataset for screen-based gaze estimation. The dataset captures synchronized facial observations from eight fixed screen-mounted cameras and two additional side-view cameras, paired with precise screen-space gaze targets collected under controlled fixation conditions. Each sample contains multi-view face observations together with structured facial region crops, enabling multimodal learning from both global and local visual cues. Unlike existing single-view gaze datasets, AA provides multi-view coverage from both screen-mounted and side-mounted perspectives, enabling more robust modeling under viewpoint variation and occlusion. The dataset includes subject-independent evaluation splits and a standardized data processing pipeline to support reproducible research in gaze estimation.","short_abstract":"We present AA, a multi-view multimodal dataset for screen-based gaze estimation. The dataset captures synchronized facial observations from eight fixed screen-mounted cameras and two additional side-view cameras, paired with precise screen-space gaze targets collected under controlled fixation conditions. Each sample c...","url_abs":"https://arxiv.org/abs/2606.31211","url_pdf":"https://arxiv.org/pdf/2606.31211v1","authors":"[\"Chang Liu\",\"Jiaqi Liu\",\"Zhoutong Ye\",\"Xinjie Shen\",\"Chun Yu\",\"Yuanchun Shi\"]","published":"2026-06-30T06:47:09Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.HC\"]","methods":"[]","has_code":false}