AA: A Multi-view Multimodal Dataset for Screen-based Gaze Estimation

• URL: http://arxiv.org/abs/2606.31211v1
• Date: Tue, 30 Jun 2026 06:47:09 GMT
• Title: AA: A Multi-view Multimodal Dataset for Screen-based Gaze Estimation
• Abstract summary: We present AA, a multi-view multimodal dataset for screen-based gaze estimation.<n>The dataset captures synchronized facial observations from eight fixed screen-mounted cameras and two additional side-view cameras.<n>Each sample contains multi-view face observations together with structured facial region crops, enabling multimodal learning from both global and local visual cues.
• Score: 32.96367255100354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• MultiView-Bench: A Diagnostic Benchmark for World-Centric Multi-View Integration in VLMs [40.317798580235]
  Recent benchmarks for VLMs largely assess single- or limited-view perception, leaving untested the core cognitive ability to integrate observations across viewpoints into a coherent, world-centric (allocentric) 3D mental model.<n>We introduce MultiView-Bench, a diagnostic benchmark expressly designed to evaluate multi-view integration for holistic 3D scene comprehension.<n>We propose ViewNavigator, a multi-agent framework that actively selects informative viewpoints, perceives, and fuses multi-view evidence, improving diverse base models on MultiView-Bench even under a strict budget-matched comparison.
  arXiv  Detail & Related papers  (2026-07-09T22:22:42Z)
• PicoEyes: Unified Gaze Estimation Framework for Mixed Reality with a Large-Scale Multi-View Dataset [1.7596293300635233]
  PicoEyes is a unified gaze estimation framework that directly predicts all key attributes of gaze.<n>It simultaneously addresses calibration, gaze forecasting, and varying device postures.<n>It supports 3D eye reconstruction via joint estimation of eye parameters and depth maps in an end-to-end manner.
  arXiv  Detail & Related papers  (2026-05-08T03:34:09Z)
• Unsupervised Multi-View Visual Anomaly Detection via Progressive Homography-Guided Alignment [14.782512101141016]
  Unsupervised visual anomaly detection from multi-view images presents a significant challenge.<n>ViewSense-AD (VSAD) learns viewpoint-invariant representations by explicitly modeling geometric consistency across views.<n>Anomaly detection is performed by comparing multi-level features from the diffusion model against a learned memory bank of normal prototypes.
  arXiv  Detail & Related papers  (2025-11-24T05:01:16Z)
• Multi-LVI-SAM: A Robust LiDAR-Visual-Inertial Odometry for Multiple Fisheye Cameras [24.95534498798919]
  We propose a multi-camera LiDAR-visual-inertial odometry framework, Multi-LVI-SAM, which fuses data from multiple fisheye cameras, LiDAR and inertial sensors for highly accurate and robust state estimation.<n>To enable efficient and consistent integration of visual information from multiple fisheye cameras, we introduce a panoramic visual feature model that unifies multi-camera observations into a single representation.
  arXiv  Detail & Related papers  (2025-09-06T15:06:55Z)
• Multi-view Gaze Target Estimation [42.72151752119855]
  This paper presents a method that utilizes multiple camera views for the gaze target estimation (GTE) task.<n>The approach integrates information from different camera views to improve accuracy and expand applicability.<n>The paper introduces a multi-view dataset for developing and evaluating multi-view GTE methods.
  arXiv  Detail & Related papers  (2025-08-07T21:15:15Z)
• Balanced Multi-view Clustering [56.17836963920012]
  Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures.<n>The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information.<n>We propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view.
  arXiv  Detail & Related papers  (2025-01-05T14:42:47Z)
• Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval [85.73149096516543]
  We address the choice of viewpoint during sketch creation in Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) A pilot study highlights the system's struggle when query-sketches differ in viewpoint from target instances. To reconcile this, we advocate for a view-aware system, seamlessly accommodating both view-agnostic and view-specific tasks.
  arXiv  Detail & Related papers  (2024-07-01T21:20:44Z)
• Multi-Spectral Image Stitching via Spatial Graph Reasoning [52.27796682972484]
  We propose a spatial graph reasoning based multi-spectral image stitching method. We embed multi-scale complementary features from the same view position into a set of nodes. By introducing long-range coherence along spatial and channel dimensions, the complementarity of pixel relations and channel interdependencies aids in the reconstruction of aligned multi-view features.
  arXiv  Detail & Related papers  (2023-07-31T15:04:52Z)
• Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes [51.20150148066458]
  We propose a novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the generalizationally crafted masks. Experiments on real-world datasets prove the significant effectiveness and ability of the proposed method.
  arXiv  Detail & Related papers  (2023-04-18T13:55:24Z)
• Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment [50.82982601256481]
  We propose a Cross-view Partial Sample and Prototype Alignment Network (CPSPAN) for Deep Incomplete Multi-view Clustering. Unlike existing contrastive-based methods, we adopt pair-observed data alignment as 'proxy supervised signals' to guide instance-to-instance correspondence construction.
  arXiv  Detail & Related papers  (2023-03-28T02:31:57Z)
• Generative Partial Multi-View Clustering [133.36721417531734]
  We propose a generative partial multi-view clustering model, named as GP-MVC, to address the incomplete multi-view problem. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the consistent cluster structure across multiple views. Second, view-specific generative adversarial networks are developed to generate the missing data of one view conditioning on the shared representation given by other views.
  arXiv  Detail & Related papers  (2020-03-29T17:48:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.