3D Gaze Tracking for Studying Collaborative Interactions in Mixed-Reality Environments

• URL: http://arxiv.org/abs/2406.11003v1
• Date: Sun, 16 Jun 2024 16:30:56 GMT
• Title: 3D Gaze Tracking for Studying Collaborative Interactions in Mixed-Reality Environments
• Abstract summary: This study presents a novel framework for 3D gaze tracking tailored for mixed-reality settings. Our proposed framework leverages state-of-the-art computer vision and machine learning techniques to overcome obstacles.
• Score: 3.8075244788223044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study presents a novel framework for 3D gaze tracking tailored for mixed-reality settings, aimed at enhancing joint attention and collaborative efforts in team-based scenarios. Conventional gaze tracking, often limited by monocular cameras and traditional eye-tracking apparatus, struggles with simultaneous data synchronization and analysis from multiple participants in group contexts. Our proposed framework leverages state-of-the-art computer vision and machine learning techniques to overcome these obstacles, enabling precise 3D gaze estimation without dependence on specialized hardware or complex data fusion. Utilizing facial recognition and deep learning, the framework achieves real-time, tracking of gaze patterns across several individuals, addressing common depth estimation errors, and ensuring spatial and identity consistency within the dataset. Empirical results demonstrate the accuracy and reliability of our method in group environments. This provides mechanisms for significant advances in behavior and interaction analysis in educational and professional training applications in dynamic and unstructured environments.

Related papers

• From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning [3.402787708517185]
  Most collaborative learning research focuses on Euclidean data with regular, grid-like structure.<n>Learning on graphs relies on message-passing mechanisms to propagate information between connected nodes.<n>This survey provides a comprehensive investigation of collaborative learning from Euclidean to graph-structured data.
  arXiv  Detail & Related papers  (2026-09-02T13:49:46Z)
• CoMind: Understanding Collaborative Human Activity from Multiple Minds and Views [82.15362247351763]
  We introduce a novel egocentric and exocentric video dataset capturing real-world collaboration in cooking scenarios.<n>The dataset integrates multi-perspective video, high-quality audio, gaze tracking, and 3D scene and object scans, with annotations for shared attention to objects.<n>We establish benchmarks for Joint Attention Estimation, Socially Conditioned Object Interaction Anticipation, and Collaborative Handover Prediction.
  arXiv  Detail & Related papers  (2026-07-07T18:08:45Z)
• SpaceEra++: A Unified Framework Towards 3D Spatial Reasoning in Video [88.79625979053873]
  We propose a novel framework, SpaceEra, for pre-trained vision-language models (VLMs)<n>We extend the original framework into a comprehensive system, termed SpaceEra++, which spans data construction, model design, training optimization, and prompting inference.<n>In addition, to enhance spatial reasoning, we develop SpaceAlign, which enforces pairwise object constraints by jointly exploiting absolute coordinates and relative spatial relations.
  arXiv  Detail & Related papers  (2026-07-02T06:56:29Z)
• Online Segment Any 3D Thing as Instance Tracking [60.20416622842975]
  We reconceptualize online 3D segmentation as an instance tracking problem (AutoSeg3D)<n>We introduce spatial consistency learning to mitigate the fragmentation problem inherent in Vision Foundation Models.<n>Our method establishes a new state-of-the-art, surpassing ESAM by 2.8 AP on ScanNet200.
  arXiv  Detail & Related papers  (2025-12-08T14:48:51Z)
• CRIA: A Cross-View Interaction and Instance-Adapted Pre-training Framework for Generalizable EEG Representations [52.251569042852815]
  CRIA is an adaptive framework that utilizes variable-length and variable-channel coding to achieve a unified representation of EEG data across different datasets.<n>The model employs a cross-attention mechanism to fuse temporal, spectral, and spatial features effectively.<n> Experimental results on the Temple University EEG corpus and the CHB-MIT dataset show that CRIA outperforms existing methods with the same pre-training conditions.
  arXiv  Detail & Related papers  (2025-06-19T06:31:08Z)
• Spatial Understanding from Videos: Structured Prompts Meet Simulation Data [79.52833996220059]
  We present a unified framework for enhancing 3D spatial reasoning in pre-trained vision-language models without modifying their architecture.<n>This framework combines SpatialMind, a structured prompting strategy that decomposes complex scenes and questions into interpretable reasoning steps, with ScanForgeQA, a scalable question-answering dataset built from diverse 3D simulation scenes.
  arXiv  Detail & Related papers  (2025-06-04T07:36:33Z)
• Multi-person eye tracking for real-world scene perception in social settings [34.82692226532414]
  We apply mobile eye tracking in a real-world multi-person setup and develop a system to stream, record, and analyse synchronised data. Our system achieves precise time synchronisation and accurate gaze projection in challenging dynamic scenes. This advancement enables insight into collaborative behaviour, group dynamics, and social interaction, with high ecological validity.
  arXiv  Detail & Related papers  (2024-07-08T19:33:17Z)
• I-MPN: Inductive Message Passing Network for Efficient Human-in-the-Loop Annotation of Mobile Eye Tracking Data [4.487146086221174]
  We present a novel human-centered learning algorithm designed for automated object recognition within mobile eye-tracking settings. Our approach seamlessly integrates an object detector with a spatial relation-aware inductive message-passing network (I-MPN), harnessing node profile information and capturing object correlations.
  arXiv  Detail & Related papers  (2024-06-10T13:08:31Z)
• Robust Collaborative Perception without External Localization and Clock Devices [52.32342059286222]
  A consistent spatial-temporal coordination across multiple agents is fundamental for collaborative perception. Traditional methods depend on external devices to provide localization and clock signals. We propose a novel approach: aligning by recognizing the inherent geometric patterns within the perceptual data of various agents.
  arXiv  Detail & Related papers  (2024-05-05T15:20:36Z)
• CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning [18.75039816544345]
  We present a novel collaborative stance detection framework called (CoSD) CoSD learns topic-aware semantics and collaborative signals among texts, topics, and stance labels. Experiments on two benchmark datasets demonstrate the state-of-the-art detection performance of CoSD.
  arXiv  Detail & Related papers  (2024-04-26T02:04:05Z)
• Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models [56.257840490146]
  ConCue is a novel approach for improving visual feature extraction in HOI detection. We develop a transformer-based feature extraction module with a multi-tower architecture that integrates contextual cues into both instance and interaction detectors.
  arXiv  Detail & Related papers  (2023-11-26T09:11:32Z)
• Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection [57.13665112065285]
  Human-Object Interaction (HOI) detection is a challenging computer vision task. We present a framework that enhances HOI detection by incorporating structured text knowledge.
  arXiv  Detail & Related papers  (2023-07-25T14:20:52Z)
• Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation [22.63837164001751]
  Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations. To enable collaborative localization, four key challenges must be addressed, including modeling complex relationships between observed objects. We introduce a novel approach that integrates uncertainty-aware graph learning model and state estimation for a team of robots to collaboratively localize objects.
  arXiv  Detail & Related papers  (2021-11-05T22:48:13Z)
• A Variational Information Bottleneck Approach to Multi-Omics Data Integration [98.6475134630792]
  We propose a deep variational information bottleneck (IB) approach for incomplete multi-view observations. Our method applies the IB framework on marginal and joint representations of the observed views to focus on intra-view and inter-view interactions that are relevant for the target. Experiments on real-world datasets show that our method consistently achieves gain from data integration and outperforms state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2021-02-05T06:05:39Z)
• Self-supervised Human Detection and Segmentation via Multi-view Consensus [116.92405645348185]
  We propose a multi-camera framework in which geometric constraints are embedded in the form of multi-view consistency during training. We show that our approach outperforms state-of-the-art self-supervised person detection and segmentation techniques on images that visually depart from those of standard benchmarks.
  arXiv  Detail & Related papers  (2020-12-09T15:47:21Z)
• Benchmarking Unsupervised Object Representations for Video Sequences [111.81492107649889]
  We compare the perceptual abilities of four object-centric approaches: ViMON, OP3, TBA and SCALOR. Our results suggest that the architectures with unconstrained latent representations learn more powerful representations in terms of object detection, segmentation and tracking. Our benchmark may provide fruitful guidance towards learning more robust object-centric video representations.
  arXiv  Detail & Related papers  (2020-06-12T09:37:24Z)
• Taskology: Utilizing Task Relations at Scale [28.09712466727001]
  We show that we can leverage the inherent relationships among collections of tasks, as they are trained jointly. explicitly utilizing the relationships between tasks allows improving their performance while dramatically reducing the need for labeled data. We demonstrate our framework on subsets of the following collection of tasks: depth and normal prediction, semantic segmentation, 3D motion and ego-motion estimation, and object tracking and 3D detection in point clouds.
  arXiv  Detail & Related papers  (2020-05-14T22:53:46Z)

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.