# Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction

arXiv: https://arxiv.org/abs/1703.04727 · HTML: https://mlsanity.com/paper/1703.04727/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2017.

## Built on by (most cited first, 11 of 11)

- [A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings](https://mlsanity.com/paper/2307.05158/) (2023)
- [Multimodal Across Domains Gaze Target Detection](https://mlsanity.com/paper/2208.10822/) (2022)
- [LatentGaze: Cross-Domain Gaze Estimation through Gaze-Aware Analytic Latent Code Manipulation](https://mlsanity.com/paper/2209.10171/) (2022)
- [Multi-Modal Gaze Following in Conversational Scenarios](https://mlsanity.com/paper/2311.05669/) (2023)
- [HAZE-Net: High-Frequency Attentive Super-Resolved Gaze Estimation in Low-Resolution Face Images](https://mlsanity.com/paper/2209.10167/) (2022)
- [Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Following](https://mlsanity.com/paper/2406.02774/) (2024)
- [FR-Net:A Light-weight FFT Residual Net For Gaze Estimation](https://mlsanity.com/paper/2305.11875/) (2023)
- [Gaze Beyond the Frame: Forecasting Egocentric 3D Visual Span](https://mlsanity.com/paper/2511.18470/) (2025)
- [LAEO-Net++: revisiting people Looking At Each Other in videos](https://mlsanity.com/paper/2101.02136/) (2021)
- [Fixation-based Self-calibration for Eye Tracking in VR Headsets](https://mlsanity.com/paper/2311.00391/) (2023)
- [OmniGF: A Dual-Branch Vision-Language Framework for Unified Gaze Following](https://mlsanity.com/paper/2605.26399/) (2026)

## Abstract (arXiv)

The visual focus of attention (VFOA) has been recognized as a prominent conversational cue. We are interested in estimating and tracking the VFOAs associated with multi-party social interactions. We note that in this type of situations the participants either look at each other or at an object of interest; therefore their eyes are not always visible. Consequently both gaze and VFOA estimation cannot be based on eye detection and tracking. We propose a method that exploits the correlation between eye gaze and head movements. Both VFOA and gaze are modeled as latent variables in a Bayesian switching state-space model. The proposed formulation leads to a tractable learning procedure and to an efficient algorithm that simultaneously tracks gaze and visual focus. The method is tested and benchmarked using two publicly available datasets that contain typical multi-party human-robot and human-human interactions.