# GazeProphetV2: Head-Movement-Based Gaze Prediction Enabling Efficient Foveated Rendering on Mobile VR

arXiv: https://arxiv.org/abs/2511.19988 · HTML: https://mlsanity.com/paper/2511.19988/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model](https://mlsanity.com/paper/1611.09571/) (2016)
- [FoV-NeRF: Foveated Neural Radiance Fields for Virtual Reality](https://mlsanity.com/paper/2103.16365/) (2021)
- [Few-Shot Adaptive Gaze Estimation](https://mlsanity.com/paper/1905.01941/) (2019)
- [Deep Visual Attention Prediction](https://mlsanity.com/paper/1705.02544/) (2017)
- [Deep Pictorial Gaze Estimation](https://mlsanity.com/paper/1807.10002/) (2018)
- [DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations](https://mlsanity.com/paper/1510.02927/) (2015)
- [Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition](https://mlsanity.com/paper/1312.7570/) (2013)
- [A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings](https://mlsanity.com/paper/2307.05158/) (2023)
- [Simple vs complex temporal recurrences for video saliency prediction](https://mlsanity.com/paper/1907.01869/) (2019)
- [SalNet360: Saliency Maps for omni-directional images with CNN](https://mlsanity.com/paper/1709.06505/) (2017)

## Abstract (arXiv)

Predicting gaze behavior in virtual reality environments remains a significant challenge with implications for rendering optimization and interface design. This paper introduces a multimodal approach to VR gaze prediction that combines temporal gaze patterns, head movement data, and visual scene information. By leveraging a gated fusion mechanism with cross-modal attention, the approach learns to adaptively weight gaze history, head movement, and scene content based on contextual relevance. Evaluations using a dataset spanning 22 VR scenes with 5.3M gaze samples demonstrate improvements in predictive accuracy when combining modalities compared to using individual data streams alone. The results indicate that integrating past gaze trajectories with head orientation and scene content enhances prediction accuracy across 1-3 future frames. Cross-scene generalization testing shows consistent performance with 93.1% validation accuracy and temporal consistency in predicted gaze trajectories. These findings contribute to understanding attention mechanisms in virtual environments while suggesting potential applications in rendering optimization, interaction design, and user experience evaluation. The approach represents a step toward more efficient virtual reality systems that can anticipate user attention patterns without requiring expensive eye tracking hardware.