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CrossGaze: A Strong Method for 3D Gaze Estimation in the Wild

2024-02-13 09:20:26
Andy Cătrună, Adrian Cosma, Emilian Rădoi

Abstract

Gaze estimation, the task of predicting where an individual is looking, is a critical task with direct applications in areas such as human-computer interaction and virtual reality. Estimating the direction of looking in unconstrained environments is difficult, due to the many factors that can obscure the face and eye regions. In this work we propose CrossGaze, a strong baseline for gaze estimation, that leverages recent developments in computer vision architectures and attention-based modules. Unlike previous approaches, our method does not require a specialised architecture, utilizing already established models that we integrate in our architecture and adapt for the task of 3D gaze estimation. This approach allows for seamless updates to the architecture as any module can be replaced with more powerful feature extractors. On the Gaze360 benchmark, our model surpasses several state-of-the-art methods, achieving a mean angular error of 9.94 degrees. Our proposed model serves as a strong foundation for future research and development in gaze estimation, paving the way for practical and accurate gaze prediction in real-world scenarios.

Abstract (translated)

凝视估计,预测个人正在看向的位置是一个关键任务,在领域如人机交互和虚拟现实中有直接应用。在约束环境中估计眼神方向很难,因为有许多因素会遮挡面部和眼睛区域。在这项工作中,我们提出了CrossGaze,一个强大的凝视估计基线,它利用了计算机视觉架构和基于注意力的模块的最近发展。与之前的方法不同,我们的方法不需要专门的架构,而是利用我们架构中已有的模型,并针对3D凝视估计进行适应。这种方法允许随着任何模块的更强大的特征提取器进行无缝更新。在Gaze360基准上,我们的模型超越了几个最先进的方法,实现了9.94度的平均角误差。我们提出的模型为未来凝视估计的研究和开发奠定了坚实的基础,为现实世界场景中的实际和准确凝视预测铺平了道路。

URL

https://arxiv.org/abs/2402.08316

PDF

https://arxiv.org/pdf/2402.08316.pdf


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