论文标题
通过合成图像解决的目光估计问题
Gaze estimation problem tackled through synthetic images
论文作者
论文摘要
在本文中，我们评估了一个合成框架，该框架采用深度学习技术的目光估算领域。缺乏足够的注释数据可以通过使用综合评估框架与实际情况的行为相似。在这项工作中，我们使用使用i2head DatataSet的U2EYES合成环境作为基于替代培训和测试策略进行比较的真正基准。获得的结果表明，这两个框架之间的平均行为可比，尽管合成图像可以检索出明显更健壮和稳定的性能。此外，在用户的特定校准策略中应用合成预审慎的模型的潜力显示出出色的性能。
In this paper, we evaluate a synthetic framework to be used in the field of gaze estimation employing deep learning techniques. The lack of sufficient annotated data could be overcome by the utilization of a synthetic evaluation framework as far as it resembles the behavior of a real scenario. In this work, we use U2Eyes synthetic environment employing I2Head datataset as real benchmark for comparison based on alternative training and testing strategies. The results obtained show comparable average behavior between both frameworks although significantly more robust and stable performance is retrieved by the synthetic images. Additionally, the potential of synthetically pretrained models in order to be applied in user's specific calibration strategies is shown with outstanding performances.
