Purifying Real Images with an Attention-guided Style Transfer Network for Gaze Estimation

TL;DR

This paper introduces an attention-guided style transfer network to purify real images by converting outdoor images into indoor synthetic-like images, enhancing gaze estimation accuracy.

Contribution

It proposes a novel region-level task-guided loss and uses segmentation masks to improve style transfer for image purification in gaze estimation.

Findings

Achieves state-of-the-art results on LPW, COCO, and MPIIGaze datasets.

Effectively converts outdoor real images to indoor synthetic-like images.

Improves gaze estimation performance through image purification.

Abstract

Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distribution of synthetic images compared to real images, the desired performance cannot be achieved. Real images consist of multiple forms of light orientation, while synthetic images consist of a uniform light orientation. These features are considered to be characteristic of outdoor and indoor scenes, respectively. To solve this problem, the previous method learned a model to improve the realism of the synthetic image. Different from the previous methods, this paper try to purify real image by extracting discriminative and robust features to convert outdoor real images to indoor synthetic images. In this paper, we first introduce the segmentation masks to construct RGB-mask pairs as inputs, then we design a attention-guided style transfer network to learn style features separately from the attention and bkgd(background) region , learn content features from full and attention region. Moreover, we propose a novel region-level task-guided loss to restrain the features learnt from style and content. Experiments were performed using mixed studies (qualitative and quantitative) methods to demonstrate the possibility of purifying real images in complex directions. We evaluate the proposed method on three public datasets, including LPW, COCO and MPIIGaze. Extensive experimental results show that the proposed method is effective and achieves the state-of-the-art results.