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Self-Learning Transformations for Improving Gaze and Head Redirection
Yufeng Zheng · Seonwook Park · Xucong Zhang · Shalini De Mello · Otmar Hilliges

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #306

Many computer vision tasks rely on labeled data. Rapid progress in generative modeling has led to the ability to synthesize photorealistic images. However, controlling specific aspects of the generation process such that the data can be used for supervision of downstream tasks remains challenging. In this paper we propose a novel generative model for images of faces, that is capable of producing high-quality images under fine-grained control over eye gaze and head orientation angles. This requires the disentangling of many appearance related factors including gaze and head orientation but also lighting, hue etc. We propose a novel architecture which learns to discover, disentangle and encode these extraneous variations in a self-learned manner. We further show that explicitly disentangling task-irrelevant factors results in more accurate modelling of gaze and head orientation. A novel evaluation scheme shows that our method improves upon the state-of-the-art in redirection accuracy and disentanglement between gaze direction and head orientation changes. Furthermore, we show that in the presence of limited amounts of real-world training data, our method allows for improvements in the downstream task of semi-supervised cross-dataset gaze estimation. Please check our project page at: https://ait.ethz.ch/projects/2020/STED-gaze/

Author Information

Yufeng Zheng (ETH Zurich)
Seonwook Park (ETH Zurich)
Xucong Zhang (ETH Zurich)
Shalini De Mello (NVIDIA)
Shalini De Mello

Shalini De Mello is a Principal Research Scientist and Research Lead in the Learning and Perception Research group at NVIDIA, which she joined in 2013. Her research interests are in human-centric vision (face and gaze analysis) and in data-efficient (synth2real, low-shot, self-supervised and multimodal) machine learning. She has co-authored 48 peer-reviewed publications and holds 38 patents. Her inventions have contributed to several NVIDIA products, including DriveIX and Maxine. Previously, she has worked at Texas Instruments and AT&T Laboratories. She received her Doctoral degree in Electrical and Computer Engineering from the University of Texas at Austin.

Otmar Hilliges (ETH Zurich)

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