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​Improving Gaze Reconstruction Accuracy in Generated Faces
This project investigated the effects of adding multiple loss terms to the optimization functions of a face swapping model. We found that both an image reconstruction metric based on the eyes and a metric using difference in gaze angles derived by a pretrained expert model both increased the accuracy of gaze representation in generated faces.
Funding source(s):
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NIH R21 “Protecting the privacy of the child through facial identity removal in recorded behavioral observation sessions” (2020-2022)
Publications
Introducing Explicit Gaze Constraints to Face Swapping
Wilson, Ethan and Shic, Frederick and Jain, Eakta. Introducing Explicit Gaze Constraints to Face Swapping. ACM Symposium on Eye Tracking Research & Applications (ETRA). (2023) (in press)
Resources:
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Paper (coming soon)
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Bibtex:
@inproceedings{wilson_gazeconstraints_2023,
title={Introducing Explicit Gaze Constraints to Face Swapping},
author={Wilson, Ethan and Shic, Frederick and Jain, Eakta},
booktitle={2023 Symposium on Eye Tracking Research and Applications},
year={2023}
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