Privacy-Preserving Video Conferencing via Thermal-Generative Images
Sheng–Yang Chiu, Yu–Ting Huang, Chieh–Ting Lin, Yu–Chee Tseng, Jen–Jee Chen, Meng–Hsuan Tu, Bo–Chen Tung, YuJou Nieh
Abstract
Due to the COVID-19 epidemic, video conferencing has evolved as a new paradigm of communication and teamwork. However, private and personal information can be easily leaked through cameras during video conferencing. This includes leakage of a person's appearance as well as the contents in the background. This paper proposes a novel way of using online low-resolution thermal images as conditions to guide the synthesis of RGB images, bringing a promising solution for real-time video conferencing when privacy leakage is a concern. SPADE-SR [1] (Spatially-Adaptive De-normalization with Self Resampling), a variant of SPADE, is adopted to incorporate the spatial property of a thermal heatmap and the non-thermal property of a normal, privacy-free pre-recorded RGB image provided in a form of latent code. We create a PAIR-LRT-Human (LRT = Low-Resolution Thermal) dataset to validate our claims. The result enables a convenient way of video conferencing where users no longer need to groom themselves and tidy up backgrounds for a short meeting. Additionally, it allows a user to switch to a different appearance and background during a conference.
BibTeX
@inproceedings{icra2023_privacypreservin,
title = {Privacy-Preserving Video Conferencing via Thermal-Generative Images},
author = {Sheng–Yang Chiu and Yu–Ting Huang and Chieh–Ting Lin and Yu–Chee Tseng and Jen–Jee Chen and Meng–Hsuan Tu and Bo–Chen Tung and YuJou Nieh},
booktitle = {ICRA 2023},
year = {2023}
}