# Privacy-Preserving Video Conferencing via Thermal-Generative Images

arXiv: https://arxiv.org/abs/2303.09279 · HTML: https://mlsanity.com/paper/2303.09279/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2023.

## Built on by (most cited first, 2 of 2)

- ["Reading Between the Heat": Co-Teaching Body Thermal Signatures for Non-intrusive Stress Detection](https://mlsanity.com/paper/2310.09932/) (2023)
- [WiFi2Cap: Semantic Action Captioning from Wi-Fi CSI via Limb-Level Semantic Alignment](https://mlsanity.com/paper/2603.22690/) (2026)

## Abstract (arXiv)

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 (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.