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.

This paper introduces a wearable device that aims to facilitate marathon runners to maintain the most decent running pace. We adopt deep learning approach to predict the runner's heart rate. This derived information is combined with existing altitude and road conditions to generate the most suitable pacing suggestion to coach the runner for the best performance. In addition, we have developed a face recognition mechanism to help runners to easily identify and retrieve their photos shot during running in the cloud photo bank with the aid of a personal cell phone instantaneously. Our pilot system has been proven for its feasibility in both the heart rate prediction performance and photo identification rate.