Richard Blythman virtually presented the team’s work on creating synthetic thermal image datasets for deep learning at an IEEE conference, Quality of Multimedia Experience (QoMEX 2020). Synthetic data holds promise for reducing the number of real images required to train deep learning algorithms, especially for thermal imaging applications where there are much fewer publicly-available datasets. The team used photogrammetry techniques to reconstruct 3D thermographic models of humans, which were then rendered in various positions and poses to mimic in-cabin monitoring scenarios. Finally, deep learning algorithms for face detection and head pose estimation were trained and evaluated on a combination of synthetic and real data. In future, we hope to build a multi-thermal camera data acquisition setup to improve the data quality.