  Advances in low-light video RAW-to-RGB translation are opening up the
possibility of fast low-light imaging on commodity devices (e.g. smartphone
cameras) without the need for a tripod. However, it is challenging to collect
the required paired short-long exposure frames to learn a supervised mapping.
Current approaches require a specialised rig or the use of static videos with
no subject or object motion, resulting in datasets that are limited in size,
diversity, and motion. We address the data collection bottleneck for low-light
video RAW-to-RGB by proposing a data synthesis mechanism, dubbed SIDGAN, that
can generate abundant dynamic video training pairs. SIDGAN maps videos found
'in the wild' (e.g. internet videos) into a low-light (short, long exposure)
domain. By generating dynamic video data synthetically, we enable a recently
proposed state-of-the-art RAW-to-RGB model to attain higher image quality
(improved colour, reduced artifacts) and improved temporal consistency,
compared to the same model trained with only static real video data.
