  Cell tracking remains a pivotal yet challenging task in biomedical research.
The full potential of deep learning for this purpose is often untapped due to
the limited availability of comprehensive and varied training data sets. In
this paper, we present SynCellFactory, a generative cell video augmentation. At
the heart of SynCellFactory lies the ControlNet architecture, which has been
fine-tuned to synthesize cell imagery with photorealistic accuracy in style and
motion patterns. This technique enables the creation of synthetic yet realistic
cell videos that mirror the complexity of authentic microscopy time-lapses. Our
experiments demonstrate that SynCellFactory boosts the performance of
well-established deep learning models for cell tracking, particularly when
original training data is sparse.
