  Virtual try-on has garnered interest as a neural rendering benchmark task to
evaluate complex object transfer and scene composition. Recent works in virtual
clothing try-on feature a plethora of possible architectural and data
representation choices. However, they present little clarity on quantifying the
isolated visual effect of each choice, nor do they specify the hyperparameter
details that are key to experimental reproduction. Our work, ShineOn,
approaches the try-on task from a bottom-up approach and aims to shine light on
the visual and quantitative effects of each experiment. We build a series of
scientific experiments to isolate effective design choices in video synthesis
for virtual clothing try-on. Specifically, we investigate the effect of
different pose annotations, self-attention layer placement, and activation
functions on the quantitative and qualitative performance of video virtual
try-on. We find that DensePose annotations not only enhance face details but
also decrease memory usage and training time. Next, we find that attention
layers improve face and neck quality. Finally, we show that GELU and ReLU
activation functions are the most effective in our experiments despite the
appeal of newer activations such as Swish and Sine. We will release a
well-organized code base, hyperparameters, and model checkpoints to support the
reproducibility of our results. We expect our extensive experiments and code to
greatly inform future design choices in video virtual try-on. Our code may be
accessed at https://github.com/andrewjong/ShineOn-Virtual-Tryon.
