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StyleGAN-V

StyleGAN-V extends the capabilities of StyleGAN2 to video generation by treating videos as continuous signals in time. It employs neural representations with positional embeddings to model continuous motion, allowing for the generation of high-fidelity videos. Notably, StyleGAN-V can be effectively trained on sparse video datasets, requiring as few as two frames per clip. This approach facilitates spatial manipulations and offers a straightforward architecture for generating natural videos using likelihood-based modeling.