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Videos show continuous events, yet most $-$ if not all $-$ video synthesisframeworks treat them discretely in time. In this work, we think of videos ofwhat they should be $-$ time-continuous signals, and extend the paradigm ofneural representations to build a continuous-time video generator. For this, wefirst design continuous motion representations through the lens of positionalembeddings. Then, we explore the question of training on very sparse videos anddemonstrate that a good generator can be learned by using as few as 2 framesper clip. After that, we rethink the traditional image + video discriminatorspair and design a holistic discriminator that aggregates temporal informationby simply concatenating frames' features. This decreases the training cost andprovides richer learning signal to the generator, making it possible to traindirectly on 1024$^2$ videos for the first time. We build our model on top ofStyleGAN2 and it is just ${\approx}5\%$ more expensive to train at the sameresolution while achieving almost the same image quality. Moreover, our latentspace features similar properties, enabling spatial manipulations that ourmethod can propagate in time. We can generate arbitrarily long videos atarbitrary high frame rate, while prior work struggles to generate even 64frames at a fixed rate. Our model is tested on four modern 256$^2$ and one1024$^2$-resolution video synthesis benchmarks. In terms of sheer metrics, itperforms on average ${\approx}30\%$ better than the closest runner-up. Projectwebsite: https://universome.github.io.

StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2