Continuous conditional video synthesis by neural processes
share
We propose a unified model for multiple conditional video synthesis tasks, including video prediction and video frame interpolation. We show that conditional video synthesis can be formulated as a neural process, which maps input spatio-temporal coordinates to target pixel values given context spatio-temporal coordinates and pixels values. Specifically, we feed an implicit neural representations of coordinates into a Transformer-based non-autoregressive conditional video synthesis model. Our task-specific models outperform previous work for video interpolation on multiple datasets and reach a competitive performance with the state-of-the-art models for video prediction. Importantly, the model is able to interpolate or predict with an arbitrary high frame rate, i.e., continuous synthesis. Our source code is available at https://github.com/NPVS/NPVS.
READ FULL TEXT