Title: Contrastive Video Textures. Abstract: Existing methods for video generation struggle to generate more than a short sequence of frames. We introduce a non-parametric approach for infinite video generation based on learning to resample frames from an input video. Our work is inspired by Video Textures, a classic method relying on pixel similarity to stitch sequences of frames, which performs well for videos with a high degree of regularity but fails in less constrained settings. Our method learns a distance metric to compare frames in a manner that scales to more challenging dynamics and allows for conditioning on heterogeneous data, such as audio. We learn representations for video frames and probabilities of transitioning by fitting a video-specific bi-gram model trained using contrastive learning. To synthesize the texture, we represent the video as a graph where the nodes are frames and edges are transitions with probabilities predicted by our video-specific model. By randomly traversing edges with high transition probabilities, we generate diverse temporally smooth videos with novel sequences and transitions. The model naturally extends with no additional training to handle the task of Audio Conditioned Video Synthesis, when conditioned on an audio signal. Our model outperforms baselines on human perceptual scores, can handle a diverse range of input videos, and can combine semantic and audio-visual cues in order to synthesize videos that synchronize well with an audio signal.