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Frame by Familiar Frame: Understanding Replication in Video Diffusion Models

Building on the momentum of image generation diffusion models, there is an increasing interest in video-based diffusion models. However, video generation poses greater challenges due to its higher-dimensional nature, the scarcity of training data, and the complex spatiotemporal relationships involved. Image generation models, due to their extensive data requirements, have already strained computational resources to their limits. There have been instances of these models reproducing elements from the training samples, leading to concerns and even legal disputes over sample replication. Video diffusion models, which operate with even more constrained datasets and are tasked with generating both spatial and temporal content, may be more prone to replicating samples from their training sets. Compounding the issue, these models are often evaluated using metrics that inadvertently reward replication. In our paper, we present a systematic investigation into the phenomenon of sample replication in video diffusion models. We scrutinize various recent diffusion models for video synthesis, assessing their tendency to replicate spatial and temporal content in both unconditional and conditional generation scenarios. Our study identifies strategies that are less likely to lead to replication. Furthermore, we propose new evaluation strategies that take replication into account, offering a more accurate measure of a model's ability to generate the original content.

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