Frame by Familiar Frame: Understanding Replication in Video Diffusion Models

TL;DR

A systematic investigation into the phenomenon of sample replication in video diffusion models is presented, and new evaluation strategies that take replication into account are proposed, offering a more accurate measure of a model's ability to generate the original content.

Abstract

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.

Figures (9)

• Figure 1: Diffusion-based video synthesis models can sometimes replicate training data by assembling memorized foreground and background elements. We demonstrate this trend across multiple diffusion models trained on diverse datasets. Such occurrences prompt inquiries regarding data memorization and the ownership of videos produced by diffusion methods. Bottom row: Videos sourced from the RaMViD hoppe2022diffusion, VIDM mei2023vidm, and LVDM he2022latent project websites. Top row: The most similar counterparts from the training dataset.
• Figure 2: Definition of replication in video generation domain. Content and motion replication refers to the direct duplication of content and motion from the training dataset, essentially producing a 1:1 copy. On the other hand, motion replication assesses a video generation model's inherent ability to create motion from an initial frame. This initial frame supplies the content context, but the true measure of a video generation network's capability lies in its understanding of the comprehensive content within that first frame. The critical question is whether the network genuinely comprehends and generates subsequent motion, or if it merely replicates sequences it has learned from the training data.
• Figure 3: The highest similarities identified within the UCF-101 dataset compared against outputs generated by an unconditional video generation approach, utilizing a latent video diffusion model (LVDM) he2022latent.
• Figure 4: The initial frame provided as a condition to the video generation model is denoted as the '$1^{st}$ frame'. '1st Frames' represent their original orientation from the dataset, while those with a red outline signify altered frames. Observations show that the model properly generates motion when presented with frames in their original orientation. However, it struggles to produce consistent motion when given an augmented version of the same image, indicating the model memorized the motion.
• Figure 5: An instance of replication in a text-to-video (T2V) model yin2023nuwa. Generated with the text prompt "Fred and Barney driving a car". NUWA-XL has been trained solely on the episodes of "The Flintstones". The replicated segment is from the episode "Disorder in the Court".