# Frame by Familiar Frame: Understanding Replication in Video Diffusion Models

arXiv: https://arxiv.org/abs/2403.19593 · HTML: https://mlsanity.com/paper/2403.19593/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 35 of its 54 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 30 of 35)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://mlsanity.com/paper/2303.13439/) (2023)
- [ModelScope Text-to-Video Technical Report](https://mlsanity.com/paper/2308.06571/) (2023)
- [VideoCrafter1: Open Diffusion Models for High-Quality Video Generation](https://mlsanity.com/paper/2310.19512/) (2023)
- [Phenaki: Variable Length Video Generation From Open Domain Textual Description](https://mlsanity.com/paper/2210.02399/) (2022)
- [MagicVideo: Efficient Video Generation With Latent Diffusion Models](https://mlsanity.com/paper/2211.11018/) (2022)
- [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://mlsanity.com/paper/2212.03860/) (2022)
- [I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models](https://mlsanity.com/paper/2311.04145/) (2023)
- [VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation](https://mlsanity.com/paper/2303.08320/) (2023)
- [MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation](https://mlsanity.com/paper/2205.09853/) (2022)
- [Understanding and Mitigating Copying in Diffusion Models](https://mlsanity.com/paper/2305.20086/) (2023)
- [Dreamix: Video Diffusion Models are General Video Editors](https://mlsanity.com/paper/2302.01329/) (2023)
- [A Self-Supervised Descriptor for Image Copy Detection](https://mlsanity.com/paper/2202.10261/) (2022)
- [NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation](https://mlsanity.com/paper/2303.12346/) (2023)
- [Diffusion Models for Video Prediction and Infilling](https://mlsanity.com/paper/2206.07696/) (2022)
- [VIDM: Video Implicit Diffusion Models](https://mlsanity.com/paper/2212.00235/) (2022)
- [A Non-Parametric Test to Detect Data-Copying in Generative Models](https://mlsanity.com/paper/2004.05675/) (2020)
- [Analyzing Bias in Diffusion-based Face Generation Models](https://mlsanity.com/paper/2305.06402/) (2023)

## Built on by (most cited first, 11 of 11)

- [On the Edge of Memorization in Diffusion Models](https://mlsanity.com/paper/2508.17689/) (2025)
- [Replication in Visual Diffusion Models: A Survey and Outlook](https://mlsanity.com/paper/2408.00001/) (2024)
- [SIDE: Surrogate Conditional Data Extraction from Diffusion Models](https://mlsanity.com/paper/2410.02467/) (2024)
- [Gradient Variance Reveals Failure Modes in Flow-Based Generative Models](https://mlsanity.com/paper/2510.18118/) (2025)
- [Investigating Memorization in Video Diffusion Models](https://mlsanity.com/paper/2410.21669/) (2024)
- [Enhancing Privacy-Utility Trade-offs to Mitigate Memorization in Diffusion Models](https://mlsanity.com/paper/2504.18032/) (2025)
- [Generative AI Training and Copyright Law](https://mlsanity.com/paper/2502.15858/) (2025)
- [YoCausal: How Far is Video Generation from World Model? A Causality Perspective](https://mlsanity.com/paper/2605.30346/) (2026)
- [Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches](https://mlsanity.com/paper/2506.10825/) (2025)
- [Generative AI in Collaborative Academic Report Writing: Advantages, Disadvantages, and Ethical Considerations](https://mlsanity.com/paper/2504.08832/) (2025)
- [Few-Shot-Based Modular Image-to-Video Adapter for Diffusion Models](https://mlsanity.com/paper/2512.20000/) (2025)

## Abstract (arXiv)

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.