First Frame Is the Place to Go for Video Content Customization
Jingxi Chen, Zongxia Li, Zhichao Liu, Guangyao Shi, Xiyang Wu, Fuxiao Liu, Cornelia Fermuller, Brandon Y. Feng, Yiannis Aloimonos
2025-11-21
Summary
This paper investigates how video generation models use the very first image you give them, and finds it's more important than previously thought.
What's the problem?
Traditionally, the first frame in a video generation model was seen as just the starting point – like the first domino in a chain. Researchers assumed it simply kicked off the animation process. The problem was that customizing videos, or making changes based on a specific example, usually required a lot of new training data or complicated changes to the model itself.
What's the solution?
The researchers discovered that video models actually *remember* the visual elements from that first frame and store them as a kind of 'memory'. They then cleverly reuse those elements throughout the video generation process. Because of this, they were able to customize videos effectively – changing objects or styles – using only a small number of example images, like 20 to 50, without needing to retrain the entire model or make any major adjustments.
Why it matters?
This is important because it shows that existing video generation models have a hidden ability to be customized easily. It means we don't necessarily need huge datasets or complex engineering to get these models to create videos exactly how we want them, opening up possibilities for more personalized and controlled video creation.
Abstract
What role does the first frame play in video generation models? Traditionally, it's viewed as the spatial-temporal starting point of a video, merely a seed for subsequent animation. In this work, we reveal a fundamentally different perspective: video models implicitly treat the first frame as a conceptual memory buffer that stores visual entities for later reuse during generation. Leveraging this insight, we show that it's possible to achieve robust and generalized video content customization in diverse scenarios, using only 20-50 training examples without architectural changes or large-scale finetuning. This unveils a powerful, overlooked capability of video generation models for reference-based video customization.