Multi-subject Open-set Personalization in Video Generation

TL;DR

Video Alchemist introduces a multi-subject, open-set personalization model for video generation that eliminates the need for time-consuming optimization, leveraging a new Diffusion Transformer and a comprehensive dataset and evaluation framework.

Contribution

The paper presents a novel multi-subject, open-set video personalization method with a Diffusion Transformer, along with a new dataset construction pipeline and benchmark for evaluation.

Findings

Outperforms existing methods in personalization quality

Supports multiple subjects and open-set scenarios

Eliminates test-time optimization

Abstract

Video personalization methods allow us to synthesize videos with specific concepts such as people, pets, and places. However, existing methods often focus on limited domains, require time-consuming optimization per subject, or support only a single subject. We present Video Alchemist $-$ a video model with built-in multi-subject, open-set personalization capabilities for both foreground objects and background, eliminating the need for time-consuming test-time optimization. Our model is built on a new Diffusion Transformer module that fuses each conditional reference image and its corresponding subject-level text prompt with cross-attention layers. Developing such a large model presents two main challenges: dataset and evaluation. First, as paired datasets of reference images and videos are extremely hard to collect, we sample selected video frames as reference images and synthesize a clip of the target video. However, while models can easily denoise training videos given reference frames, they fail to generalize to new contexts. To mitigate this issue, we design a new automatic data construction pipeline with extensive image augmentations. Second, evaluating open-set video personalization is a challenge in itself. To address this, we introduce a personalization benchmark that focuses on accurate subject fidelity and supports diverse personalization scenarios. Finally, our extensive experiments show that our method significantly outperforms existing personalization methods in both quantitative and qualitative evaluations.