Multi-subject Open-set Personalization in Video Generation
Authors: name: Tsai-Shien Chen, name: Aliaksandr Siarohin, name: Willi Menapace, name: Yuwei Fang, name: Kwot Sin Lee, name: Ivan Skorokhodov, name: Kfir Aberman, name: Jun-Yan Zhu, name: Ming-Hsuan Yang, name: Sergey Tulyakov

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.