SUGAR: Subject-Driven Video Customization in a Zero-Shot Manner
Authors: name: Yufan Zhou, name: Ruiyi Zhang, name: Jiuxiang Gu, name: Nanxuan Zhao, name: Jing Shi, name: Tong Sun

We present SUGAR, a zero-shot method for subject-driven video customization.
Given an input image, SUGAR is capable of generating videos for the subject
contained in the image and aligning the generation with arbitrary visual
attributes such as style and motion specified by user-input text. Unlike
previous methods, which require test-time fine-tuning or fail to generate
text-aligned videos, SUGAR achieves superior results without the need for extra
cost at test-time. To enable zero-shot capability, we introduce a scalable
pipeline to construct synthetic dataset which is specifically designed for
subject-driven customization, leading to 2.5 millions of image-video-text
triplets. Additionally, we propose several methods to enhance our model,
including special attention designs, improved training strategies, and a
refined sampling algorithm. Extensive experiments are conducted. Compared to
previous methods, SUGAR achieves state-of-the-art results in identity
preservation, video dynamics, and video-text alignment for subject-driven video
customization, demonstrating the effectiveness of our proposed method.