Scaling Zero-Shot Reference-to-Video Generation
Authors: name: Zijian Zhou, name: Shikun Liu, name: Haozhe Liu, name: Haonan Qiu, name: Zhaochong An, name: Weiming Ren, name: Zhiheng Liu, name: Xiaoke Huang, name: Kam Woh Ng, name: Tian Xie, name: Xiao Han, name: Yuren Cong, name: Hang Li, name: Chuyan Zhu, name: Aditya Patel, name: Tao Xiang, name: Sen He
Organization: AI at Meta

Reference-to-video (R2V) generation aims to synthesize videos that align with a text prompt while preserving the subject identity from reference images. However, current R2V methods are hindered by the reliance on explicit reference image-video-text triplets, whose construction is highly expensive and difficult to scale. We bypass this bottleneck by introducing Saber, a scalable zero-shot framework that requires no explicit R2V data. Trained exclusively on video-text pairs, Saber employs a masked training strategy and a tailored attention-based model design to learn identity-consistent and reference-aware representations. Mask augmentation techniques are further integrated to mitigate copy-paste artifacts common in reference-to-video generation. Moreover, Saber demonstrates remarkable generalization capabilities across a varying number of references and achieves superior performance on the OpenS2V-Eval benchmark compared to methods trained with R2V data.