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
Published: 2025-12-07 16:10:25

Abstract: 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.