Jacob Zhiyuan Fang — accepted AI-conference papers

2026

MAGREF: Masked Guidance for Any-Reference Video Generation with Subject Disentanglement

Yufan Deng, Yuanyang Yin, Xun Guo, Yizhi Wang, Jacob Zhiyuan Fang, Shenghai Yuan +5

ICLR 2026poster

We tackle the task of any-reference video generation, which aims to synthesize videos conditioned on arbitrary types and combinations of reference subjects, together with textual prompts. This task faces persistent challenges, including identity inconsistency, entanglement among multiple reference s…

Cited by 0 Source ↗code ↗Scholar ↗

2026

TGT: Text-Grounded Trajectories for Locally Controlled Video Generation

Guofeng Zhang, Angtian Wang, Jacob Zhiyuan Fang, Liming Jiang, Haotian Yang, Bo Liu +5

CVPR 2026

Text-to-video generation has advanced rapidly in visual fidelity, whereas standard methods still have limited ability to control the subject composition of generated scenes. Prior work shows that adding localized text control signals, such as bounding boxes or segmentation masks, can help. However,

Cited by 0 Source ↗Scholar ↗