UniReal: Universal Image Generation and Editing via Learning Real-world
  Dynamics
Authors: name: Xi Chen, name: Zhifei Zhang, name: He Zhang, name: Yuqian Zhou, name: Soo Ye Kim, name: Qing Liu, name: Yijun Li, name: Jianming Zhang, name: Nanxuan Zhao, name: Yilin Wang, name: Hui Ding, name: Zhe Lin, name: Hengshuang Zhao

We introduce UniReal, a unified framework designed to address various image
generation and editing tasks. Existing solutions often vary by tasks, yet share
fundamental principles: preserving consistency between inputs and outputs while
capturing visual variations. Inspired by recent video generation models that
effectively balance consistency and variation across frames, we propose a
unifying approach that treats image-level tasks as discontinuous video
generation. Specifically, we treat varying numbers of input and output images
as frames, enabling seamless support for tasks such as image generation,
editing, customization, composition, etc. Although designed for image-level
tasks, we leverage videos as a scalable source for universal supervision.
UniReal learns world dynamics from large-scale videos, demonstrating advanced
capability in handling shadows, reflections, pose variation, and object
interaction, while also exhibiting emergent capability for novel applications.