title: UniReal: Universal Image Generation and Editing via Learning Real-world
    Dynamics
summary: 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.
paper_authors._id: 675904147ab15d62b77e40e0
paper_authors.name: Xi Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-11T10:19:38.341Z
paper_authors._id: 644a1b6401e18bf93a6f45c1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/644a1b6401e18bf93a6f45c1/P0i_CgCrIzOS2tYRlxoE9.png
paper_authors.fullname: xichen
paper_authors.type: user
paper_authors.user: xichenhku
paper_authors._id: 675904147ab15d62b77e40e1
paper_authors.name: Zhifei Zhang
paper_authors._id: 675904147ab15d62b77e40e2
paper_authors.name: He Zhang
paper_authors._id: 675904147ab15d62b77e40e3
paper_authors.name: Yuqian Zhou
paper_authors._id: 675904147ab15d62b77e40e4
paper_authors.name: Soo Ye Kim
paper_authors._id: 675904147ab15d62b77e40e5
paper_authors.name: Qing Liu
paper_authors._id: 675904147ab15d62b77e40e6
paper_authors.name: Yijun Li
paper_authors._id: 675904147ab15d62b77e40e7
paper_authors.name: Jianming Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-11T10:21:15.636Z
paper_authors._id: 632d52047bad38a82f75daee
paper_authors.avatarUrl: /avatars/56cc9b5dea95408a83cb80ee8c5565bf.svg
paper_authors.fullname: Jianming Zhang
paper_authors.type: user
paper_authors.user: jimmie33
paper_authors._id: 675904147ab15d62b77e40e8
paper_authors.name: Nanxuan Zhao
paper_authors._id: 675904147ab15d62b77e40e9
paper_authors.name: Yilin Wang
paper_authors._id: 675904147ab15d62b77e40ea
paper_authors.name: Hui Ding
paper_authors._id: 675904147ab15d62b77e40eb
paper_authors.name: Zhe Lin
paper_authors._id: 675904147ab15d62b77e40ec
paper_authors.name: Hengshuang Zhao
paper_title: UniReal: Universal Image Generation and Editing via Learning Real-world
    Dynamics
paper_summary: 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.
paper_ai_keywords: unified framework image generation editing customization composition discontinuous video generation world dynamics shadows reflections pose variation object interaction