About ChronoEdit

Learn about the research, methodology, and official resources for ChronoEdit - a framework for temporal reasoning in image editing and world simulation.

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Educational Purpose Notice

This website is not the official ChronoEdit website. It was created for educational purposes only to provide information about the ChronoEdit research project. For official information, research papers, and code, please visit the official NVIDIA research pages linked below.

Official Resources

Research Paper

The official research paper published on arXiv provides detailed technical information about ChronoEdit's methodology, experiments, and results.

View Paper on arXiv

Project Page

The official NVIDIA research project page contains detailed information, galleries, and technical documentation.

Visit Project Page

GitHub Repository

The official GitHub repository contains code, models, and implementation details for ChronoEdit.

View on GitHub

Citation

If you use ChronoEdit in your research, please cite the original paper using the provided BibTeX format.

@article{wu2025chronoedit,
title={ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation},
author={Wu, Jay Zhangjie and Ren, Xuanchi and Shen, Tianchang and Cao, Tianshi and He, Kai and Lu, Yifan and Gao, Ruiyuan and Xie, Enze and Lan, Shiyi and Alvarez, Jose M. and Gao, Jun and Fidler, Sanja and Wang, Zian and Ling, Huan},
journal={arXiv preprint arXiv:2510.04290},
year={2025}
}

Research Team

Authors

NVIDIA Research

  • Jay Zhangjie Wu*
  • Xuanchi Ren*
  • Tianchang Shen
  • Tianshi Cao
  • Kai He
  • Yifan Lu
  • Ruiyuan Gao
  • Enze Xie
  • Shiyi Lan
  • Jose M. Alvarez
  • Jun Gao
  • Sanja Fidler
  • Zian Wang
  • Huan Ling*†

University of Toronto

  • Xuanchi Ren*
  • Tianchang Shen
  • Tianshi Cao
  • Kai He
  • Yifan Lu
  • Ruiyuan Gao
  • Enze Xie
  • Shiyi Lan
  • Sanja Fidler
  • Zian Wang

* equal contribution † corresponding author

Technical Overview

Abstract

Recent advances in large generative models have significantly advanced image editing and in-context image generation, yet a critical gap remains in ensuring physical consistency, where edited objects must remain coherent. This capability is especially vital for world simulation related tasks. In this paper, we present ChronoEdit, a framework that reframes image editing as a video generation problem.

Key Contributions

  • • ChronoEdit treats the input and edited images as the first and last frames of a video, allowing it to use large pretrained video generative models that capture not only object appearance but also the implicit physics of motion and interaction through learned temporal consistency.
  • • ChronoEdit introduces a temporal reasoning stage that explicitly performs editing at inference time. Under this setting, the target frame is jointly denoised with reasoning tokens to imagine a plausible editing trajectory that constrains the solution space to physically viable transformations.
  • • The reasoning tokens are then dropped after a few steps to avoid the high computational cost of rendering a full video.
  • • To validate ChronoEdit, we introduce PBench-Edit, a new benchmark of image-prompt pairs for contexts that require physical consistency, and demonstrate that ChronoEdit surpasses state-of-the-art baselines in both visual fidelity and physical plausibility.

Methodology

ChronoEdit's approach involves two main stages:

  1. Temporal Reasoning Stage: The model imagines and denoises a short trajectory of intermediate frames. These intermediate frames act as reasoning tokens, guiding how the edit should unfold in a physically consistent manner.
  2. Editing Frame Generation Stage: For efficiency, the reasoning tokens are discarded in this subsequent stage, where the target frame is further refined into the final edited image.

Important Disclaimer

This website is not affiliated with NVIDIA or the official ChronoEdit research team. It was created independently for educational purposes to provide information about the ChronoEdit research project.

All research, code, and official resources belong to NVIDIA and the University of Toronto research teams. This website does not claim ownership of any ChronoEdit intellectual property or research findings.

For official information, research papers, code, and support, please visit the official NVIDIA research pages and GitHub repository linked above.

This educational website is provided "as is" without any warranties or guarantees regarding the accuracy of the information presented.