ChronoEdit: Towards Temporal Reasoning for Image Editing and World
  Simulation
Authors: name: Jay Zhangjie Wu, name: Xuanchi Ren, name: Tianchang Shen, name: Tianshi Cao, name: Kai He, name: Yifan Lu, name: Ruiyuan Gao, name: Enze Xie, name: Shiyi Lan, name: Jose M. Alvarez, name: Jun Gao, name: Sanja Fidler, name: Zian Wang, name: Huan Ling
Organization: NVIDIA

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. First, ChronoEdit treats the input and edited images
as the first and last frames of a video, allowing it to leverage large
pretrained video generative models that capture not only object appearance but
also the implicit physics of motion and interaction through learned temporal
consistency. Second, 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. Code and models for both the 14B and 2B variants of ChronoEdit
will be released on the project page:
https://research.nvidia.com/labs/toronto-ai/chronoedit