ChronoEdit: Towards Temporal Reasoning for Image Editing and World
  Simulation
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
68e495cbe4e093a7044e4dc1
Jay Zhangjie Wu
claimed_verified
2025-10-07T12:26:23.451Z
633aaf695df91da9cea92960
https://cdn-avatars.huggingface.co/v1/production/uploads/633aaf695df91da9cea92960/h1oOWUudbdlsd63Q5w0hM.png
Jay Wu
user
jayw
68e495cbe4e093a7044e4dc2
Xuanchi Ren
68e495cbe4e093a7044e4dc3
Tianchang Shen
68e495cbe4e093a7044e4dc4
Tianshi Cao
68e495cbe4e093a7044e4dc5
Kai He
68e495cbe4e093a7044e4dc6
Yifan Lu
68e495cbe4e093a7044e4dc7
Ruiyuan Gao
68e495cbe4e093a7044e4dc8
Enze Xie
68e495cbe4e093a7044e4dc9
Shiyi Lan
68e495cbe4e093a7044e4dca
Jose M. Alvarez
68e495cbe4e093a7044e4dcb
Jun Gao
68e495cbe4e093a7044e4dcc
Sanja Fidler
68e495cbe4e093a7044e4dcd
Zian Wang
68e495cbe4e093a7044e4dce
Huan Ling
ChronoEdit: Towards Temporal Reasoning for Image Editing and World
  Simulation
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
ChronoEdit addresses physical consistency in image editing by reframing it as a video generation problem, leveraging pretrained video models and temporal reasoning tokens.
video generative models temporal consistency temporal reasoning denoising reasoning tokens PBench-Edit visual fidelity physical plausibility