ChronoEdit：面向图像编辑与世界模拟的时间推理
ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation
AI总结：
ChronoEdit将图像编辑重构为视频生成，通过时间推理阶段约束物理可行性，并引入PBench-Edit基准，在视觉保真度和物理合理性上超越现有方法。
摘要：
大型生成模型的最新进展极大地增强了图像编辑和上下文图像生成的能力，但在确保物理一致性方面仍存在关键差距，即编辑后的对象必须保持连贯。这一能力对于世界模拟相关任务尤为重要。在本文中，我们提出了ChronoEdit，一个将图像编辑重新定义为视频生成问题的框架。首先，ChronoEdit将输入图像和编辑后的图像视为视频的首帧和末帧，从而能够利用大型预训练视频生成模型，这些模型不仅捕捉对象外观，还通过学习的时间一致性捕捉运动和交互的隐式物理规律。其次，ChronoEdit引入了一个时间推理阶段，在推理时显式执行编辑。在此设置下，目标帧与推理令牌（reasoning tokens）联合去噪，以想象一条合理的编辑轨迹，将解空间约束为物理上可行的变换。随后，在几步之后丢弃推理令牌，以避免渲染完整视频的高计算成本。为了验证ChronoEdit，我们引入了PBench-Edit，一个针对需要物理一致性的场景的新图像-提示对基准，并证明ChronoEdit在视觉保真度和物理合理性方面均超越了最先进的基线。项目和代码页面：https://research.nvidia.com/labs/toronto-ai/chronoedit
英文摘要：
Recent advances in large generative models have greatly enhanced both 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, 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. Project page for code and models: https://research.nvidia.com/labs/toronto-ai/chronoedit
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
