SmartPhotoCrafter: 统一推理、生成与优化用于自动摄影图像编辑
SmartPhotoCrafter: Unified Reasoning, Generation and Optimization for Automatic Photographic Image Editing
AI总结：
本文提出SmartPhotoCrafter，通过统一推理生成过程实现自动摄影图像编辑，提升图像质量与美观，无需人工指令。
摘要：
传统摄影图像编辑需要用户具备足够的审美理解以提供调整图像质量和相机参数的指令。然而，这种范式依赖于显式的审美意图指令，常存在模糊、不完整或非专家用户无法访问的问题。本文提出SmartPhotoCrafter，将图像编辑视为紧密耦合的推理到生成过程。该模型首先通过Image Critic模块进行图像质量理解并识别缺陷，然后通过Photographic Artist模块实现针对性编辑以提升图像吸引力，无需显式的人类指令。采用多阶段训练流程：(i) 基础预训练以建立基本审美理解和编辑能力，(ii) 适应性训练以多编辑监督结合丰富的语义指导，(iii) 协调推理到生成强化学习以共同优化推理和生成。训练过程中，SmartPhotoCrafter强调逼真图像生成，同时支持图像修复和润色任务，保持颜色和色调相关的语义一致性。我们还构建了阶段特定的数据集，逐步构建推理和可控生成，有效跨模块协作，最终实现高质量的摄影增强。实验表明，SmartPhotoCrafter在自动摄影增强任务中优于现有生成模型，实现了逼真结果并表现出更高的色调敏感性以润色指令。项目页面：https://github.com/vivoCameraResearch/SmartPhotoCrafter.
英文摘要：
Traditional photographic image editing typically requires users to possess sufficient aesthetic understanding to provide appropriate instructions for adjusting image quality and camera parameters. However, this paradigm relies on explicit human instruction of aesthetic intent, which is often ambiguous, incomplete, or inaccessible to non-expert users. In this work, we propose SmartPhotoCrafter, an automatic photographic image editing method which formulates image editing as a tightly coupled reasoning-to-generation process. The proposed model first performs image quality comprehension and identifies deficiencies by the Image Critic module, and then the Photographic Artist module realizes targeted edits to enhance image appeal, eliminating the need for explicit human instructions. A multi-stage training pipeline is adopted: (i) Foundation pretraining to establish basic aesthetic understanding and editing capabilities, (ii) Adaptation with reasoning-guided multi-edit supervision to incorporate rich semantic guidance, and (iii) Coordinated reasoning-to generation reinforcement learning to jointly optimize reasoning and generation. During training, SmartPhotoCrafter emphasizes photo-realistic image generation, while supporting both image restoration and retouching tasks with consistent adherence to color- and tone-related semantics. We also construct a stage-specific dataset, which progressively builds reasoning and controllable generation, effective cross-module collaboration, and ultimately high-quality photographic enhancement. Experiments demonstrate that SmartPhotoCrafter outperforms existing generative models on the task of automatic photographic enhancement, achieving photo-realistic results while exhibiting higher tonal sensitivity to retouching instructions. Project page: https://github.com/vivoCameraResearch/SmartPhotoCrafter.
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。
