SmartPhotoCrafter: Unified Reasoning, Generation and Optimization for Automatic Photographic Image Editing
Summary
SmartPhotoCrafter introduces an automatic photographic image editing pipeline that unifies quality comprehension and enhancement without explicit human instructions, outperforming existing generative models on photo-realistic enhancement tasks.
View Cached Full Text
Cached at: 04/22/26, 06:17 AM
Paper page - SmartPhotoCrafter: Unified Reasoning, Generation and Optimization for Automatic Photographic Image Editing
Source: https://huggingface.co/papers/2604.19587 Authors:
,
,
,
,
,
,
,
,
,
,
,
Abstract
SmartPhotoCrafter automates photographic image editing by combining image quality comprehension with targeted enhancement, using a reasoning-to-generation approach that eliminates the need for explicit human instructions.
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 performsimage quality comprehensionand identifies deficiencies by theImage Critic module, and then thePhotographic Artist modulerealizes targeted edits to enhance image appeal, eliminating the need for explicit human instructions. Amulti-stage training pipelineis adopted: (i)Foundation pretrainingto establish basic aesthetic understanding and editing capabilities, (ii)Adaptation with reasoning-guided multi-edit supervisionto incorporate richsemantic guidance, and (iii) Coordinated reasoning-to generation reinforcement learning to jointly optimize reasoning and generation. During training, SmartPhotoCrafter emphasizesphoto-realistic image generation, while supporting bothimage restorationandretouching taskswith 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.
View arXiv pageView PDFAdd to collection
Get this paper in your agent:
hf papers read 2604\.19587
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2604.19587 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2604.19587 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2604.19587 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
PhotoCraft: Agentic Reasoning with Hierarchical Self-Evolving Memory for Deep Image Search
PhotoCraft proposes a training-free hierarchical memory system for photo-search agents, integrating working, episodic, and semantic memory to maintain long-horizon context and transfer knowledge across tasks, achieving up to 18.5% improvement on DISBench.
ETCHR: Editing To Clarify and Harness Reasoning
ETCHR is a novel image editing approach that decouples visual reasoning from image generation, using a two-stage training process (Reasoning Imitation and Reasoning Enhancement) to improve multimodal language model performance across five visual reasoning tasks. It achieves consistent gains of 4-5% Pass@1 on models like Qwen3-VL-8B, Gemini-3.1-Flash-Lite, and Kimi K2.5.
Uni-Edit: Intelligent Editing Is A General Task For Unified Model Tuning
Uni-Edit proposes using intelligent image editing as a single general task to simultaneously improve unified multimodal models' understanding, generation, and editing capabilities, with an automated data synthesis pipeline creating complex editing instructions.
Is This Edit Correct? A Multi-Dimensional Benchmark for Reasoning-Aware Image Editing
This paper introduces RE-Edit, a benchmark for evaluating image editing systems across five reasoning dimensions (physical, environmental, cultural, causal, referential) to assess logical consistency beyond visual plausibility. The benchmark includes 1,000 samples and evaluates ten open-source and two commercial models, showing that even advanced systems struggle with implicit multi-dimensional reasoning.
From Plans to Pixels: Learning to Plan and Orchestrate for Open-Ended Image Editing
An experiential framework for long-horizon image editing that couples planning with reward-driven execution to improve coherence and reliability of complex multi-step edits.