Papers Image Editing
“Image Editing” 태그가 달린 논문 613편 · 필터 해제
When Text-to-Image Helps Editing: The Effects of Conditioning During Denoising
Unified models are trained for both instruction-based image editing and text-to-image (T2I) generation, but standard editing pipelines keep source-image conditioning throughout denoising. We ask whether editing can benef…
Image EditingDo MLLM Judges Judge the Edit? Auditing Bias in Image Editing Evaluation with Verified Quality Preservation
Multimodal large language models (MLLMs) are increasingly used as automated judges for instruction-based image editing and as reward signals for model training. However, systematically auditing whether these judges are i…
Image EditingFlowTool: Controlling Tool Parameter in Image Retouching via Flow Matching
Tool-based image editing (image retouching) is commonly formulated with autoregressive multimodal large language models (MLLMs) that sequentially generate reasoning, tool selections, and parameter values. In this work, w…
Image EditingOn-Policy Self-Distillation for Multi-Turn Image Editing
Instruction-based image editing has achieved strong performance in single-turn settings, yet practical editing is often iterative, with each instruction applied to the output of the previous turn. We find that existing e…
Image EditingRefinement Is Inherently Editable: Training-Free Prompt-to-Prompt Image Editing with Generative Refinement Network
Text-guided image editing must introduce the requested changes while preserving unrelated source content. Diffusion-based editors rely on spatial controls whose inaccuracies can leave edits incomplete or alter unrelated …
Image EditingMi-Ripple: Restoring Images Degraded by Iterative AI Editing
Iterative reference-conditioned image editing can introduce grid-like and granular textures, commonly described as digital ripple. We present Mi-Ripple, a diagnosis-guided restoration workflow that suppresses this digita…
Image EditingSenseNova-U1.5: Towards Native Unified Visual Intelligence
We launch SenseNova-U1.5, an 8B-MoT native unified multimodal model that understands, reasons about, and generates visual content within an encoder-free and VAE-free architecture. We strengthen its visual interface throu…
Reinforcement LearningInstruction FollowingImage EditingWeAgent-MMGenEdit: A Full-Stack Recipe for Multimodal Agentic Image Generation and Editing
Image generation and editing models have advanced rapidly, yet remain unreliable when prompts require external world knowledge. Bounded and long-tail parametric knowledge prevents direct or reason-then-generate approache…
Image GenerationImage EditingLearning 3D Editing without Paired Supervision via Generative Prior Distillation
Instruction-guided 3D editing is essential for interactive content creation, yet it faces a significant bottleneck: the severe scarcity of high-quality paired training data. Existing approaches attempt to bypass this by …
Instruction FollowingImage EditingLensStyle: Learning the Optical Aesthetics for Controllable Stylized Lens Effect Rendering
The visual aesthetics of photographs are deeply influenced by lens characteristics such as aperture shape, optical vignetting and optical diffraction, which together define a camera's unique optical style. Existing lens …
Image EditingAngelFingerprint: A Traceable, Explainable, and White-Box Stealthy Watermark for Text-Guided Image Editing
Text-guided diffusion editing raises disinformation concerns, making reliable image provenance essential. While watermarks are commonly used for this purpose, most methods carry a fixed ID that cannot explain what was ch…
Image EditingAn Evaluation Framework for Generating Multi-View Images of a Person in a Scene
Recent generative image-editing Diffusion Transformers (DiTs) demonstrate impressive semantic editing capabilities but still struggle with spatially consistent camera angle changes. A primary bottleneck in training found…
Image EditingAphanta: Diagnosing Task-Aligned Image-Edited Intermediates for Multimodal Reasoning
Explicit visual intermediates can help multimodal large language models (MLLMs) externalize spatial evidence and updated visual states, but their utility depends on whether an image editor can faithfully realize the requ…
Multimodal ReasoningImage EditingRubricRM: Generative Reward Modeling via Dynamic Rubrics for Image Generation and Editing
Reward models play an essential role in aligning visual generative models, yet most existing visual reward models use a single scalar score or rely on fixed criteria that cannot adapt to different instructions. This limi…
Text-to-Image GenerationImage EditingRefVideo-6M: A Reliable Reference-Based Dataset for Instructional Video Editing
Recent advances in video editing have been largely driven by large-scale instruction-based datasets. However, existing datasets still suffer from two critical limitations. First, target videos are commonly produced by au…
Image EditingFlashNormal: Detailed Surface Normal Estimation from Flash and No-Flash Images
High-quality surface normal estimation is preferred for detailed surface shape recovery and image editing. Existing single image-based methods, though being a practical setup, often struggle to recover fine surface detai…
Image EditingWhen the Edit Changes the Patient: Measuring Identity Preservation in Counterfactual Retinal Images
Counterfactual medical image generation aims to modify an existing image to reflect a hypothetical scenario in which certain characteristics of the imaged subject are altered, while keeping their identity fixed. Most exi…
Medical Image GenerationImage EditingCan We Perform Online RL for Image Editing without Editing Rewards?
Reinforcement learning (RL) enables direct preference optimization for image editing through editing-specific rewards, which remain less developed due to costly triplet supervision and complex task-dependent calibration.…
Reinforcement LearningImage EditingExploring the Performance Frontier of Compact Unified Image Generation Models
We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi-image editing. Our goal is to explore how far a relatively small visual generator can be pushed through system…
Text-to-Image GenerationReinforcement LearningModel CompressionImage EditingDARS: Dual-Level Credit Assignment RL with Structured Reasoning for Instruction-Based Image Editing
Instruction-based image editing uses a planner-renderer pipeline: a vision-language model (VLM) first converts the instruction into an edit plan, and a diffusion model then executes that plan. Training such systems with …
Reinforcement LearningImage Editing