arXiv Computer Vision October 28, 2026 By Beomjin Ahn

PREEN: Exemplar-Based Color Style Transfer with Smartphone Editor Presets

Read the original on arXiv Computer Vision →

PREEN (Preset Regression via an Exemplar-based Editor Network) performs color style transfer by predicting a 29‑parameter preset for a deterministic image editor, rather than generating a raster image. It uses a style‑transfer teacher to create pseudo‑presets, then trains a CLIP‑conditioned regressor refined with LoRA and a CIELAB loss. On the PST50 benchmark, PREEN outperforms baselines in pixel‑ and perceptual‑fidelity, runs in sub‑second on‑device latency, and offers a single inference‑time strength knob that smoothly transitions from default to vivid presets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv AI

Sep 18

Paint-Anything: Unified Any-Color Control for Image Generation and Editing

Paint-Anything introduces a unified hex-prompt interface that allows users to specify any 24‑bit hex color for both image generation and editing. The method trains on a new Paint‑500K dataset created from real images with object grounding, perceptual color labeling, and editing‑pair synthesis, and supplements this with pure‑color anchors to address shadow‑induced color inaccuracies. Evaluated on the newly proposed Any Color Benchmark (ACBench), Paint‑Anything achieves significant improvements over the base FLUX.2‑4B model, boosting T2I and editing scores by 85.3 % and 28.3 % respectively, and outperforms competing methods on the CompColor metric.

By Ji Xie, Dewei Zhou, Xinyu Huang, Zhennan Chen, Xun Wang

llms diffusion benchmarks data-engineering

arXiv Machine Learning

Aug 18

Deep Analog: Open-Set Film Emulation with Reference-Conditioned 3D LUTs

arXiv:2608. 14702v1 Announce Type: cross Abstract: Film emulation reproduces the look of an analog film stock on a new digital photograph.

By Yitong Mu

arXiv Computer Vision

Sep 17

Visual Autoregressive Priors for RAW-to-sRGB Image Signal Processing

arXiv:2609.18302v1 Announce Type: new Abstract: RAW-to-sRGB image signal processing (ISP) must recover perceptually faithful colors and fine details from sensor measurements, often under imperfect sp...

By Tailai Chen, Xiaotong Luo, Yuan Gao, Xin Jin, Wenjun Zeng

benchmarks safety

Hugging Face Trending Papers

Jul 7

From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models

Large-scale text-to-image models are attractive backbones for dense prediction because RGB generation pretraining learns rich semantic, structural, and geometric priors. Existing generative and editing approaches reuse these priors by casting dense prediction as target generation: annotations such as depth, normals, alpha mattes, masks, and heatmaps are encoded into an RGB-trained VAE latent space and decoded back as image-like targets.

diffusion computer-vision fine-tuning benchmarks

Hugging Face Trending Papers

Aug 3

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.

llms fine-tuning multimodal benchmarks

arXiv Computer Vision

Oct 8

Iris-3B: Going Beyond the Latent with Pixel-Space Diffusion Training, Conversion and Fine-Tuning

arXiv:2610.09450v1 Announce Type: new Abstract: Pixel-space diffusion models avoid the lossy VAE of latent models, which suggests an advantage on downstream tasks where fine-grained detail matters. W...

By Hanqiu Li Cai (SperidLabs), Chema Garabito (SperidLabs)

llms diffusion computer-vision fine-tuning safety