arXiv Computer Vision
GenScale: A Benchmark for Relative Object Scale in Image Generation and Editing
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arXiv Computer Vision
Oct 19
Kingfisher: Fast Personalized Visual Autoregressive Generation via Scale-Aware Prompt-Conditioned Scale Decomposition
Kingfisher is a fast, model‑tuning‑free framework for personalized image generation using visual autoregressive models. It decomposes early autoregressive scale codes into content and style components conditioned on prompts, then recombines these components from two real images into a compact semantic prefix. The frozen generator refines the remaining scales, achieving strong content preservation and superior style and text alignment while cutting pair‑specific preparation time by 54× compared with diffusion methods and 7.5× compared with other VAR approaches.
Hugging Face Trending Papers
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Context-Aware Autoregressive Image Generation for Emerging Reasoning Properties
Autoregressive image generation has recently emerged as a competitive visual generation paradigm, yet its generation order is often predefined or heuristic, such as next-patch prediction, random-order...
arXiv AI
Jun 2
TECCI: Tricky Edits of Collected and Curated Images
arXiv:2606. 01213v1 Announce Type: cross Abstract: Despite tremendous recent progress, current text-guided image editing methods still struggle with many aspects of editing involving instruction following, minimally editing the source image, and ensuring high visual quality.
arXiv Computer Vision
Aug 28
RubricRM: Generative Reward Modeling via Dynamic Rubrics for Image Generation and Editing
RubricRM introduces a pairwise generative reward modeling framework that generates an input‑specific rubric—comprising evaluation dimensions, weights, and scoring criteria—to score candidate images. The method is trained in two stages: supervised fine‑tuning to learn the rubric‑based scoring paradigm and GRPO to refine dimension‑level rewards. Experiments on text‑to‑image generation and instruction‑based image editing benchmarks demonstrate that RubricRM outperforms existing specialized reward models and competes with strong proprietary MLLM judges while using smaller backbones.
arXiv Computer Vision
4d ago
Context-Aware Autoregressive Image Generation for Emerging Reasoning Properties
arXiv:2611.06838v1 Announce Type: new Abstract: Autoregressive image generation has recently emerged as a competitive visual generation paradigm, yet its generation order is often predefined or heuri...
arXiv Computer Vision
Nov 2
From Evaluation to Evolution: Benchmarking Image Editing and Evolving Image Models
The paper introduces WeGenEditBench, a benchmark with 2,740 evaluation instances and 148 task types for image editing, focusing on local failure detection and prioritization for model improvement. It also presents WeGenEvoStudio, a multi‑agent harness that supports recursive self‑improvement by diagnosing failures, constructing retrieval‑grounded data, and adapting models. Experiments show that models such as GPT‑Image‑2 and Seedream‑5.0‑Pro excel in single‑image and text‑intensive editing, and evolution experiments demonstrate significant targeted gains on selected benchmarks.