KwaiMind is a commercial image editing system that combines general editing capabilities with e-commerce specialization. It uses an agent-based data engine with 1.8 million editing pairs and a multimodal diffusion transformer trained through pre‑training, fine‑tuning, preference optimization, and online reinforcement learning. The system is guided by a vision‑language judge and specialized rewards for click‑through rate, text rendering, and product consistency, and it achieves top scores on ImgEdit, GEdit, REDEdit, and the new Ecom‑Bench, while improving predicted and actual CTR in offline and online experiments.
By Junlong Wu, Zijun Li, Yuting Hu, Jia Sun, Pengcheng Wei, Yimin Zhou, Honglie Wang, Huaiqing Wang, Dewen Fan, Fei Zuo, Haixuan Gao, Lihui Peng, Tingxuan She, Yuqing Li, Boheng Zhang, Fan Yang, Wenwu Ou
arXiv:2608. 00584v1 Announce Type: cross Abstract: Recent advances in image generation and editing have made prompt quality a key bottleneck for e-commerce creatives.
By Jingtong Chen, Jiahui Wang, Xue Zhao, ShaoGuo Liu, Minghao Li
PinDCO is a scalable dynamic creative optimization system designed for Pinterest’s billion‑scale visual discovery platform. It uses a Creative Component Fusion Network to score ad creatives by modeling individual components (image, title, layout) with dedicated towers and fusing their representations, while a Pixel‑aware Adjustment Module tailors scores to creative size for better whole‑page outcomes. The system incorporates a lightweight pre‑selection model, caching, and dynamic batching to handle large candidate volumes, achieving a 3.09% lift in ad click‑through rate in online experiments.
By Yu Hao, Yuchun Li, Peimeng Sui, Meilin Liu, Tianyuan Cui, Hao Li, Zicong Zhou, Akanksha Baid
arXiv:2608. 04504v1 Announce Type: cross Abstract: Vision-language models excel in many multimodal tasks but remain prone to a subtle yet impactful failure mode: they tend to overestimate dominant visual-textual cues while underestimating sparse but decision-critical contextual variables.
By Shuo Liu, Huixiang Cai, Weiru Zhang, Xiaoyi Zeng
Traditional Image Aesthetic Assessment (IAA) methods mainly rely on regressing absolute Mean Opinion Scores (MOS). However, such a paradigm overlooks the inherently dynamic nature of human aesthetic perception, which relies on subconscious comparison against implicit visual references.
CommerceVibe is a system that generates e‑commerce creatives by synthesizing executable HTML/CSS code conditioned on product images, design requirements, and product information. It uses dual‑feedback reinforcement learning, combining rule‑based checks for text readability, product visibility, and layout validity with visual feedback from a vision‑language model that evaluates perceptual and commercial aspects. After fine‑tuning a large language model on 28,000 examples and applying dual‑feedback reinforcement learning, CommerceVibe achieves a weighted score of 94.0/100 on a 1,300‑case benchmark, outperforming both its SFT‑only counterpart and external models, and is validated by expert blind evaluations.
By Yajiao Xu, Jin Zhang, Jiangbo Ai, Tao Jiang, Mo Xu, Lina Huang, Chengfu Huo
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.
By Aishwarya Agrawal, Roy Hirsch, Yasumasa Onoe, Sherry Ben, Jason Baldridge
arXiv:2602. 15278v2 Announce Type: replace-cross Abstract: The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs).
By Manuel Cherep, Pranav M R, Pattie Maes, Nikhil Singh
arXiv:2610.09015v1 Announce Type: new
Abstract: Diffusion models can generate high-quality images, yet aligning their outputs with individual user preferences remains challenging. A key bottleneck is...
By Jiamu Bai, Jiaming Hu, Yanhong Wu, Zellux Wang
arXiv:2610.18860v1 Announce Type: new
Abstract: Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable,...
By Xin Gao, Yifan Yang, Xin Li, Pan Wang, Yan Li, Muzhao Tian, Mingxi Cheng, Bei Liu, Kai Qiu, Qi Dai, Dong Chen, Yue Dong, Leandros Tassiulas, Rex Ying, Ji Li, Chong Luo
The paper introduces a unified benchmark for personalized image generation that uses users' historical data—such as reviews, posts, images, captions, and metadata—to create images aligned with their lifestyle and aesthetic preferences. It defines two tasks: Personalized Scene Generation, which places objects in scenes reflecting user preferences for product presentation, and Personalized Creative Generation, which produces novel images faithful to a user's aesthetic for social media content. The authors also propose PEARL, a method that interleaves multimodal reasoning with a frozen image generator, achieving a 15% average improvement over baselines on personalization metrics.
By Bo Ni, Ngoc N. Tran, Qinwen Ge, Franck Dernoncourt, Seunghyun Yoon, Samyadeep Basu, Sungchul Kim, Puneet Mathur, Nedim Lipka, Tong Yu, Yu Wang, Ryan A. Rossi, Tyler Derr
The paper investigates inference‑time optimization of prompt embeddings for the Stable Diffusion XL Turbo model, comparing the gradient‑free Separable Covariance Matrix Adaptation Evolution Strategy (sep‑CMA‑ES) with the gradient‑based Adam optimizer. Using a weighted objective that blends LAION Aesthetic Predictor V2 and CLIPScore, the study evaluates 36 prompts under three weighting regimes (aesthetics‑only, balanced, alignment‑only). Across all settings, sep‑CMA‑ES consistently outperforms Adam in objective value, while also demonstrating favorable divergence metrics and lower compute and memory footprints, indicating its effectiveness as an inference‑time optimizer without requiring model fine‑tuning.
By Dom\'icio Pereira Neto, Jo\~ao Correia, Penousal Machado