Loomis Painter is a framework that learns painting processes by training on reversed image‑to‑video sequences, preserving reference‑attention behavior and avoiding long‑range dependencies that disrupt pretrained models. It introduces a media‑transfer strategy to decouple input appearance from the target process, enabling generation across different artistic media. Evaluated on a curated dataset of real‑world painting processes, the method outperforms baselines on LPIPS, DINO, and CLIP metrics and receives consistent preference in expert and non‑expert user studies.
By Markus Pobitzer, Chang Liu, Chenyi Zhuang, Teng Long, Bin Ren, Nicu Sebe
arXiv:2605.20941v2 Announce Type: replace
Abstract: Existing neural painting methods are target-driven: given a reference image, strokes are optimized to reconstruct it, fixing the outcome before pai...
By Yunge Wen, Yaluo Wang, Yuancheng Shen, Robert Krueger, Paul Pu Liang
arXiv:2606. 00188v1 Announce Type: cross Abstract: While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle.
By Kai Xu, Ellis Brown, Shrikar Madhu, Rob Fergus, He He, Saining Xie
VINCIE‑NExT is a unified framework that transfers image editing capabilities to video editing by using in‑context visual demonstrations. It decomposes video editing into a chain of sub‑tasks (Video → Image → Image → Video), routing editing intent through the image domain and enabling joint training from heterogeneous image and video data under a single diffusion objective. The method introduces a novel position encoding to link image demonstrations and video frames, allowing pixel‑faithful appearance propagation, and supports test‑time scaling through progressive diffusion stages.
By Leigang Qu, Feng Cheng, Ziyan Yang, Bangbang Yang, Zhaoyang Huang, Wei Chow, Yicong Li, Wenjie Wang, Tat-Seng Chua, Yan Zeng
arXiv:2607.18227v2 Announce Type: replace
Abstract: In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and imag...
By Dingyun Zhang, Lixue Gong, Wei Liu
arXiv:2605. 09233v2 Announce Type: replace-cross Abstract: Recent advances in visual generative models have enabled high-fidelity image editing guided by human instructions.
By Zilai Zeng, Mingdeng Cao, Zijie Li, Xiaochen Lian, Yichun Shi, Peihao Zhu, Chen Sun, Peng Wang
arXiv:2610.34303v1 Announce Type: new
Abstract: Recent advances in image editing have enabled fine-grained and highly controllable visual modifications. However, achieving similar levels of controlla...
By Guojun Lei, Hong Li, Hongbing Yang, Lixue Gong, Chi Wang, Zheng Dong
arXiv:2610.14253v2 Announce Type: replace
Abstract: Slider-based control aims to give users continuous, fine-grained control over the intensity of individual edits in instruction-based image editing....
By Arman Zarei, Sayan Nag, Bryan Wang, Franck Dernoncourt, Varun Manjunatha, Nedim Lipka, Soheil Feizi, Samyadeep Basu
Generative Cinematographer (GenCine) lifts a single image into an editable 3D scene scaffold, allowing artists to jointly author camera paths and foreground motion using local 3D motion handles. The system projects these controls into guidance maps that encode handle positions and colors across frames, enabling a pretrained video model to follow camera-relative, piecewise-rigid motion without physics simulation. Training recovers controls from real videos and synthetic ground-truth geometry, and experiments demonstrate consistent camera-relative motion, improved geometric consistency, and strong controllability across diverse real-world scenes.
By Jiahan Zhang, Chaohao Yang, Namitha Guruprasad, Vivekjyoti Banerjee, Trong-Tung Nguyen, Alan Yuille, Anand Bhattad
arXiv:2607. 05465v1 Announce Type: cross Abstract: Complex image creation and editing often require more than a single generation or editing model.
By Hairui Zhu, Yiying Yang, Tengjin Weng, Ziyu Lu, Xiao Yao, Xiaoyang Ye, Lin Ma, Wenhao Jiang
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
CameraEditor is a new framework that transforms camera-controlled image editing into a temporal sequence prediction problem. By using video diffusion models, it incorporates a geometric perception module and dynamic reference routing to create precise visual references through dynamic panorama cropping. The method also inserts intermediate transition frames to handle large perspective shifts, maintaining content identity and spatial coherence, and is evaluated on a dataset of 5,760 instances with a benchmark of 462 test cases, achieving state‑of‑the‑art performance.
By Xin Shen, Chengyou Jia, Keshuo Xing, Zifeng Zhu, Changliang Xia, Bowen Ping, Zhuohang Dang, Hangwei Qian, Minnan Luo