TransPhy is a framework for visually in-context learning that focuses on physically grounded image editing. It introduces PhysVICL-74, a dataset of 74 transformation rules and 5,240 source–target pairs, and evaluates models on novel-instance transfer and unseen-rule generalization. The method predicts the demonstrated rule and a query-specific target-state description, then synthesizes the target image using token-wise mixture-of-experts guided by localized transition cues, improving rule adherence, query consistency, and generalization over existing methods.
By Siyi Xie, Xuanke Shi, Jinsheng Quan, Haoran Tang, Zukai Chen, Lei Yang, Quan Wang
arXiv:2610.34333v1 Announce Type: new
Abstract: Language models switch tasks from demonstrations, but vision still often relies on separate systems for restoration, editing, and stylization. A before...
By Yibo Zhao, Lifan Jiang, Senlin cheng, Tianrun Wu, Bolei Xu, Xiaobo Li, Can Ye, Xiaofei He, Dehai Zhao, Boxi Wu
arXiv:2608.16745v2 Announce Type: replace
Abstract: Despite remarkable progress in instruction-based video editing, textual descriptions remain limited in expressing fine-grained appearance, spatial...
By Yuji Wang, Teng Hu, Yuheng Chen, Ran Yi, Han Feng, Weijian Cao, Chengjie Wang, Lizhuang Ma, Jiangning Zhang
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
Despite progress in instruction-based video editing, unimodal textual instructions inherently struggle to convey fine-grained textures and complex dynamics. To bridge this perceptual gap, we propose Visual In-context Editing, a new paradigm elevating video editing from textual instructions to multi-modal visual guidance encompassing single image, image pair, and video pair.
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:2606. 04269v1 Announce Type: cross Abstract: Deformable object manipulation (DOM) is challenging due to high-dimensional, partially observable states that evolve through long-horizon, topology-changing interactions with multiple valid manipulation modes.
By Yilong Wang, Cheng Qian, Edward Johns
arXiv:2610.02117v1 Announce Type: cross
Abstract: On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a froze...
By Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc, Nicolas Thome, Spyros Gidaris
RADI3N is a new framework for part‑level image editing that provides parametric 3D control. It introduces a CS‑ARAP deformation module that smooths rotation discontinuities at part boundaries, and an inversion‑free selective texture transfer mechanism that reduces positional bias and enhances semantic matching. The method achieves state‑of‑the‑art results in geometric fidelity, viewpoint consistency, and controllability.
By Abdul Wasi, Akhil Gorugantu, Mahesh Bhosale, Anjan Dutta, Junsong Yuan, Sauradip Nag, David Doermann
arXiv:2609.38616v1 Announce Type: cross
Abstract: While Vision-Language-Action (VLA) models enable flexible action generation, their generalization across diverse environmental elements, including ma...
By Yanyan Zhang, Disheng Liu, Xinpeng Li, Chaoda Song, Mohsen Hariri, Debargha Ganguly, Wang Yang, Kai Ye, Bryce Grant, Vipin Chaudhary, Yu Yin
The paper introduces ProCoRe, a contrastive reinforcement learning approach that improves visual grounding in multimodal large language models (MLLMs) for in-context learning (ICL). By generating captions for contrastive image pairs, decomposing them into propositions, and optimizing contrastive rewards, ProCoRe enhances alignment with relevant visual evidence without requiring ICL-formatted training data. The method achieves significant gains in multimodal ICL classification accuracy and captioning ROUGE-L over the Qwen-VL-3-8B baseline and outperforms state‑of‑the‑art ICL‑data‑dependent methods across multiple benchmarks. Additionally, the authors present PICL, a personalized multimodal ICL captioning benchmark designed to test genuine understanding of visual demonstrations.
By Minhyuk Seo, Minjae Lee, Chaeeun Lee, Wei Lin, Muhammad Jehanzeb Mirza, Tinne Tuytelaars, Jonghyun Choi
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