Hugging Face Trending Papers
TransPhy: Visual In-Context Learning for Physically Grounded Image Editing
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arXiv AI
Aug 26
TransPhy: Visual In-Context Learning for Physically Grounded Image Editing
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.
arXiv Computer Vision
Oct 27
CrossPair: Unlocking Generalist Visual Transformation Learning in Image Editors
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...
arXiv Computer Vision
2d ago
VicEdit: Learning to Edit Videos from Visual In-Context Examples
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...
arXiv Computer Vision
Aug 27
FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Generation and Modality Mimicry
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...
Hugging Face Trending Papers
Aug 17
VicEdit: Learning to Edit Videos from Visual In-Context Examples
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.
arXiv AI
Oct 9
VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling
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.