arXiv Computer Vision
Oct 28

Abstraction in Style: Beyond Texture and Color

The paper introduces Abstraction in Style (AiS), a generative framework that separates structural abstraction from visual stylization. AiS first creates an abstraction proxy that reinterprets a target image’s structure according to the abstraction logic of a given style, then renders this proxy to produce a stylized output. By learning transformations from visual exemplars without explicit geometric supervision, AiS enables a broader range of stylistic transformations, improves controllability, and allows more expressive stylization.

By Min Lu, Yuanfeng He, Anthony Chen, Jianhuang He, Pu Wang, Daniel Cohen-Or, Hui Huang
arXiv Computer Vision
Sep 15

MorphoStyle: Motion Style Transfer with Morphology Control

MorphoStyle is a new framework for shape‑aware motion style transfer that uses a shape‑conditioned FSQ‑VAE. It disentangles style from content through a contrastive style encoder, a text‑guided style‑routing mechanism, and a manifold‑preserving style modulator. Experiments on benchmark datasets show that MorphoStyle outperforms existing baselines in both shape control and motion style transfer.

By Xin Feng, Eleonora D'Arnese, Mohan Sridharan
arXiv Computer Vision
Sep 23

GeoComposer: Geometry-Grounded Photographic Composition Instruction

GeoComposer is a new framework that improves photographic composition by generating textual guidance and visual exemplars for a given image. It uses a geometry-aware representation learning mechanism that incorporates geometric priors from a visual geometry foundation model to maintain global structure and fine-grained correspondences. A reinforcement learning strategy with a hybrid reward optimizes instruction following, aesthetic quality, and geometric consistency, leading to superior results compared to state‑of‑the‑art methods.

By Shuangzhi Li, Qiaoqiao Jia, Xingxin Chen, Guile Wu, Dongfeng Bai