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
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:2608.29905v1 Announce Type: new
Abstract: Text-guided style editing of 3D assets is essential for adapting existing objects to diverse visual aesthetics in digital content creation. Despite rap...
By Tomohiro Aizawa, Shigeru Kuriyama, Chunzhi Gu
arXiv:2606. 05261v1 Announce Type: cross Abstract: Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size.
By Nadav Benedek, Ariel Shamir, Ohad Fried
arXiv:2608.25461v1 Announce Type: cross
Abstract: Using conditional image generators, texture artists can explore many single-view looks for an existing 3D shape. Despite impressive progress, state-o...
By Chenyue Cai, Anita Hu, James Lucas, Szymon Rusinkiewicz, Masha Shugrina
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
Artistic image synthesis aims to recreate the expressive visual identity of a target artist, yet existing methods often fail to capture an artist's global style. Conventional style transfer methods transfer the style of one or a few reference artworks to a content image in a One-to-One manner, making them effective for artwork-level stylization but limited in representing the broader stylistic distribution of an artist.
arXiv:2502. 08884v3 Announce Type: replace-cross Abstract: We present ShapeLib, the first method that uses the priors of Large Language Models (LLMs) to design libraries of programmatic 3D shape abstractions.
By R. Kenny Jones, Paul Guerrero, Niloy J. Mitra, Daniel Ritchie
Image style is a highly abstract, human-constructed concept shaped by a range of visual factors and intrinsically entangled with content, yet a unified and explicit definition of image style remains l...
MegaStyle++ introduces a hierarchical definition of image style, ranging from overall style identity to fine‑grained visual attributes, to provide a more structured, transferable, and interpretable representation. Using this definition, the authors refined the MegaStyle annotation pipeline and released MegaStyle++‑8M, a dataset with 150K style identities, 1M fine‑grained prompts, and 8M stylized images. Analyses show that the hierarchical approach expands style diversity and semantic breadth while accurately capturing the intrinsic visual style of reference images.
By Junyao Gao, Sibo Liu, Jiaxing Li, Yanan Sun, Weidong Zhang, Cairong Zhao, Jun Zhang
SpaceFlow is a training‑free pipeline that enables locally controllable 3D generation from text and geometric primitives. Each primitive acts as a proxy for an object part and receives a local control level, allowing users to dictate strict adherence to input shapes or permit generative completion in different regions. The method enforces spatial constraints during structure generation and matches appearance to primitives, limiting cross‑part leakage and achieving state‑of‑the‑art prompt faithfulness and material accuracy.
By Neil De La Fuente, Joan Lafuente, Mukhammadali Sayfiddinov, Felicia Scharitzer, Marc Pollefeys, Ata Celen, Sayan Deb Sarkar, Elisabetta Fedele
SemEllipsoid introduces a semantic‑aware shape abstraction method that uses ellipsoidal primitives to represent 3D shapes. Unlike prior geometry‑only approaches, it enforces that each primitive lies entirely within a single semantic part by incorporating semantic constraints derived from large‑scale foundation models. The fully differentiable projection‑blending‑evaluation framework allows iterative optimization of both primitive placement and semantic assignment without requiring training data, and the method supports adjustable abstraction levels across diverse shape categories.
By Kuankuan Cheng, Haocheng Yuan, Changjian Li