Beyond Patches: Global-aware Autoregressive Model for Multimodal Few-Shot Font Generation
Wangxuan Institute of Computer Technology, Peking University（北京大学王学轩计算机技术研究所）；School of Electronics Engineering and Computer Science, Peking University（北京大学电子工程与计算机科学学院）
Manual font design is an intricate process that transforms a stylistic visual concept into a coherent glyph set. This challenge persists in automated Few-shot Font Generation (FFG), where models often struggle to preserve both the structural integrity and stylistic fidelity from limited references. While autoregressive (AR) models have demonstrated impressive generative capabilities, their application to FFG is constrained by conventional patch-level tokenization, which neglects global dependencies crucial for coherent font synthesis. Moreover, existing FFG methods remain within the image-to-image paradigm, relying solely on visual references and overlooking the role of language in conveying stylistic intent during font design. To address these limitations, we propose GAR-Font, a novel AR framework for multimodal few-shot font generation. GAR-Font introduces a global-aware tokenizer that effectively captures both local structures and global stylistic patterns, a multimodal style encoder offering flexible style control through a lightweight language-style adapter without requiring intensive multimodal pretraining, and a post-refinement pipeline that further enhances structural fidelity and style coherence. Extensive experiments show that GAR-Font outperforms existing FFG methods, excelling in maintaining global style faithfulness and achieving higher-quality results with textual stylistic guidance.
Microsoft Research Asia（微软亚洲研究院）；Peking University（北京大学）；Microsoft Azure AI（微软Azure人工智能）
In this paper, we present NUWA-Infinity, a generative model for infinite visual synthesis, which is defined as the task of generating arbitrarily-sized high-resolution images or long-duration videos. An autoregressive over autoregressive generation mechanism is proposed to deal with this variable-size generation task, where a global patch-level autoregressive model considers the dependencies between patches, and a local token-level autoregressive model considers dependencies between visual tokens within each patch. A Nearby Context Pool (NCP) is introduced to cache-related patches already gene…
Yonsei University（延世大学）；Korea Institute of Science and Technology（韩国科学技术院）；LG AI Research（LG人工智能研究）；University of Michigan（密歇根大学）；Seoul National University（首尔国立大学）
BrepGPT: Autoregressive B-rep Generation with Voronoi Half-Patch
MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS）；University of Chinese Academy of Sciences（中国科学院大学）；King Abdullah University of Science and Technology（国王 Abdullah 科学与技术大学）
ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based Upsampling
TU Wien, Center for Geometry and Computational Design（维也纳技术大学，几何与计算设计中心）；University of Innsbruck, Department of Design, i.sd | Structure and Design（因斯布鲁克大学，设计系，i.sd | 结构与设计）
