The growing demand for rapid and scalable 3D asset creation has driven interest in feed-forward 3D reconstruction methods, with 3D Gaussian Splatting (3DGS) emerging as an effective scene representation. While recent approaches have demonstrated pose-free reconstruction from unposed image collections, integrating stylization or appearance control into such pipelines remains underexplored. Existing attempts largely rely on image-based conditioning, which limits both controllability and flexibility. In this work, we introduce AnyStyle, a feed-forward 3D reconstruction and stylization framework that enables pose-free, zero-shot stylization through multimodal conditioning. Our method supports both textual and visual style inputs, allowing users to control the scene appearance using natural language descriptions or reference images. We propose a modular stylization architecture that requires only minimal architectural modifications and can be integrated into existing feed-forward 3D reconstruction backbones. Experiments demonstrate that AnyStyle improves style controllability over prior feed-forward stylization methods while preserving high-quality geometric reconstruction. A user study further confirms that AnyStyle achieves superior stylization quality compared to an existing state-of-the-art approach. Repository: https://github.com/joaxkal/AnyStyle.
Capacity-Controlled Multi-View Stylization of 3D Gaussian Splatting
Guangdong Provincial Key Laboratory of Visual Media and Multidimensional Intelligence, CSSE, Shenzhen University（广东省可视媒体与多维智能重点实验室，计算机科学与软件工程学院，深圳大学）；Tencent Games（腾讯游戏）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Tel Aviv University（特拉维夫大学）
StylizedGS: Controllable Stylization for 3D Gaussian Splatting
University of Chinese Academy of Sciences（中国科学院大学）；Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences（北京移动计算与 pervasive 设备重点实验室，中国科学院计算技术研究所）；Victoria University of Wellington（维多利亚大学）；Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences（智能信息处理重点实验室，中国科学院计算技术研究所）
DReSG: Diffusion Residuals for Stylized Gaussian Splatting
School of Software Engineering, East China Normal University（华东师范大学软件工程学院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）
Geometry-Aware Style Transfer in 3D Gaussian Splatting
Department of Computer Science and Artificial Intelligence/Center for Advanced Image Information Technology, Jeonbuk National University（全北国立大学计算机科学与人工智能系/高级图像信息技术中心）；Division of Electronic and Communication Engineering, Pukyong National University（釜庆国立大学电子与通信工程系）
Tune-Your-Style: Intensity-tunable 3D Style Transfer with Gaussian Splatting
School of Electronic and Computer Engineering, Peking University, Shenzhen, China（电子与计算机工程学院，北京大学深圳校区）；Pengcheng Laboratory, Shenzhen, China（鹏城实验室）；AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, China（人工智能科学（AI4S）优选计划，北京大学深圳研究生院）；Department of Automation and BNRist, Tsinghua University, Beijing, China（自动化系和BNRist，清华大学，北京，中国）；Dalian University of Technology, China（大连理工大学）
Style4D-Bench: A Benchmark Suite for 4D Stylization
Harbin Institute of Technology（哈尔滨工业大学）；Great Bay University（大湾大学）；Vision, Graphics, and X Group（视觉、图形与X小组）；Nanjing University（南京大学）；Sun Yat-Sen University（中山大学）；Alibaba Group（阿里巴巴集团）
