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3D animation is central to modern visual media, yet traditional production pipelines remain labor-intensive, expertise-demanding, and computationally expensive. Recent AIGC-based approaches partially automate asset creation and rigging, but…

Computer Vision and Pattern Recognition · Computer Science 2025-12-22 Yupeng Zhu , Xiongzhen Zhang , Ye Chen , Bingbing Ni

While generative artificial intelligence has advanced significantly across text, image, audio, and video domains, 3D generation remains comparatively underdeveloped due to fundamental challenges such as data scarcity, algorithmic…

Computer Vision and Pattern Recognition · Computer Science 2025-05-13 Weiyu Li , Xuanyang Zhang , Zheng Sun , Di Qi , Hao Li , Wei Cheng , Weiwei Cai , Shihao Wu , Jiarui Liu , Zihao Wang , Xiao Chen , Feipeng Tian , Jianxiong Pan , Zeming Li , Gang Yu , Xiangyu Zhang , Daxin Jiang , Ping Tan

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Shengqu Cai , Duygu Ceylan , Matheus Gadelha , Chun-Hao Paul Huang , Tuanfeng Yang Wang , Gordon Wetzstein

3D content creation has long been a complex and time-consuming process, often requiring specialized skills and resources. While recent advancements have allowed for text-guided 3D object and scene generation, they still fall short of…

Computer Vision and Pattern Recognition · Computer Science 2024-08-06 Xingyi Li , Yizheng Wu , Jun Cen , Juewen Peng , Kewei Wang , Ke Xian , Zhe Wang , Zhiguo Cao , Guosheng Lin

Recently, generating 3D assets with the control of condition images has achieved impressive quality. However, existing 3D generation methods are limited to handling a single control objective and lack the ability to utilize multiple images…

Graphics · Computer Science 2026-02-20 Xuancheng Jin , Rengan Xie , Wenting Zheng , Rui Wang , Hujun Bao , Yuchi Huo

Diffusion models have achieved great success in generating 2D images. However, the quality and generalizability of 3D content generation remain limited. State-of-the-art methods often require large-scale 3D assets for training, which are…

Graphics · Computer Science 2025-03-24 Jiantao Lin , Xin Yang , Meixi Chen , Yingjie Xu , Dongyu Yan , Leyi Wu , Xinli Xu , Lie XU , Shunsi Zhang , Ying-Cong Chen

3D object generation has undergone significant advancements, yielding high-quality results. However, fall short of achieving precise user control, often yielding results that do not align with user expectations, thus limiting their…

Graphics · Computer Science 2024-04-26 Shaocong Dong , Lihe Ding , Zhanpeng Huang , Zibin Wang , Tianfan Xue , Dan Xu

Generative methods for 3D assets have recently achieved remarkable progress, yet providing intuitive and precise control over the object geometry remains a key challenge. Existing approaches predominantly rely on text or image prompts,…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Elisabetta Fedele , Francis Engelmann , Ian Huang , Or Litany , Marc Pollefeys , Leonidas Guibas

Recent breakthroughs in 3D generation have enabled the synthesis of high-fidelity individual assets. However, generating 3D compositional objects from single images--particularly under occlusions--remains challenging. Existing methods often…

Computer Vision and Pattern Recognition · Computer Science 2026-03-18 Hui Shan , Keyang Luo , Ming Li , Sizhe Zheng , Yanwei Fu , Zhen Chen , Xiangru Huang

Recent advances in deep generative modeling have unlocked unprecedented opportunities for video synthesis. In real-world applications, however, users often seek tools to faithfully realize their creative editing intentions with precise and…

Computer Vision and Pattern Recognition · Computer Science 2025-09-29 Yuhao Liu , Tengfei Wang , Fang Liu , Zhenwei Wang , Rynson W. H. Lau

Recently, 3D generation methods have shown their powerful ability to automate 3D model creation. However, most 3D generation methods only rely on an input image or a text prompt to generate a 3D model, which lacks the control of each…

Computer Vision and Pattern Recognition · Computer Science 2025-07-08 Peng Li , Suizhi Ma , Jialiang Chen , Yuan Liu , Congyi Zhang , Wei Xue , Wenhan Luo , Alla Sheffer , Wenping Wang , Yike Guo

Recent advances in 3D-native generative models have accelerated asset creation for games, film, and design. However, most methods still rely primarily on image or text conditioning and lack fine-grained, cross-modal controls, which limits…

Recently, the impressive generative capabilities of diffusion models have been demonstrated, producing images with remarkable fidelity. Particularly, existing methods for the 3D object generation tasks, which is one of the fastest-growing…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Jaeseok Lee , Jaekoo Lee

In the realm of digital creativity, our potential to craft intricate 3D worlds from imagination is often hampered by the limitations of existing digital tools, which demand extensive expertise and efforts. To narrow this disparity, we…

Computer Vision and Pattern Recognition · Computer Science 2024-06-21 Longwen Zhang , Ziyu Wang , Qixuan Zhang , Qiwei Qiu , Anqi Pang , Haoran Jiang , Wei Yang , Lan Xu , Jingyi Yu

Text- or image-to-3D generators and 3D scanners can now produce 3D assets with high-quality shapes and textures. These assets typically consist of a single, fused representation, like an implicit neural field, a Gaussian mixture, or a mesh,…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Minghao Chen , Roman Shapovalov , Iro Laina , Tom Monnier , Jianyuan Wang , David Novotny , Andrea Vedaldi

Manually creating 3D environments for AR/VR applications is a complex process requiring expert knowledge in 3D modeling software. Pioneering works facilitate this process by generating room meshes conditioned on textual style descriptions.…

Computer Vision and Pattern Recognition · Computer Science 2023-12-11 Jonas Schult , Sam Tsai , Lukas Höllein , Bichen Wu , Jialiang Wang , Chih-Yao Ma , Kunpeng Li , Xiaofang Wang , Felix Wimbauer , Zijian He , Peizhao Zhang , Bastian Leibe , Peter Vajda , Ji Hou

As 3D generation techniques continue to flourish, the demand for generating personalized content is rapidly rising. Users increasingly seek to apply various editing methods to polish generated 3D content, aiming to enhance its color, style,…

Computer Vision and Pattern Recognition · Computer Science 2025-08-12 Weitao Wang , Haoran Xu , Jun Meng , Haoqian Wang

Sketches serve as fundamental blueprints in artistic creation because sketch editing is easier and more intuitive than pixel-level RGB image editing for painting artists, yet sketch generation remains unexplored despite advancements in…

Computer Vision and Pattern Recognition · Computer Science 2025-04-14 Ruohao Zhan , Yijin Li , Yisheng He , Shuo Chen , Yichen Shen , Xinyu Chen , Zilong Dong , Zhaoyang Huang , Guofeng Zhang

In this work, we present a novel framework built to simplify 3D asset generation for amateur users. To enable interactive generation, our method supports a variety of input modalities that can be easily provided by a human, including…

Computer Vision and Pattern Recognition · Computer Science 2023-03-23 Yen-Chi Cheng , Hsin-Ying Lee , Sergey Tulyakov , Alexander Schwing , Liangyan Gui

Three-dimensional content generation has progressed from producing isolated, visually plausible shapes to constructing structured assets that can be deployed in real-time interactive environments. This trajectory is driven by converging…

Graphics · Computer Science 2026-05-12 Jiafeng Wu , Zhuofan Lou , Jian Liu , Dazhao Du , Chunchao Guo , Song Guo
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