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Related papers: Recent Advances in 3D Object and Scene Generation:…

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This paper presents Omni-View, which extends the unified multimodal understanding and generation to 3D scenes based on multiview images, exploring the principle that "generation facilitates understanding". Consisting of understanding model,…

Computer Vision and Pattern Recognition · Computer Science 2026-02-02 JiaKui Hu , Shanshan Zhao , Qing-Guo Chen , Xuerui Qiu , Jialun Liu , Zhao Xu , Weihua Luo , Kaifu Zhang , Yanye Lu

Generating immersive 3D scenes from texts is a core task in computer vision, crucial for applications in virtual reality and game development. Despite the promise of leveraging 2D diffusion priors, existing methods suffer from spatial…

Computer Vision and Pattern Recognition · Computer Science 2026-01-28 Jisheng Chu , Wenrui Li , Rui Zhao , Wangmeng Zuo , Shifeng Chen , Xiaopeng Fan

Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing Artificial Intelligence for General Creativity (AIGC).…

Artificial Intelligence · Computer Science 2023-12-27 Hanqun Cao , Cheng Tan , Zhangyang Gao , Yilun Xu , Guangyong Chen , Pheng-Ann Heng , Stan Z. Li

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

The demand for efficient 3D model generation techniques has grown exponentially, as manual creation of 3D models is time-consuming and requires specialized expertise. While generative models have shown potential in creating 3D textured…

Computer Vision and Pattern Recognition · Computer Science 2023-07-28 Fanghua Yu , Xintao Wang , Zheyuan Li , Yan-Pei Cao , Ying Shan , Chao Dong

3D scene generation conditioned on text prompts has significantly progressed due to the development of 2D diffusion generation models. However, the textual description of 3D scenes is inherently inaccurate and lacks fine-grained control…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Minglin Chen , Longguang Wang , Sheng Ao , Ye Zhang , Kai Xu , Yulan Guo

Automatically generating high-quality real world 3D scenes is of enormous interest for applications such as virtual reality and robotics simulation. Towards this goal, we introduce NeuralField-LDM, a generative model capable of synthesizing…

Computer Vision and Pattern Recognition · Computer Science 2023-04-20 Seung Wook Kim , Bradley Brown , Kangxue Yin , Karsten Kreis , Katja Schwarz , Daiqing Li , Robin Rombach , Antonio Torralba , Sanja Fidler

This report reviews recent advancements in human motion prediction, reconstruction, and generation. Human motion prediction focuses on forecasting future poses and movements from historical data, addressing challenges like nonlinear…

Computer Vision and Pattern Recognition · Computer Science 2025-02-25 Canxuan Gang , Yiran Wang

Deep generative models have achieved great success in areas such as image, speech, and natural language processing in the past few years. Thanks to the advances in graph-based deep learning, and in particular graph representation learning,…

Machine Learning · Computer Science 2021-01-01 Faezeh Faez , Yassaman Ommi , Mahdieh Soleymani Baghshah , Hamid R. Rabiee

The rise of non-linear and interactive media such as video games has increased the need for automatic movement animation generation. In this survey, we review and analyze different aspects of building automatic movement generation systems…

Machine Learning · Computer Science 2019-03-21 Omid Alemi , Philippe Pasquier

Recent advances in diffusion models have revolutionized 2D and 3D content creation, yet generating photorealistic dynamic 4D scenes remains a significant challenge. Existing dynamic 4D generation methods typically rely on distilling…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Vinayak Gupta , Yunze Man , Yu-Xiong Wang

3D scene generation is in high demand across various domains, including virtual reality, gaming, and the film industry. Owing to the powerful generative capabilities of text-to-image diffusion models that provide reliable priors, the…

Computer Vision and Pattern Recognition · Computer Science 2024-07-23 Haiyang Zhou , Xinhua Cheng , Wangbo Yu , Yonghong Tian , Li Yuan

We consider the problem of scaling deep generative shape models to high-resolution. Drawing motivation from the canonical view representation of objects, we introduce a novel method for the fast up-sampling of 3D objects in voxel space…

Computer Vision and Pattern Recognition · Computer Science 2018-11-16 Edward Smith , Scott Fujimoto , David Meger

Acquiring detailed 3D scenes typically demands costly equipment, multi-view data, or labor-intensive modeling. Therefore, a lightweight alternative, generating complex 3D scenes from a single top-down image, plays an essential role in…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Kaizhi Zheng , Ruijian Zha , Zishuo Xu , Jing Gu , Jie Yang , Xin Eric Wang

Humans intuitively perceive object shape and orientation from a single image, guided by strong priors about canonical poses. However, existing 3D generative models often produce misaligned results due to inconsistent training data, limiting…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Yichong Lu , Yuzhuo Tian , Zijin Jiang , Yikun Zhao , Yuanbo Yang , Hao Ouyang , Haoji Hu , Huimin Yu , Yujun Shen , Yiyi Liao

Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale realscanned 3D databases. To facilitate the development of 3D perception, reconstruction, and generation in the real world, we propose…

Computer Vision and Pattern Recognition · Computer Science 2023-04-12 Tong Wu , Jiarui Zhang , Xiao Fu , Yuxin Wang , Jiawei Ren , Liang Pan , Wayne Wu , Lei Yang , Jiaqi Wang , Chen Qian , Dahua Lin , Ziwei Liu

Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model…

Computer Vision and Pattern Recognition · Computer Science 2021-08-17 Rundi Wu , Chang Xiao , Changxi Zheng

World building with 3D scene representations is increasingly important for content creation, simulation, and interactive experiences, yet real workflows are inherently iterative: creators must repeatedly extend an existing scene under user…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Zijian He , Renjie Liu , Yihao Wang , Weizhi Zhong , Huan Yuan , Kun Gai , Guangrun Wang , Guanbin Li

Autonomous driving, in recent years, has been receiving increasing attention for its potential to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving pipelines, the perception system is an indispensable…

Computer Vision and Pattern Recognition · Computer Science 2023-04-05 Jiageng Mao , Shaoshuai Shi , Xiaogang Wang , Hongsheng Li

This report presents a comprehensive framework for generating high-quality 3D shapes and textures from diverse input prompts, including single images, multi-view images, and text descriptions. The framework consists of 3D shape generation…