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Although recent 3D-native generators have made great progress in synthesizing reliable geometry, they still fall short in achieving realistic appearances. A key obstacle lies in the lack of diverse and high-quality real-world 3D assets with…

Computer Vision and Pattern Recognition · Computer Science 2026-03-09 Xinyue Liang , Zhinyuan Ma , Lingchen Sun , Yanjun Guo , Lei Zhang

Generative models for high-quality materials are particularly desirable to make 3D content authoring more accessible. However, the majority of material generation methods are trained on synthetic data. Synthetic data provides precise…

Previous works leveraging video models for image-to-3D scene generation tend to suffer from geometric distortions and blurry content. In this paper, we renovate the pipeline of image-to-3D scene generation by unlocking the potential of…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Yuhao Wan , Lijuan Liu , Jingzhi Zhou , Zihan Zhou , Xuying Zhang , Dongbo Zhang , Shaohui Jiao , Qibin Hou , Ming-Ming Cheng

Video generation is experiencing rapid growth, driven by advances in diffusion models and the development of better and larger datasets. However, producing high-quality videos remains challenging due to the high-dimensional data and the…

Computer Vision and Pattern Recognition · Computer Science 2025-04-10 Elia Peruzzo , Dejia Xu , Xingqian Xu , Humphrey Shi , Nicu Sebe

Recent developments in generative models and large-scale datasets have substantially advanced 3D world generation, facilitating a broad range of domains including spatial intelligence, embodied intelligence, and autonomous driving. While…

Computer Vision and Pattern Recognition · Computer Science 2026-05-14 Hanxin Zhu , Cong Wang , Peiyan Tu , Jiayi Luo , Tianyu He , Xin Jin , Zhibo Chen

We present a novel video generation framework that integrates 3-dimensional geometry and dynamic awareness. To achieve this, we augment 2D videos with 3D point trajectories and align them in pixel space. The resulting 3D-aware video…

Computer Vision and Pattern Recognition · Computer Science 2025-10-24 Yunuo Chen , Junli Cao , Vidit Goel , Sergei Korolev , Chenfanfu Jiang , Jian Ren , Sergey Tulyakov , Anil Kag

We introduce RealmDreamer, a technique for generating forward-facing 3D scenes from text descriptions. Our method optimizes a 3D Gaussian Splatting representation to match complex text prompts using pretrained diffusion models. Our key…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Jaidev Shriram , Alex Trevithick , Lingjie Liu , Ravi Ramamoorthi

Understanding and predicting dynamics of the physical world can enhance a robot's ability to plan and interact effectively in complex environments. While recent video generation models have shown strong potential in modeling dynamic scenes,…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Zeyi Liu , Shuang Li , Eric Cousineau , Siyuan Feng , Benjamin Burchfiel , Shuran Song

Video generation has achieved remarkable progress with the introduction of diffusion models, which have significantly improved the quality of generated videos. However, recent research has primarily focused on scaling up model training,…

Computer Vision and Pattern Recognition · Computer Science 2025-01-16 Chenyang Si , Weichen Fan , Zhengyao Lv , Ziqi Huang , Yu Qiao , Ziwei Liu

Real-world data collection for robotics is costly and resource-intensive, requiring skilled operators and expensive hardware. Simulations offer a scalable alternative but often fail to achieve sim-to-real generalization due to geometric and…

Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture meaningful geometric-aware structure in their learned…

Computer Vision and Pattern Recognition · Computer Science 2026-05-06 Haoyu Wu , Diankun Wu , Tianyu He , Junliang Guo , Yang Ye , Yueqi Duan , Jiang Bian

The rapid advancement of diffusion models holds the promise of revolutionizing the application of VR and AR technologies, which typically require scene-level 4D assets for user experience. Nonetheless, existing diffusion models…

Computer Vision and Pattern Recognition · Computer Science 2025-05-14 Haiyang Zhou , Wangbo Yu , Jiawen Guan , Xinhua Cheng , Yonghong Tian , Li Yuan

In recent years, video generation has seen significant advancements. However, challenges still persist in generating complex motions and interactions. To address these challenges, we introduce ReVision, a plug-and-play framework that…

Computer Vision and Pattern Recognition · Computer Science 2026-01-12 Qihao Liu , Ju He , Qihang Yu , Liang-Chieh Chen , Alan Yuille

Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond their creative potential, such models also hold promise as world simulators for robotics and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 David Romero , Ariana Bermudez , Viacheslav Iablochnikov , Hao Li , Fabio Pizzati , Ivan Laptev

Recent advances in diffusion models enable high-quality video generation and editing, but precise relighting with consistent video contents, which is critical for shaping scene atmosphere and viewer attention, remains unexplored. Mainstream…

Computer Vision and Pattern Recognition · Computer Science 2025-11-11 Weikang Bian , Xiaoyu Shi , Zhaoyang Huang , Jianhong Bai , Qinghe Wang , Xintao Wang , Pengfei Wan , Kun Gai , Hongsheng Li

Generating high-fidelity upper-body 3D avatars from one-shot input image remains a significant challenge. Current 3D avatar generation methods, which rely on large reconstruction models, are fast and capable of producing stable body…

Computer Vision and Pattern Recognition · Computer Science 2025-12-10 Fan Yang , Heyuan Li , Peihao Li , Weihao Yuan , Lingteng Qiu , Chaoyue Song , Cheng Chen , Yisheng He , Shifeng Zhang , Xiaoguang Han , Steven Hoi , Guosheng Lin

Creating photorealistic materials for 3D rendering requires exceptional artistic skill. Generative models for materials could help, but are currently limited by the lack of high-quality training data. While recent video generative models…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Bowen Xue , Saeed Hadadan , Zheng Zeng , Fabrice Rousselle , Zahra Montazeri , Milos Hasan

We propose 4Real-Video, a novel framework for generating 4D videos, organized as a grid of video frames with both time and viewpoint axes. In this grid, each row contains frames sharing the same timestep, while each column contains frames…

Computer Vision and Pattern Recognition · Computer Science 2024-12-06 Chaoyang Wang , Peiye Zhuang , Tuan Duc Ngo , Willi Menapace , Aliaksandr Siarohin , Michael Vasilkovsky , Ivan Skorokhodov , Sergey Tulyakov , Peter Wonka , Hsin-Ying Lee

Photorealism is an important aspect of modern video games since it can shape player experience and impact immersion, narrative engagement, and visual fidelity. To achieve photorealism, beyond traditional rendering pipelines, generative…

Computer Vision and Pattern Recognition · Computer Science 2026-02-10 Stefanos Pasios , Nikos Nikolaidis

Automatically generating a complete 3D scene from a text description, a reference image, or both has significant applications in fields like virtual reality and gaming. However, current methods often generate low-quality textures and…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Zhexiao Xiong , Zhang Chen , Zhong Li , Yi Xu , Nathan Jacobs