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Reconstructing dynamic 4D scenes is challenging, as it requires robust disentanglement of dynamic objects from the static background. While 3D foundation models like VGGT provide accurate 3D geometry, their performance drops markedly when…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Yu Hu , Chong Cheng , Sicheng Yu , Xiaoyang Guo , Hao Wang

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

Scene-level 3D generation represents a critical frontier in multimedia and computer graphics, yet existing approaches either suffer from limited object categories or lack editing flexibility for interactive applications. In this paper, we…

Graphics · Computer Science 2025-04-18 Wenqi Dong , Bangbang Yang , Zesong Yang , Yuan Li , Tao Hu , Hujun Bao , Yuewen Ma , Zhaopeng Cui

Reconstructing dynamic 3D urban scenes is crucial for autonomous driving, yet current methods face a stark trade-off between fidelity and computational cost. This inefficiency stems from their semantically agnostic design, which allocates…

Computer Vision and Pattern Recognition · Computer Science 2025-10-15 Ying A , Wenzhang Sun , Chang Zeng , Chunfeng Wang , Hao Li , Jianxun Cui

Text-guided human motion generation has drawn significant interest because of its impactful applications spanning animation and robotics. Recently, application of diffusion models for motion generation has enabled improvements in the…

Computer Vision and Pattern Recognition · Computer Science 2023-05-17 Samaneh Azadi , Akbar Shah , Thomas Hayes , Devi Parikh , Sonal Gupta

Realtime 4D reconstruction for dynamic scenes remains a crucial challenge for autonomous driving perception. Most existing methods rely on depth estimation through self-supervision or multi-modality sensor fusion. In this paper, we propose…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Xin Fei , Wenzhao Zheng , Yueqi Duan , Wei Zhan , Masayoshi Tomizuka , Kurt Keutzer , Jiwen Lu

In this paper, we tackle a new task of 3D object synthesis, where a 3D model is composited with another object category to create a novel 3D model. However, most existing text/image/3D-to-3D methods struggle to effectively integrate…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Zeren Xiong , Zikun Chen , Zedong Zhang , Xiang Li , Ying Tai , Jian Yang , Jun Li

Generating high-fidelity, physically interactive 3D simulated tabletop scenes is essential for embodied AI -- especially for robotic manipulation policy learning and data synthesis. However, current text- or image-driven 3D scene generation…

Computer Vision and Pattern Recognition · Computer Science 2025-12-08 Ziqian Wang , Yonghao He , Licheng Yang , Wei Zou , Hongxuan Ma , Liu Liu , Wei Sui , Yuxin Guo , Hu Su

We introduce a method to generate 3D scenes that are disentangled into their component objects. This disentanglement is unsupervised, relying only on the knowledge of a large pretrained text-to-image model. Our key insight is that objects…

Computer Vision and Pattern Recognition · Computer Science 2024-02-28 Dave Epstein , Ben Poole , Ben Mildenhall , Alexei A. Efros , Aleksander Holynski

World models that support controllable and editable spatiotemporal environments are valuable for robotics, enabling scalable training data, repro ducible evaluation, and flexible task design. While recent text-to-video models generate…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Xuehai He , Shijie Zhou , Thivyanth Venkateswaran , Kaizhi Zheng , Ziyu Wan , Achuta Kadambi , Xin Eric Wang

In this paper, we introduce \textbf{DimensionX}, a framework designed to generate photorealistic 3D and 4D scenes from just a single image with video diffusion. Our approach begins with the insight that both the spatial structure of a 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-11-08 Wenqiang Sun , Shuo Chen , Fangfu Liu , Zilong Chen , Yueqi Duan , Jun Zhang , Yikai Wang

Generating 3D faces from textual descriptions has a multitude of applications, such as gaming, movie, and robotics. Recent progresses have demonstrated the success of unconditional 3D face generation and text-to-3D shape generation.…

Computer Vision and Pattern Recognition · Computer Science 2023-09-01 Cuican Yu , Guansong Lu , Yihan Zeng , Jian Sun , Xiaodan Liang , Huibin Li , Zongben Xu , Songcen Xu , Wei Zhang , Hang Xu

Recently, text-to-image generation has exhibited remarkable advancements, with the ability to produce visually impressive results. In contrast, text-to-3D generation has not yet reached a comparable level of quality. Existing methods…

Computer Vision and Pattern Recognition · Computer Science 2023-08-21 Yukang Cao , Yan-Pei Cao , Kai Han , Ying Shan , Kwan-Yee K. Wong

We propose a diffusion-based approach for Text-to-Image (T2I) generation with interactive 3D layout control. Layout control has been widely studied to alleviate the shortcomings of T2I diffusion models in understanding objects' placement…

Computer Vision and Pattern Recognition · Computer Science 2024-08-28 Abdelrahman Eldesokey , Peter Wonka

Emerging Metaverse applications demand accessible, accurate, and easy-to-use tools for 3D digital human creations in order to depict different cultures and societies as if in the physical world. Recent large-scale vision-language advances…

Graphics · Computer Science 2023-04-07 Longwen Zhang , Qiwei Qiu , Hongyang Lin , Qixuan Zhang , Cheng Shi , Wei Yang , Ye Shi , Sibei Yang , Lan Xu , Jingyi Yu

This paper introduces the first text-guided work for generating the sequence of hand-object interaction in 3D. The main challenge arises from the lack of labeled data where existing ground-truth datasets are nowhere near generalizable in…

Computer Vision and Pattern Recognition · Computer Science 2024-04-03 Junuk Cha , Jihyeon Kim , Jae Shin Yoon , Seungryul Baek

Cinematic video production requires control over scene-subject composition and camera movement, but live-action shooting remains costly due to the need for constructing physical sets. To address this, we introduce the task of cinematic…

Computer Vision and Pattern Recognition · Computer Science 2026-02-09 Kaiyi Huang , Yukun Huang , Yu Li , Jianhong Bai , Xintao Wang , Zinan Lin , Xuefei Ning , Jiwen Yu , Pengfei Wan , Yu Wang , Xihui Liu

Reconstructing dynamic 3D scenes with photorealistic detail and strong temporal coherence remains a significant challenge. Existing Gaussian splatting approaches for dynamic scene modeling often rely on per-frame optimization, which can…

Computer Vision and Pattern Recognition · Computer Science 2026-03-13 Tingxuan Huang , Haowei Zhu , Jun-hai Yong , Hao Pan , Bin Wang

The generation of 3D scenes from user-specified conditions offers a promising avenue for alleviating the production burden in 3D applications. Previous studies required significant effort to realize the desired scene, owing to limited…

Computer Vision and Pattern Recognition · Computer Science 2024-11-28 Takayuki Hara , Tatsuya Harada

With the rapid advancement of 3D representation techniques and generative models, substantial progress has been made in reconstructing full-body 3D avatars from a single image. However, this task remains fundamentally ill-posedness due to…

Computer Vision and Pattern Recognition · Computer Science 2025-09-17 Gaofeng Liu , Hengsen Li , Ruoyu Gao , Xuetong Li , Zhiyuan Ma , Tao Fang
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