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High-fidelity visual reconstruction and novel-view synthesis are essential for realistic closed-loop evaluation in autonomous driving. While 4D Gaussian Splatting (4DGS) offers a promising balance of accuracy and efficiency, existing…

Computer Vision and Pattern Recognition · Computer Science 2026-03-10 Haibao Yu , Kuntao Xiao , Jiahang Wang , Ruiyang Hao , Yuxin Huang , Guoran Hu , Haifang Qin , Bowen Jing , Yuntian Bo , Ping Luo

Reconstructing dynamic 3D scenes from monocular input is fundamentally under-constrained, with ambiguities arising from occlusion and extreme novel views. While dynamic Gaussian Splatting offers an efficient representation, vanilla models…

Computer Vision and Pattern Recognition · Computer Science 2026-03-02 Fengzhi Guo , Chih-Chuan Hsu , Sihao Ding , Cheng Zhang

Recent advancements in 3D content generation from text or a single image struggle with limited high-quality 3D datasets and inconsistency from 2D multi-view generation. We introduce DiffSplat, a novel 3D generative framework that natively…

Computer Vision and Pattern Recognition · Computer Science 2025-01-29 Chenguo Lin , Panwang Pan , Bangbang Yang , Zeming Li , Yadong Mu

Portrait animation aims to generate photo-realistic videos from a single source image by reenacting the expression and pose from a driving video. While early methods relied on 3D morphable models or feature warping techniques, they often…

Computer Vision and Pattern Recognition · Computer Science 2025-09-23 Mallikarjun B. R. , Fei Yin , Vikram Voleti , Nikita Drobyshev , Maksim Lapin , Aaryaman Vasishta , Varun Jampani

We present DreamPolisher, a novel Gaussian Splatting based method with geometric guidance, tailored to learn cross-view consistency and intricate detail from textual descriptions. While recent progress on text-to-3D generation methods have…

Computer Vision and Pattern Recognition · Computer Science 2024-03-27 Yuanze Lin , Ronald Clark , Philip Torr

In this work, we introduce a novel approach for creating controllable dynamics in 3D-generated Gaussians using casually captured reference videos. Our method transfers the motion of objects from reference videos to a variety of generated 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-07-09 Zhoujie Fu , Jiacheng Wei , Wenhao Shen , Chaoyue Song , Xiaofeng Yang , Fayao Liu , Xulei Yang , Guosheng Lin

Text-guided diffusion models have shown superior performance in image/video generation and editing. While few explorations have been performed in 3D scenarios. In this paper, we discuss three fundamental and interesting problems on this…

Computer Vision and Pattern Recognition · Computer Science 2023-10-13 Gang Li , Heliang Zheng , Chaoyue Wang , Chang Li , Changwen Zheng , Dacheng Tao

Recent advancements in foundation models for 2D vision have substantially improved the analysis of dynamic scenes from monocular videos. However, despite their strong generalization capabilities, these models often lack 3D consistency, a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-04 Haoran Zhou , Gim Hee Lee

Video representation is a long-standing problem that is crucial for various down-stream tasks, such as tracking,depth prediction,segmentation,view synthesis,and editing. However, current methods either struggle to model complex motions due…

Computer Vision and Pattern Recognition · Computer Science 2024-06-27 Yang-Tian Sun , Yi-Hua Huang , Lin Ma , Xiaoyang Lyu , Yan-Pei Cao , Xiaojuan Qi

This paper proposes ProEdit - a simple yet effective framework for high-quality 3D scene editing guided by diffusion distillation in a novel progressive manner. Inspired by the crucial observation that multi-view inconsistency in scene…

Computer Vision and Pattern Recognition · Computer Science 2024-11-08 Jun-Kun Chen , Yu-Xiong Wang

Reconstructing and rendering 3D objects from highly sparse views is of critical importance for promoting applications of 3D vision techniques and improving user experience. However, images from sparse views only contain very limited 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-11-14 Chen Yang , Sikuang Li , Jiemin Fang , Ruofan Liang , Lingxi Xie , Xiaopeng Zhang , Wei Shen , Qi Tian

Recent text-guided generation of individual 3D object has achieved great success using diffusion priors. However, these methods are not suitable for object insertion and replacement tasks as they do not consider the background, leading to…

Computer Vision and Pattern Recognition · Computer Science 2025-08-25 Hanyuan Xiao , Yingshu Chen , Huajian Huang , Haolin Xiong , Jing Yang , Pratusha Prasad , Yajie Zhao

3D Gaussian Splatting (3DGS) has recently unlocked real-time, high-fidelity novel view synthesis by representing scenes using explicit 3D primitives. However, traditional methods often require millions of Gaussians to capture complex…

Computer Vision and Pattern Recognition · Computer Science 2026-04-14 Anh Thuan Tran , Jana Kosecka

While diffusion models have demonstrated remarkable progress in 2D image generation and editing, extending these capabilities to 3D editing remains challenging, particularly in maintaining multi-view consistency. Classical approaches…

Computer Vision and Pattern Recognition · Computer Science 2025-08-05 Yufeng Chi , Huimin Ma , Kafeng Wang , Jianmin Li

As XR technology continues to advance rapidly, 3D generation and editing are increasingly crucial. Among these, stylization plays a key role in enhancing the appearance of 3D models. By utilizing stylization, users can achieve consistent…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Dingxi Zhang , Yu-Jie Yuan , Zhuoxun Chen , Fang-Lue Zhang , Zhenliang He , Shiguang Shan , Lin Gao

The emergence of neural representations has revolutionized our means for digitally viewing a wide range of 3D scenes, enabling the synthesis of photorealistic images rendered from novel views. Recently, several techniques have been proposed…

Computer Vision and Pattern Recognition · Computer Science 2025-02-14 Gal Fiebelman , Tamir Cohen , Ayellet Morgenstern , Peter Hedman , Hadar Averbuch-Elor

Dynamic Gaussian Splatting approaches have achieved remarkable performance for 4D scene reconstruction. However, these approaches rely on dense-frame video sequences for photorealistic reconstruction. In real-world scenarios, due to…

Computer Vision and Pattern Recognition · Computer Science 2025-11-11 Changyue Shi , Chuxiao Yang , Xinyuan Hu , Minghao Chen , Wenwen Pan , Yan Yang , Jiajun Ding , Zhou Yu , Jun Yu

Our goal is to develop fine-grained real-image editing methods suitable for real-world applications. In this paper, we first summarize four requirements for these methods and propose a novel diffusion-based image editing framework with…

Computer Vision and Pattern Recognition · Computer Science 2023-06-01 Naoki Matsunaga , Masato Ishii , Akio Hayakawa , Kenji Suzuki , Takuya Narihira

3D object generation from a single image involves estimating the full 3D geometry and texture of unseen views from an unposed RGB image captured in the wild. Accurately reconstructing an object's complete 3D structure and texture has…

Computer Vision and Pattern Recognition · Computer Science 2024-11-21 Hritam Basak , Hadi Tabatabaee , Shreekant Gayaka , Ming-Feng Li , Xin Yang , Cheng-Hao Kuo , Arnie Sen , Min Sun , Zhaozheng Yin

Real-time, high-fidelity reconstruction of dynamic driving scenes is challenged by complex dynamics and sparse views, with prior methods struggling to balance quality and efficiency. We propose DrivingScene, an online, feed-forward…

Computer Vision and Pattern Recognition · Computer Science 2025-10-30 Qirui Hou , Wenzhang Sun , Chang Zeng , Chunfeng Wang , Hao Li , Jianxun Cui
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