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To address the data scarcity associated with 3D assets, 2D-lifting techniques such as Score Distillation Sampling (SDS) have become a widely adopted practice in text-to-3D generation pipelines. However, the diffusion models used in these…

Computer Vision and Pattern Recognition · Computer Science 2025-01-23 Utkarsh Nath , Rajeev Goel , Eun Som Jeon , Changhoon Kim , Kyle Min , Yezhou Yang , Yingzhen Yang , Pavan Turaga

We present Stable Video 4D 2.0 (SV4D 2.0), a multi-view video diffusion model for dynamic 3D asset generation. Compared to its predecessor SV4D, SV4D 2.0 is more robust to occlusions and large motion, generalizes better to real-world…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Chun-Han Yao , Yiming Xie , Vikram Voleti , Huaizu Jiang , Varun Jampani

Current image-to-3D approaches suffer from high computational costs and lack scalability for high-resolution outputs. In contrast, we introduce a novel framework to directly generate explicit surface geometry and texture using multi-view 2D…

Computer Vision and Pattern Recognition · Computer Science 2024-11-19 Haoyu Wu , Meher Gitika Karumuri , Chuhang Zou , Seungbae Bang , Yuelong Li , Dimitris Samaras , Sunil Hadap

Current generative models struggle to synthesize dynamic 4D driving scenes that simultaneously support temporal extrapolation and spatial novel view synthesis (NVS) without per-scene optimization. Bridging generation and novel view…

Computer Vision and Pattern Recognition · Computer Science 2025-09-25 Hao Lu , Zhuang Ma , Guangfeng Jiang , Wenhang Ge , Bohan Li , Yuzhan Cai , Wenzhao Zheng , Yunpeng Zhang , Yingcong Chen

Despite the remarkable performance of score distillation in text-to-3D generation, such techniques notoriously suffer from view inconsistency issues, also known as "Janus" artifact, where the generated objects fake each view with multiple…

Computer Vision and Pattern Recognition · Computer Science 2024-04-02 Peihao Wang , Dejia Xu , Zhiwen Fan , Dilin Wang , Sreyas Mohan , Forrest Iandola , Rakesh Ranjan , Yilei Li , Qiang Liu , Zhangyang Wang , Vikas Chandra

Recent advancements in generative models have enabled the creation of dynamic 4D content - 3D objects in motion - based on text prompts, which holds potential for applications in virtual worlds, media, and gaming. Existing methods provide…

Computer Vision and Pattern Recognition · Computer Science 2025-05-28 Ohad Rahamim , Ori Malca , Dvir Samuel , Gal Chechik

Ground-truth RGBD data are fundamental for a wide range of computer vision applications; however, those labeled samples are difficult to collect and time-consuming to produce. A common solution to overcome this lack of data is to employ…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 L. Papa , P. Russo , I. Amerini

Video generation has recently emerged as a central task in the field of generative AI. However, the substantial computational cost inherent in video synthesis makes model distillation a critical technique for efficient deployment. Despite…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Yuyang You , Yongzhi Li , Jiahui Li , Yadong Mu , Quan Chen , Peng Jiang

4D Gaussian Splatting (4DGS) enables high-quality dynamic novel view synthesis, yet current models remain monolithic bitstreams that clients must download in full before any frame can be rendered, causing black-screen waits of tens to…

Computer Vision and Pattern Recognition · Computer Science 2026-05-13 Jiachen Li , Guangzhi Han , Jin Wan , Delong Han , Yuan Gao , Min Li , Mingle Zhou , Gang Li

3D Gaussian Splatting (3DGS) has shown convincing performance in rendering speed and fidelity, yet the generation of Gaussian Splatting remains a challenge due to its discreteness and unstructured nature. In this work, we propose DiffGS, a…

Computer Vision and Pattern Recognition · Computer Science 2024-10-31 Junsheng Zhou , Weiqi Zhang , Yu-Shen Liu

With the increasing demand for 3D animation, generating high-fidelity, controllable 4D avatars from textual descriptions remains a significant challenge. Despite notable efforts in 4D generative modeling, existing methods exhibit…

Computer Vision and Pattern Recognition · Computer Science 2025-11-21 Eddie Pokming Sheung , Qihao Liu , Wufei Ma , Prakhar Kaushik , Jianwen Xie , Alan Yuille

4D content generation aims to create dynamically evolving 3D content that responds to specific input objects such as images or 3D representations. Current approaches typically incorporate physical priors to animate 3D representations, but…

Computer Vision and Pattern Recognition · Computer Science 2025-11-04 Jiajing Lin , Zhenzhong Wang , Dejun Xu , Shu Jiang , YunPeng Gong , Min Jiang

The blooming of virtual reality and augmented reality (VR/AR) technologies has driven an increasing demand for the creation of high-quality, immersive, and dynamic environments. However, existing generative techniques either focus solely on…

Computer Vision and Pattern Recognition · Computer Science 2024-10-04 Renjie Li , Panwang Pan , Bangbang Yang , Dejia Xu , Shijie Zhou , Xuanyang Zhang , Zeming Li , Achuta Kadambi , Zhangyang Wang , Zhengzhong Tu , Zhiwen Fan

While 2D diffusion models generate realistic, high-detail images, 3D shape generation methods like Score Distillation Sampling (SDS) built on these 2D diffusion models produce cartoon-like, over-smoothed shapes. To help explain this…

Computer Vision and Pattern Recognition · Computer Science 2024-10-11 Artem Lukoianov , Haitz Sáez de Ocáriz Borde , Kristjan Greenewald , Vitor Campagnolo Guizilini , Timur Bagautdinov , Vincent Sitzmann , Justin Solomon

Video diffusion models have rapidly become the dominant paradigm for high-fidelity generative video synthesis, but their practical deployment remains constrained by severe inference costs. Compared with image generation, video synthesis…

Computer Vision and Pattern Recognition · Computer Science 2026-04-20 Shitong Shao , Lichen Bai , Pengfei Wan , James Kwok , Zeke Xie

Score distillation of 2D diffusion models has proven to be a powerful mechanism to guide 3D optimization, for example enabling text-based 3D generation or single-view reconstruction. A common limitation of existing score distillation…

Computer Vision and Pattern Recognition · Computer Science 2025-04-17 Yanbo Xu , Jayanth Srinivasa , Gaowen Liu , Shubham Tulsiani

This paper addresses the problem of decomposed 4D scene reconstruction from multi-view videos. Recent methods achieve this by lifting video segmentation results to a 4D representation through differentiable rendering techniques. Therefore,…

Computer Vision and Pattern Recognition · Computer Science 2025-12-30 Yongzhen Hu , Yihui Yang , Haotong Lin , Yifan Wang , Junting Dong , Yifu Deng , Xinyu Zhu , Fan Jia , Hujun Bao , Xiaowei Zhou , Sida Peng

Multi-view video reconstruction plays a vital role in computer vision, enabling applications in film production, virtual reality, and motion analysis. While recent advances such as 4D Gaussian Splatting (4DGS) have demonstrated impressive…

Computer Vision and Pattern Recognition · Computer Science 2025-11-17 Zhixin Xu , Hengyu Zhou , Yuan Liu , Wenhan Xue , Hao Pan , Wenping Wang , Bin Wang

3D open-vocabulary scene understanding, which accurately perceives complex semantic properties of objects in space, has gained significant attention in recent years. In this paper, we propose GAGS, a framework that distills 2D CLIP features…

Computer Vision and Pattern Recognition · Computer Science 2025-03-11 Yuning Peng , Haiping Wang , Yuan Liu , Chenglu Wen , Zhen Dong , Bisheng Yang

We introduce DreamPolish, a text-to-3D generation model that excels in producing refined geometry and high-quality textures. In the geometry construction phase, our approach leverages multiple neural representations to enhance the stability…

Computer Vision and Pattern Recognition · Computer Science 2024-11-05 Yean Cheng , Ziqi Cai , Ming Ding , Wendi Zheng , Shiyu Huang , Yuxiao Dong , Jie Tang , Boxin Shi
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