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Distilling 3D representations from pretrained 2D diffusion models is essential for 3D creative applications across gaming, film, and interior design. Current SDS-based methods are hindered by inefficient information distillation from…

Computer Vision and Pattern Recognition · Computer Science 2025-03-13 Haoran Li , Yuli Tian , Yonghui Wang , Yong Liao , Lin Wang , Yuyang Wang , Peng Yuan Zhou

Despite the recent success of multi-view diffusion models for text/image-based 3D asset generation, instruction-based editing of 3D assets lacks surprisingly far behind the quality of generation models. The main reason is that recent…

Graphics · Computer Science 2025-12-15 Maria Parelli , Michael Oechsle , Michael Niemeyer , Federico Tombari , Andreas Geiger

Video generation models have made significant progress in generating realistic content, enabling applications in simulation, gaming, and film making. However, current generated videos still contain visual artifacts arising from 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Duolikun Danier , Ge Gao , Steven McDonagh , Changjian Li , Hakan Bilen , Oisin Mac Aodha

We introduce 3DEgo to address a novel problem of directly synthesizing photorealistic 3D scenes from monocular videos guided by textual prompts. Conventional methods construct a text-conditioned 3D scene through a three-stage process,…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Umar Khalid , Hasan Iqbal , Azib Farooq , Jing Hua , Chen Chen

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

Video object insertion is a critical task for dynamically inserting new objects into existing environments. Previous video generation methods focus primarily on synthesizing entire scenes while struggling with ensuring consistent object…

Computer Vision and Pattern Recognition · Computer Science 2026-04-17 Xia Qi , Peishan Cong , Yichen Yao , Ziyi Wang , Yaoqin Ye , Yuexin Ma

Existing multi-view image generation methods often make invasive modifications to pre-trained text-to-image (T2I) models and require full fine-tuning, leading to (1) high computational costs, especially with large base models and…

Computer Vision and Pattern Recognition · Computer Science 2024-12-06 Zehuan Huang , Yuan-Chen Guo , Haoran Wang , Ran Yi , Lizhuang Ma , Yan-Pei Cao , Lu Sheng

Generating novel views of an object from a single image is a challenging task. It requires an understanding of the underlying 3D structure of the object from an image and rendering high-quality, spatially consistent new views. While recent…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Jeong-gi Kwak , Erqun Dong , Yuhe Jin , Hanseok Ko , Shweta Mahajan , Kwang Moo Yi

Pairwise pose estimation from images with little or no overlap is an open challenge in computer vision. Existing methods, even those trained on large-scale datasets, struggle in these scenarios due to the lack of identifiable…

Computer Vision and Pattern Recognition · Computer Science 2024-12-23 Ruojin Cai , Jason Y. Zhang , Philipp Henzler , Zhengqi Li , Noah Snavely , Ricardo Martin-Brualla

Recent advancements in diffusion and flow models have greatly improved text-based image editing, yet methods that edit images independently often produce geometrically and photometrically inconsistent results across different views of the…

Computer Vision and Pattern Recognition · Computer Science 2026-03-23 Josef Bengtson , David Nilsson , Dong In Lee , Yaroslava Lochman , Fredrik Kahl

Applying image processing algorithms independently to each video frame often leads to temporal inconsistency in the resulting video. To address this issue, we present a novel and general approach for blind video temporal consistency. Our…

Computer Vision and Pattern Recognition · Computer Science 2020-10-23 Chenyang Lei , Yazhou Xing , Qifeng Chen

Recently, multi-view diffusion-based 3D generation methods have gained significant attention. However, these methods often suffer from shape and texture misalignment across generated multi-view images, leading to low-quality 3D generation…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Zhuojiang Cai , Yiheng Zhang , Meitong Guo , Mingdao Wang , Yuwang Wang

Recent advances in text-guided image editing and 3D Gaussian Splatting (3DGS) have enabled high-quality 3D scene manipulation. However, existing pipelines rely on iterative edit-and-fit optimization at test time, alternating between 2D…

Computer Vision and Pattern Recognition · Computer Science 2026-04-23 Haitao Huang , Shin-Fang Chng , Huangying Zhan , Qingan Yan , Yi Xu

Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent…

Computer Vision and Pattern Recognition · Computer Science 2024-03-12 Zilong Chen , Yikai Wang , Feng Wang , Zhengyi Wang , Huaping Liu

This work presents 3DPE, a practical method that can efficiently edit a face image following given prompts, like reference images or text descriptions, in a 3D-aware manner. To this end, a lightweight module is distilled from a 3D portrait…

Computer Vision and Pattern Recognition · Computer Science 2024-07-19 Qingyan Bai , Zifan Shi , Yinghao Xu , Hao Ouyang , Qiuyu Wang , Ceyuan Yang , Xuan Wang , Gordon Wetzstein , Yujun Shen , Qifeng Chen

Video matting has traditionally been limited by the lack of high-quality ground-truth data. Most existing video matting datasets provide only human-annotated imperfect alpha and foreground annotations, which must be composited to background…

Computer Vision and Pattern Recognition · Computer Science 2025-08-12 Yongtao Ge , Kangyang Xie , Guangkai Xu , Mingyu Liu , Li Ke , Longtao Huang , Hui Xue , Hao Chen , Chunhua Shen

Recent advancements of generative AI have significantly promoted content creation and editing, where prevailing studies further extend this exciting progress to video editing. In doing so, these studies mainly transfer the inherent motion…

Computer Vision and Pattern Recognition · Computer Science 2025-12-04 Chang Liu , Rui Li , Kaidong Zhang , Yunwei Lan , Dong Liu

High-quality 3D scene reconstruction has recently advanced toward generalizable feed-forward architectures, enabling the generation of complex environments in a single forward pass. However, despite their strong performance in static scene…

Computer Vision and Pattern Recognition · Computer Science 2026-05-20 Kaixin Zhu , Yiwen Tang , Yifan Yang , Renrui Zhang , Bohan Zeng , Ziyu Guo , Ruichuan An , Zhou Liu , Qizhi Chen , Delin Qu , Jaehong Yoon , Wentao Zhang

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

Text-guided 3D face synthesis has achieved remarkable results by leveraging text-to-image (T2I) diffusion models. However, most existing works focus solely on the direct generation, ignoring the editing, restricting them from synthesizing…

Computer Vision and Pattern Recognition · Computer Science 2023-12-04 Yunjie Wu , Yapeng Meng , Zhipeng Hu , Lincheng Li , Haoqian Wu , Kun Zhou , Weiwei Xu , Xin Yu