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Related papers: Gen-3Diffusion: Realistic Image-to-3D Generation v…

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Motivated by discrete diffusion's success in language-vision modeling, we explore its potential for multi-view generation, a task dominated by continuous approaches. We introduce ViewMask-1-to-3, formulating multi-view synthesis as a…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Ruishu Zhu , Zhihao Huang , Jiacheng Sun , Ping Luo , Hongyuan Zhang , Xuelong Li

In 3D modeling, designers often use an existing 3D model as a reference to create new ones. This practice has inspired the development of Phidias, a novel generative model that uses diffusion for reference-augmented 3D generation. Given an…

Computer Vision and Pattern Recognition · Computer Science 2024-09-18 Zhenwei Wang , Tengfei Wang , Zexin He , Gerhard Hancke , Ziwei Liu , Rynson W. H. Lau

Transparent image layer generation plays a significant role in digital art and design workflows. Existing methods typically decompose transparent layers from a single RGB image using a set of tools or generate multiple transparent layers…

Computer Vision and Pattern Recognition · Computer Science 2025-11-11 Dingbang Huang , Wenbo Li , Yifei Zhao , Xinyu Pan , Chun Wang , Yanhong Zeng , Bo Dai

In this work, we introduce Wonder3D, a novel method for efficiently generating high-fidelity textured meshes from single-view images.Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry…

Computer Vision and Pattern Recognition · Computer Science 2023-11-10 Xiaoxiao Long , Yuan-Chen Guo , Cheng Lin , Yuan Liu , Zhiyang Dou , Lingjie Liu , Yuexin Ma , Song-Hai Zhang , Marc Habermann , Christian Theobalt , Wenping Wang

In the past several decades, many attempts have been made to model synthetic realistic geometric data. The goal of such models is to generate plausible 3D geometries and textures. Perhaps the best known of its kind is the linear 3D…

Computational Geometry · Computer Science 2018-08-28 Ron Slossberg , Gil Shamai , Ron Kimmel

Sparse RGBD scene completion is a challenging task especially when considering consistent textures and geometries throughout the entire scene. Different from existing solutions that rely on human-designed text prompts or predefined camera…

Computer Vision and Pattern Recognition · Computer Science 2024-08-05 Ming-Feng Li , Yueh-Feng Ku , Hong-Xuan Yen , Chi Liu , Yu-Lun Liu , Albert Y. C. Chen , Cheng-Hao Kuo , Min Sun

We present HuGDiffusion, a generalizable 3D Gaussian splatting (3DGS) learning pipeline to achieve novel view synthesis (NVS) of human characters from single-view input images. Existing approaches typically require monocular videos or…

Computer Vision and Pattern Recognition · Computer Science 2025-10-17 Yingzhi Tang , Qijian Zhang , Junhui Hou

Diffusion Handles is a novel approach to enabling 3D object edits on diffusion images. We accomplish these edits using existing pre-trained diffusion models, and 2D image depth estimation, without any fine-tuning or 3D object retrieval. The…

Computer Vision and Pattern Recognition · Computer Science 2023-12-08 Karran Pandey , Paul Guerrero , Matheus Gadelha , Yannick Hold-Geoffroy , Karan Singh , Niloy Mitra

Face aging is the process of converting an individual's appearance to a younger or older version of themselves. Existing face aging techniques have been limited to 2D settings, which often weaken their applications as there is a growing…

Computer Vision and Pattern Recognition · Computer Science 2024-08-29 Junaid Wahid , Fangneng Zhan , Pramod Rao , Christian Theobalt

Despite the latest remarkable advances in generative modeling, efficient generation of high-quality 3D assets from textual prompts remains a difficult task. A key challenge lies in data scarcity: the most extensive 3D datasets encompass…

Computer Vision and Pattern Recognition · Computer Science 2024-01-17 Antoine Mercier , Ramin Nakhli , Mahesh Reddy , Rajeev Yasarla , Hong Cai , Fatih Porikli , Guillaume Berger

A fundamental challenge in text-to-3D face generation is achieving high-quality geometry. The core difficulty lies in the arbitrary and intricate distribution of vertices in 3D space, making it challenging for existing models to establish…

Computer Vision and Pattern Recognition · Computer Science 2026-01-21 Junyi Zhang , Yiming Wang , Yunhong Lu , Qichao Wang , Wenzhe Qian , Xiaoyin Xu , David Gu , Min Zhang

Recently, significant advancements have been made in 3D generative models, however training these models across diverse domains is challenging and requires an huge amount of training data and knowledge of pose distribution. Text-guided…

Computer Vision and Pattern Recognition · Computer Science 2023-04-05 Gwanghyun Kim , Ji Ha Jang , Se Young Chun

Generating photorealistic 3D faces from given conditions is a challenging task. Existing methods often rely on time-consuming one-by-one optimization approaches, which are not efficient for modeling the same distribution content, e.g.,…

Computer Vision and Pattern Recognition · Computer Science 2024-01-12 Xiaolong Shen , Jianxin Ma , Chang Zhou , Zongxin Yang

We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Due to the wide variety of human identities, poses, and stochastic details, the generation of 3D human meshes has been a challenging…

Computer Vision and Pattern Recognition · Computer Science 2023-09-18 Byungjun Kim , Patrick Kwon , Kwangho Lee , Myunggi Lee , Sookwan Han , Daesik Kim , Hanbyul Joo

Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. Additionally, 3D scene generation is vital for advancing embodied AI and world models, which depend…

Computer Vision and Pattern Recognition · Computer Science 2025-11-19 Yuxin Zhang , Ziyu Lu , Hongbo Duan , Keyu Fan , Pengting Luo , Peiyu Zhuang , Mengyu Yang , Houde Liu

Recent advances in generative adversarial networks (GANs) have achieved great success in automated image composition that generates new images by embedding interested foreground objects into background images automatically. On the other…

Computer Vision and Pattern Recognition · Computer Science 2023-04-25 Changgong Zhang , Fangneng Zhan , Shijian Lu , Feiying Ma , Xuansong Xie

The advancement of text-driven 3D content editing has been blessed by the progress from 2D generative diffusion models. However, a major obstacle hindering the widespread adoption of 3D content editing is its time-intensive processing. This…

Computer Vision and Pattern Recognition · Computer Science 2023-12-29 Liangchen Song , Liangliang Cao , Jiatao Gu , Yifan Jiang , Junsong Yuan , Hao Tang

Diffusion models have recently shown strong progress in generative tasks, offering a more stable alternative to GAN-based approaches for makeup transfer. Existing methods often suffer from limited datasets, poor disentanglement between…

Computer Vision and Pattern Recognition · Computer Science 2026-02-03 Qihe Pan , Yiming Wu , Xing Zhao , Liang Xie , Guodao Sun , Ronghua Liang

Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts. However, most existing models fall short in simultaneously…

Computer Vision and Pattern Recognition · Computer Science 2023-11-15 Minghua Liu , Ruoxi Shi , Linghao Chen , Zhuoyang Zhang , Chao Xu , Xinyue Wei , Hansheng Chen , Chong Zeng , Jiayuan Gu , Hao Su

Digital human avatars aim to simulate the dynamic appearance of humans in virtual environments, enabling immersive experiences across gaming, film, virtual reality, and more. However, the conventional process for creating and animating…

Computer Vision and Pattern Recognition · Computer Science 2025-10-15 Felix Taubner , Ruihang Zhang , Mathieu Tuli , Sherwin Bahmani , David B. Lindell
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