Generative 3D Painting is among the top productivity boosters in high-resolution 3D asset management and recycling. Ever since text-to-image models became accessible for inference on consumer hardware, the performance of 3D Painting methods has consistently improved and is currently close to plateauing. At the core of most such models lies denoising diffusion in the latent space, an inherently time-consuming iterative process. Multiple techniques have been developed recently to accelerate generation and reduce sampling iterations by orders of magnitude. Designed for 2D generative imaging, these techniques do not come with recipes for lifting them into 3D. In this paper, we address this shortcoming by proposing a Latent Consistency Model (LCM) adaptation for the task at hand. We analyze the strengths and weaknesses of the proposed model and evaluate it quantitatively and qualitatively. Based on the Objaverse dataset samples study, our 3D painting method attains strong preference in all evaluations. Source code is available at https://github.com/kongdai123/consistency2.
CCLAP: Controllable Chinese Landscape Painting Generation via Latent Diffusion Model
Beijing Institute of Technology（北京理工大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Institute of Intelligent Computing Technology, Chinese Academy of Sciences（中国科学院智能计算技术研究所）；Tomorrow Advancing Life（好未来）
Beijing University of Post and Telecommunication（北京邮电大学）；Tsinghua University（清华大学）；Institute of AI (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））；School of Artificial Intelligence（人工智能学院）；Northwestern Polytechnical University (NWPU)（西北工业大学）
SRAGAN: Saliency Regularized and Attended Generative Adversarial Network for Chinese Ink-wash Painting Style Transfer
Wangxuan Institute of Computer Technology, Peking University（北京大学王轩计算机技术研究所）；School of Mathematical Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）
OPa-Ma: Text Guided Mamba for 360-degree Image Out-painting
Cleveland Clinic（克利夫兰诊所）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；East China Normal University（华东师范大学）；Solon High School（索伦高中）；Tianyuan Biotechnology（天元生物科技）
Multi-scale Latent Point Consistency Models for 3D Shape Generation
Wangxuan Institute of Computer Technology, Peking University（北京大学王轩计算机技术研究所）；Department of Electrical and Computer Engineering, University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）
Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping
South China University of Technology（华南理工大学）；Nanyang Technological University（南洋理工大学）；Beijing Institute of Technology（北京理工大学）；The University of Sydney（悉尼大学）
DLP-GAN: learning to draw modern Chinese landscape photos with generative adversarial network
School of Computer and Communication, Lanzhou University of Technology（兰州理工大学计算机与通信学院）；School of Information Science and Engineering, Lanzhou University（兰州大学信息科学与工程学院）
LDC-VAE: A Latent Distribution Consistency Approach to Variational AutoEncoders
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps
AI Center, Guangdong OPPO Mobile Telecommunications Corp., Ltd（广东欧珀移动通信有限公司AI中心）；Qing Yuan Research Institute, SEIEE, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院清源研究院）
InterLCM: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration
Nankai University（南开大学）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Linkoping University（林雪平大学）；SB Intuitions, SoftBank（软银SB Intuitions）；Nankai International Advanced Research Institute (Shenzhen Futian), Nankai University（南开大学深圳福田国际先进研究院）
Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN
University of California, Santa Barbara（加州大学圣巴巴拉分校）；University of Oregon（俄勒冈大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）；Picsart AI Research（Picsart人工智能研究院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Texas A&M University（德克萨斯农工大学）
DreamLCM: Towards High-Quality Text-to-3D Generation via Latent Consistency Model
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Visual Intelligence + X International Joint Laboratory of the Ministry of Education（教育部视觉智能与X国际联合实验室）；College of Electrical Engineering and Automation, Shandong University of Science and Technology（山东科技大学电气与自动化工程学院）；PengCheng Laboratory（鹏城实验室）
LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization
CNRS（法国国家科学研究中心）；Univ. Bordeaux（波尔多大学）；INRIA（法国国家信息与自动化研究所）；Bordeaux INP（波尔多国立高等工程技术学院）；Heriot-Watt University（赫瑞瓦特大学）；Maxwell Institute for Mathematical Sciences（麦克斯韦数学科学研究所）
SurgicaL-CD: Generating Surgical Images via Unpaired Image Translation with Latent Consistency Diffusion Models
NCT/UCC Dresden（德累斯顿癌症中心/癌症联盟）；DKFZ Heidelberg（海德堡德国癌症研究中心）；Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院及大学医院）；HZDR Dresden（德累斯顿亥姆霍兹中心）；The Centre for Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology（德累斯顿工业大学人机交互触觉互联网中心）
