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In recent years, there has been rapid development in 3D generation models, opening up new possibilities for applications such as simulating the dynamic movements of 3D objects and customizing their behaviors. However, current 3D generative…

Computer Vision and Pattern Recognition · Computer Science 2024-06-12 Fangfu Liu , Hanyang Wang , Shunyu Yao , Shengjun Zhang , Jie Zhou , Yueqi Duan

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

Recent years have seen an explosion of work and interest in text-to-3D shape generation. Much of the progress is driven by advances in 3D representations, large-scale pretraining and representation learning for text and image data enabling…

Computer Vision and Pattern Recognition · Computer Science 2024-03-21 Han-Hung Lee , Manolis Savva , Angel X. Chang

Recent breakthroughs in text-to-4D generation rely on pre-trained text-to-image and text-to-video models to generate dynamic 3D scenes. However, current text-to-4D methods face a three-way tradeoff between the quality of scene appearance,…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Sherwin Bahmani , Ivan Skorokhodov , Victor Rong , Gordon Wetzstein , Leonidas Guibas , Peter Wonka , Sergey Tulyakov , Jeong Joon Park , Andrea Tagliasacchi , David B. Lindell

While generative artificial intelligence has advanced significantly across text, image, audio, and video domains, 3D generation remains comparatively underdeveloped due to fundamental challenges such as data scarcity, algorithmic…

Computer Vision and Pattern Recognition · Computer Science 2025-05-13 Weiyu Li , Xuanyang Zhang , Zheng Sun , Di Qi , Hao Li , Wei Cheng , Weiwei Cai , Shihao Wu , Jiarui Liu , Zihao Wang , Xiao Chen , Feipeng Tian , Jianxiong Pan , Zeming Li , Gang Yu , Xiangyu Zhang , Daxin Jiang , Ping Tan

Recent advances in text-to-3D generation have made significant progress. In particular, with the pretrained diffusion models, existing methods predominantly use Score Distillation Sampling (SDS) to train 3D models such as Neural RaRecent…

Computer Vision and Pattern Recognition · Computer Science 2024-10-10 Hangyu Li , Xiangxiang Chu , Dingyuan Shi , Wang Lin

Recently, text-guided 3D generative methods have made remarkable advancements in producing high-quality textures and geometry, capitalizing on the proliferation of large vision-language and image diffusion models. However, existing methods…

Computer Vision and Pattern Recognition · Computer Science 2023-08-30 Xiao Han , Yukang Cao , Kai Han , Xiatian Zhu , Jiankang Deng , Yi-Zhe Song , Tao Xiang , Kwan-Yee K. Wong

Automatic 3D content creation has gained increasing attention recently, due to its potential in various applications such as video games, film industry, and AR/VR. Recent advancements in diffusion models and multimodal models have notably…

Computer Vision and Pattern Recognition · Computer Science 2024-11-26 Yitong Wang , Xudong Xu , Li Ma , Haoran Wang , Bo Dai

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

Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D data and efficient…

Computer Vision and Pattern Recognition · Computer Science 2022-09-30 Ben Poole , Ajay Jain , Jonathan T. Barron , Ben Mildenhall

This paper explores the burgeoning field of 3D content generation within the landscape of Artificial Intelligence Generated Content (AIGC) and large-scale models. It investigates innovative methods like Text-to-3D and Image-to-3D, which…

Graphics · Computer Science 2024-05-27 Ke Zhao , Andreas Larsen

Text-to-3D generation has recently seen significant progress. To enhance its practicality in real-world applications, it is crucial to generate multiple independent objects with interactions, similar to layer-compositing in 2D image…

Computer Vision and Pattern Recognition · Computer Science 2024-07-24 Zizheng Yan , Jiapeng Zhou , Fanpeng Meng , Yushuang Wu , Lingteng Qiu , Zisheng Ye , Shuguang Cui , Guanying Chen , Xiaoguang Han

The demand for efficient 3D model generation techniques has grown exponentially, as manual creation of 3D models is time-consuming and requires specialized expertise. While generative models have shown potential in creating 3D textured…

Computer Vision and Pattern Recognition · Computer Science 2023-07-28 Fanghua Yu , Xintao Wang , Zheyuan Li , Yan-Pei Cao , Ying Shan , Chao Dong

Diffusion models have emerged as a powerful generative method, capable of producing stunning photo-realistic images from natural language descriptions. However, these models lack explicit control over the 3D structure in the generated…

Computer Vision and Pattern Recognition · Computer Science 2024-04-05 Wufei Ma , Qihao Liu , Jiahao Wang , Angtian Wang , Xiaoding Yuan , Yi Zhang , Zihao Xiao , Guofeng Zhang , Beijia Lu , Ruxiao Duan , Yongrui Qi , Adam Kortylewski , Yaoyao Liu , Alan Yuille

Advances in 3D generation have facilitated sequential 3D model generation (a.k.a 4D generation), yet its application for animatable objects with large motion remains scarce. Our work proposes AnimatableDreamer, a text-to-4D generation…

Computer Vision and Pattern Recognition · Computer Science 2024-03-29 Xinzhou Wang , Yikai Wang , Junliang Ye , Zhengyi Wang , Fuchun Sun , Pengkun Liu , Ling Wang , Kai Sun , Xintong Wang , Bin He

Generating high-quality textures for 3D scenes is crucial for applications in interior design, gaming, and augmented/virtual reality (AR/VR). Although recent advancements in 3D generative models have enhanced content creation, significant…

Computer Vision and Pattern Recognition · Computer Science 2025-01-27 Yunfan Zhang , Zhiwei Xiong , Zhiqi Shen , Guosheng Lin , Hao Wang , Nicolas Vun

We present Acc3D to tackle the challenge of accelerating the diffusion process to generate 3D models from single images. To derive high-quality reconstructions through few-step inferences, we emphasize the critical issue of regularizing the…

Computer Vision and Pattern Recognition · Computer Science 2025-03-21 Kendong Liu , Zhiyu Zhu , Hui Liu , Junhui Hou

The emergence of virtual reality has necessitated the generation of detailed and customizable 3D hand models for interaction in the virtual world. However, the current methods for 3D hand model generation are both expensive and cumbersome,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-07 Green Rosh , Prateek Kukreja , Vishakha SR , Pawan Prasad B H

By lifting the pre-trained 2D diffusion models into Neural Radiance Fields (NeRFs), text-to-3D generation methods have made great progress. Many state-of-the-art approaches usually apply score distillation sampling (SDS) to optimize the…

Computer Vision and Pattern Recognition · Computer Science 2023-12-20 Yuze He , Yushi Bai , Matthieu Lin , Jenny Sheng , Yubin Hu , Qi Wang , Yu-Hui Wen , Yong-Jin Liu

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
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