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Generating high-quality 3D assets from text and images has long been challenging, primarily due to the absence of scalable 3D representations capable of capturing intricate geometry distributions. In this work, we introduce Direct3D, a…

Computer Vision and Pattern Recognition · Computer Science 2024-06-04 Shuang Wu , Youtian Lin , Feihu Zhang , Yifei Zeng , Jingxi Xu , Philip Torr , Xun Cao , Yao Yao

The advancements in automatic text-to-3D generation have been remarkable. Most existing methods use pre-trained text-to-image diffusion models to optimize 3D representations like Neural Radiance Fields (NeRFs) via latent-space denoising…

Computer Vision and Pattern Recognition · Computer Science 2024-03-12 Junzhe Zhu , Peiye Zhuang , Sanmi Koyejo

Single image-to-3D generation is pivotal for crafting controllable 3D assets. Given its under-constrained nature, we attempt to leverage 3D geometric priors from a novel view diffusion model and 2D appearance priors from an image generation…

Computer Vision and Pattern Recognition · Computer Science 2024-10-10 Shuzhou Yang , Yu Wang , Haijie Li , Jiarui Meng , Yanmin Wu , Xiandong Meng , Jian Zhang

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

Text-to-3D generation has achieved significant success by incorporating powerful 2D diffusion models, but insufficient 3D prior knowledge also leads to the inconsistency of 3D geometry. Recently, since large-scale multi-view datasets have…

Computer Vision and Pattern Recognition · Computer Science 2024-05-03 Junyoung Seo , Susung Hong , Wooseok Jang , Inès Hyeonsu Kim , Minseop Kwak , Doyup Lee , Seungryong Kim

Generating diverse and high-quality 3D assets automatically poses a fundamental yet challenging task in 3D computer vision. Despite extensive efforts in 3D generation, existing optimization-based approaches struggle to produce large-scale…

Computer Vision and Pattern Recognition · Computer Science 2024-05-15 Ziang Cao , Fangzhou Hong , Tong Wu , Liang Pan , Ziwei Liu

Large-scale diffusion generative models are greatly simplifying image, video and 3D asset creation from user-provided text prompts and images. However, the challenging problem of text-to-4D dynamic 3D scene generation with diffusion…

Computer Vision and Pattern Recognition · Computer Science 2024-05-08 Yufeng Zheng , Xueting Li , Koki Nagano , Sifei Liu , Karsten Kreis , Otmar Hilliges , Shalini De Mello

Advancements in text-to-image diffusion models have led to significant progress in fast 3D content creation. One common approach is to generate a set of multi-view images of an object, and then reconstruct it into a 3D model. However, this…

Computer Vision and Pattern Recognition · Computer Science 2024-12-04 Yiftach Edelstein , Or Patashnik , Dana Cohen-Bar , Lihi Zelnik-Manor

Diffusion-based image generators can now produce high-quality and diverse samples, but their success has yet to fully translate to 3D generation: existing diffusion methods can either generate low-resolution but 3D consistent outputs, or…

Computer Vision and Pattern Recognition · Computer Science 2023-08-29 Animesh Karnewar , Niloy J. Mitra , Andrea Vedaldi , David Novotny

As 3D generation techniques continue to flourish, the demand for generating personalized content is rapidly rising. Users increasingly seek to apply various editing methods to polish generated 3D content, aiming to enhance its color, style,…

Computer Vision and Pattern Recognition · Computer Science 2025-08-12 Weitao Wang , Haoran Xu , Jun Meng , Haoqian Wang

This work presents a flexible system to reconstruct 3D models of objects captured with an RGB-D sensor. A major advantage of the method is that our reconstruction pipeline allows the user to acquire a full 3D model of the object. This is…

Computer Vision and Pattern Recognition · Computer Science 2015-05-22 Aitor Aldoma , Johann Prankl , Alexander Svejda , Markus Vincze

Recent years have seen significant advancements in 3D generation. While methods like score distillation achieve impressive results, they often require extensive per-scene optimization, which limits their time efficiency. On the other hand,…

Computer Vision and Pattern Recognition · Computer Science 2024-08-21 Chen Wang , Jiatao Gu , Xiaoxiao Long , Yuan Liu , Lingjie Liu

Previous efforts have managed to generate production-ready 3D assets from text or images. However, these methods primarily employ NeRF or 3D Gaussian representations, which are not adept at producing smooth, high-quality geometries required…

Graphics · Computer Science 2024-10-15 Rengan Xie , Wenting Zheng , Kai Huang , Yizheng Chen , Qi Wang , Qi Ye , Wei Chen , Yuchi Huo

3D content generation has wide applications in various fields. One of its dominant paradigms is by sparse-view reconstruction using multi-view images generated by diffusion models. However, since directly reconstructing triangle meshes from…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Ruowen Zhao , Zhengyi Wang , Yikai Wang , Zihan Zhou , Jun Zhu

3D building generation with low data acquisition costs, such as single image-to-3D, becomes increasingly important. However, most of the existing single image-to-3D building creation works are restricted to those images with specific…

Computer Vision and Pattern Recognition · Computer Science 2023-09-04 Yao Wei , George Vosselman , Michael Ying Yang

Recent progress in 3D object generation has greatly improved both the quality and efficiency. However, most existing methods generate a single mesh with all parts fused together, which limits the ability to edit or manipulate individual…

Computer Vision and Pattern Recognition · Computer Science 2025-06-12 Jiaxiang Tang , Ruijie Lu , Zhaoshuo Li , Zekun Hao , Xuan Li , Fangyin Wei , Shuran Song , Gang Zeng , Ming-Yu Liu , Tsung-Yi Lin

Recent advancements in diffusion techniques have propelled image and video generation to unprecedented levels of quality, significantly accelerating the deployment and application of generative AI. However, 3D shape generation technology…

Computer Vision and Pattern Recognition · Computer Science 2025-03-28 Yangguang Li , Zi-Xin Zou , Zexiang Liu , Dehu Wang , Yuan Liang , Zhipeng Yu , Xingchao Liu , Yuan-Chen Guo , Ding Liang , Wanli Ouyang , Yan-Pei Cao

Benefiting from the rapid development of 2D diffusion models, 3D content generation has witnessed significant progress. One promising solution is to finetune the pre-trained 2D diffusion models to produce multi-view images and then…

Computer Vision and Pattern Recognition · Computer Science 2025-01-10 Fan Yang , Jianfeng Zhang , Yichun Shi , Bowen Chen , Chenxu Zhang , Huichao Zhang , Xiaofeng Yang , Xiu Li , Jiashi Feng , Guosheng Lin

Recent 3D generative models can synthesize high-quality assets, but their outputs are typically static: they lack the skeletal rigs, joint hierarchies, and skinning weights required for animation. This limits their use in games, film,…

Graphics · Computer Science 2026-05-14 Nikitas Chatzis , Marios Loizou , Evangelos Kalogerakis

This paper introduces MIDI, a novel paradigm for compositional 3D scene generation from a single image. Unlike existing methods that rely on reconstruction or retrieval techniques or recent approaches that employ multi-stage…

Computer Vision and Pattern Recognition · Computer Science 2025-07-18 Zehuan Huang , Yuan-Chen Guo , Xingqiao An , Yunhan Yang , Yangguang Li , Zi-Xin Zou , Ding Liang , Xihui Liu , Yan-Pei Cao , Lu Sheng
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