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While high-quality texture maps are essential for realistic 3D asset rendering, few studies have explored learning directly in the texture space, especially on large-scale datasets. In this work, we depart from the conventional approach of…

Computer Vision and Pattern Recognition · Computer Science 2024-11-25 Xin Yu , Ze Yuan , Yuan-Chen Guo , Ying-Tian Liu , JianHui Liu , Yangguang Li , Yan-Pei Cao , Ding Liang , Xiaojuan Qi

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…

Computer Vision and Pattern Recognition · Computer Science 2024-06-18 Tianfu Wang , Anton Obukhov , Konrad Schindler

In recent years, 3D generation has made great strides in both academia and industry. However, generating 3D scenes from a single RGB image remains a significant challenge, as current approaches often struggle to ensure both object…

Graphics · Computer Science 2026-02-18 Xiang Tang , Ruotong Li , Xiaopeng Fan

Large scale text-guided diffusion models have garnered significant attention due to their ability to synthesize diverse images that convey complex visual concepts. This generative power has more recently been leveraged to perform text-to-3D…

Computer Vision and Pattern Recognition · Computer Science 2023-09-20 Etai Sella , Gal Fiebelman , Peter Hedman , Hadar Averbuch-Elor

Recent advancements in text-to-3D generation technology have significantly advanced the conversion of textual descriptions into imaginative well-geometrical and finely textured 3D objects. Despite these developments, a prevalent limitation…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Zexiang Liu , Yangguang Li , Youtian Lin , Xin Yu , Sida Peng , Yan-Pei Cao , Xiaojuan Qi , Xiaoshui Huang , Ding Liang , Wanli Ouyang

Diffusion-based generative models have significantly advanced text-to-image generation but encounter challenges when processing lengthy and intricate text prompts describing complex scenes with multiple objects. While excelling in…

Computer Vision and Pattern Recognition · Computer Science 2024-02-27 Hanan Gani , Shariq Farooq Bhat , Muzammal Naseer , Salman Khan , Peter Wonka

3D object generation from a single image involves estimating the full 3D geometry and texture of unseen views from an unposed RGB image captured in the wild. Accurately reconstructing an object's complete 3D structure and texture has…

Computer Vision and Pattern Recognition · Computer Science 2024-11-21 Hritam Basak , Hadi Tabatabaee , Shreekant Gayaka , Ming-Feng Li , Xin Yang , Cheng-Hao Kuo , Arnie Sen , Min Sun , Zhaozheng Yin

Text-to-3D generation is a valuable technology in virtual reality and digital content creation. While recent works have pushed the boundaries of text-to-3D generation, producing high-fidelity 3D objects with inefficient prompts and…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Wenqing Wang , Yun Fu

In this paper, we study Text-to-3D content generation leveraging 2D diffusion priors to enhance the quality and detail of the generated 3D models. Recent progress (Magic3D) in text-to-3D has shown that employing high-resolution (e.g., 512 x…

Computer Vision and Pattern Recognition · Computer Science 2023-08-01 Jinbo Wu , Xiaobo Gao , Xing Liu , Zhengyang Shen , Chen Zhao , Haocheng Feng , Jingtuo Liu , Errui Ding

Recent 3D Gaussian Splatting (3DGS) representations have demonstrated remarkable performance in novel view synthesis; further, material-lighting disentanglement on 3DGS warrants relighting capabilities and its adaptability to broader…

Computer Vision and Pattern Recognition · Computer Science 2025-07-14 Kai Ye , Chong Gao , Guanbin Li , Wenzheng Chen , Baoquan Chen

3D style transfer enables the creation of visually expressive 3D content, enriching the visual appearance of 3D scenes and objects. However, existing VGG- and CLIP-based methods struggle to model multi-view consistency within the model…

Computer Vision and Pattern Recognition · Computer Science 2026-01-28 Yitong Yang , Xuexin Liu , Yinglin Wang , Jing Wang , Hao Dou , Changshuo Wang , Shuting He

Recent interactive matting methods have shown satisfactory performance in capturing the primary regions of objects, but they fall short in extracting fine-grained details in edge regions. Diffusion models trained on billions of image-text…

Computer Vision and Pattern Recognition · Computer Science 2025-08-05 Longfei Huang , Yu Liang , Hao Zhang , Jinwei Chen , Wei Dong , Lunde Chen , Wanyu Liu , Bo Li , Peng-Tao Jiang

Restoring low-resolution text images presents a significant challenge, as it requires maintaining both the fidelity and stylistic realism of the text in restored images. Existing text image restoration methods often fall short in hard…

Computer Vision and Pattern Recognition · Computer Science 2025-06-17 Chenglu Pan , Xiaogang Xu , Ganggui Ding , Yunke Zhang , Wenbo Li , Jiarong Xu , Qingbiao Wu

Material reconstruction from a photograph is a key component of 3D content creation democratization. We propose to formulate this ill-posed problem as a controlled synthesis one, leveraging the recent progress in generative deep networks.…

Computer Vision and Pattern Recognition · Computer Science 2024-08-28 Giuseppe Vecchio , Rosalie Martin , Arthur Roullier , Adrien Kaiser , Romain Rouffet , Valentin Deschaintre , Tamy Boubekeur

Recent research on texture synthesis for 3D shapes benefits a lot from dramatically developed 2D text-to-image diffusion models, including inpainting-based and optimization-based approaches. However, these methods ignore the modal gap…

Computer Vision and Pattern Recognition · Computer Science 2024-08-16 Shang Liu , Chaohui Yu , Chenjie Cao , Wen Qian , Fan Wang

Diffusion models have demonstrated great success in the field of text-to-image generation. However, alleviating the misalignment between the text prompts and images is still challenging. The root reason behind the misalignment has not been…

Computer Vision and Pattern Recognition · Computer Science 2024-11-28 Dongzhi Jiang , Guanglu Song , Xiaoshi Wu , Renrui Zhang , Dazhong Shen , Zhuofan Zong , Yu Liu , Hongsheng Li

Automatic 3D facial texture generation has gained significant interest recently. Existing approaches may not support the traditional physically based rendering pipeline or rely on 3D data captured by Light Stage. Our key contribution is a…

Computer Vision and Pattern Recognition · Computer Science 2024-04-16 Chi Wang , Junming Huang , Rong Zhang , Qi Wang , Haotian Yang , Haibin Huang , Chongyang Ma , Weiwei Xu

We present MatSwap, a method to transfer materials to designated surfaces in an image photorealistically. Such a task is non-trivial due to the large entanglement of material appearance, geometry, and lighting in a photograph. In the…

Computer Vision and Pattern Recognition · Computer Science 2025-06-26 Ivan Lopes , Valentin Deschaintre , Yannick Hold-Geoffroy , Raoul de Charette

Generative models for 3D object synthesis have seen significant advancements with the incorporation of prior knowledge distilled from 2D diffusion models. Nevertheless, challenges persist in the form of multi-view geometric inconsistencies…

Computer Vision and Pattern Recognition · Computer Science 2023-11-20 Lincong Feng , Muyu Wang , Maoyu Wang , Kuo Xu , Xiaoli Liu

Mean texture depth (MTD) is pivotal in assessing the skid resistance of asphalt pavements and ensuring road safety. This study focuses on developing an automated system for extracting texture features and evaluating MTD based on pavement…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Bingjie Lu , Han-Cheng Dan , Yichen Zhang , Zhetao Huang