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Related papers: 3DStyleNet: Creating 3D Shapes with Geometric and …

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Transferring the style from one image onto another is a popular and widely studied task in computer vision. Yet, style transfer in the 3D setting remains a largely unexplored problem. To our knowledge, we propose the first learning-based…

Computer Vision and Pattern Recognition · Computer Science 2021-05-19 Mattia Segu , Margarita Grinvald , Roland Siegwart , Federico Tombari

We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers…

Computer Vision and Pattern Recognition · Computer Science 2018-07-23 Thibault Groueix , Matthew Fisher , Vladimir G. Kim , Bryan C. Russell , Mathieu Aubry

Portrait stylization is a long-standing task enabling extensive applications. Although 2D-based methods have made great progress in recent years, real-world applications such as metaverse and games often demand 3D content. On the other…

Computer Vision and Pattern Recognition · Computer Science 2023-04-20 Zhuo Chen , Xudong Xu , Yichao Yan , Ye Pan , Wenhan Zhu , Wayne Wu , Bo Dai , Xiaokang Yang

We present Style3D, a novel approach for generating stylized 3D objects from a content image and a style image. Unlike most previous methods that require case- or style-specific training, Style3D supports instant 3D object stylization. Our…

Computer Vision and Pattern Recognition · Computer Science 2024-12-05 Bingjie Song , Xin Huang , Ruting Xie , Xue Wang , Qing Wang

We introduce Garment3DGen a new method to synthesize 3D garment assets from a base mesh given a single input image as guidance. Our proposed approach allows users to generate 3D textured clothes based on both real and synthetic images, such…

Computer Vision and Pattern Recognition · Computer Science 2025-05-01 Nikolaos Sarafianos , Tuur Stuyck , Xiaoyu Xiang , Yilei Li , Jovan Popovic , Rakesh Ranjan

We present TextureDreamer, a novel image-guided texture synthesis method to transfer relightable textures from a small number of input images (3 to 5) to target 3D shapes across arbitrary categories. Texture creation is a pivotal challenge…

Computer Vision and Pattern Recognition · Computer Science 2024-01-18 Yu-Ying Yeh , Jia-Bin Huang , Changil Kim , Lei Xiao , Thu Nguyen-Phuoc , Numair Khan , Cheng Zhang , Manmohan Chandraker , Carl S Marshall , Zhao Dong , Zhengqin Li

Human shape estimation is an important task for video editing, animation and fashion industry. Predicting 3D human body shape from natural images, however, is highly challenging due to factors such as variation in human bodies, clothing and…

Computer Vision and Pattern Recognition · Computer Science 2018-08-21 Gül Varol , Duygu Ceylan , Bryan Russell , Jimei Yang , Ersin Yumer , Ivan Laptev , Cordelia Schmid

Recent powerful vision classifiers are biased towards textures, while shape information is overlooked by the models. A simple attempt by augmenting training images using the artistic style transfer method, called Stylized ImageNet, can…

Computer Vision and Pattern Recognition · Computer Science 2021-08-25 Sanghyuk Chun , Song Park

Applications in virtual and augmented reality create a demand for rapid creation and easy access to large sets of 3D models. An effective way to address this demand is to edit or deform existing 3D models based on a reference, e.g., a 2D…

Computer Vision and Pattern Recognition · Computer Science 2019-03-11 Weiyue Wang , Duygu Ceylan , Radomir Mech , Ulrich Neumann

3D texture swapping allows for the customization of 3D object textures, enabling efficient and versatile visual transformations in 3D editing. While no dedicated method exists, adapted 2D editing and text-driven 3D editing approaches can…

Computer Vision and Pattern Recognition · Computer Science 2025-10-17 Xiao Cao , Beibei Lin , Bo Wang , Zhiyong Huang , Robby T. Tan

Visual content creation has spurred a soaring interest given its applications in mobile photography and AR / VR. Style transfer and single-image 3D photography as two representative tasks have so far evolved independently. In this paper, we…

Computer Vision and Pattern Recognition · Computer Science 2021-12-07 Fangzhou Mu , Jian Wang , Yicheng Wu , Yin Li

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

Learning to generate textures for a novel 3D mesh given a collection of 3D meshes and real-world 2D images is an important problem with applications in various domains such as 3D simulation, augmented and virtual reality, gaming,…

Computer Vision and Pattern Recognition · Computer Science 2024-03-08 Dharma KC , Clayton T. Morrison

This paper introduces a model for producing stylized line drawings from 3D shapes. The model takes a 3D shape and a viewpoint as input, and outputs a drawing with textured strokes, with variations in stroke thickness, deformation, and color…

Computer Vision and Pattern Recognition · Computer Science 2021-10-11 Difan Liu , Matthew Fisher , Aaron Hertzmann , Evangelos Kalogerakis

Texture cues on 3D objects are key to compelling visual representations, with the possibility to create high visual fidelity with inherent spatial consistency across different views. Since the availability of textured 3D shapes remains very…

Computer Vision and Pattern Recognition · Computer Science 2022-04-07 Yawar Siddiqui , Justus Thies , Fangchang Ma , Qi Shan , Matthias Nießner , Angela Dai

As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train…

Computer Vision and Pattern Recognition · Computer Science 2022-09-23 Jun Gao , Tianchang Shen , Zian Wang , Wenzheng Chen , Kangxue Yin , Daiqing Li , Or Litany , Zan Gojcic , Sanja Fidler

Recent generative models can create visually plausible 3D representations of objects. However, the generation process often allows for implicit control signals, such as contextual descriptions, and rarely supports bold geometric distortions…

Computer Vision and Pattern Recognition · Computer Science 2026-04-10 Changwoon Choi , Hyunsoo Lee , Clément Jambon , Yael Vinker , Young Min Kim

Creating large-scale virtual urban scenes with variant styles is inherently challenging. To facilitate prototypes of virtual production and bypass the need for complex materials and lighting setups, we introduce the first…

Computer Vision and Pattern Recognition · Computer Science 2024-07-17 Yingshu Chen , Huajian Huang , Tuan-Anh Vu , Ka Chun Shum , Sai-Kit Yeung

3D shape models are naturally parameterized using vertices and faces, \ie, composed of polygons forming a surface. However, current 3D learning paradigms for predictive and generative tasks using convolutional neural networks focus on a…

Computer Vision and Pattern Recognition · Computer Science 2017-03-14 Ayan Sinha , Asim Unmesh , Qixing Huang , Karthik Ramani

In this paper, we propose a novel method for single-view 3D style transfer that generates a unique 3D object with both shape and texture transfer. Our focus lies primarily on birds, a popular subject in 3D reconstruction, for which no…

Computer Vision and Pattern Recognition · Computer Science 2023-07-28 Renke Wang , Guimin Que , Shuo Chen , Xiang Li , Jun Li , Jian Yang
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