Browsing by Author "Fu, Hongbo"
Now showing 1 - 8 of 8
Results Per Page
Sort Options
Item Context-based Sketch Classification(ACM, 2018) Zhang, Jianhui; Chen, Yilan; Li, Lei; Fu, Hongbo; Tai, Chiew-Lan; Aydın, Tunç and Sýkora, DanielWe present a novel context-based sketch classification framework using relations extracted from scene images. Most of existing methods perform sketch classification by considering individually sketched objects and often fail to identify their correct categories, due to the highly abstract nature of sketches. For a sketched scene containing multiple objects, we propose to classify a sketched object by considering its surrounding context in the scene, which provides vital cues for resolving its recognition ambiguity. We learn such context knowledge from a database of scene images by summarizing the inter-object relations therein, such as co-occurrence, relative positions and sizes.We show that the context information can be used for both incremental sketch classification and sketch co-classification. Our method outperforms the state-of-the-art single-object classification method, evaluated on a new dataset of sketched scenes.Item Frontmatter: Pacific Graphics 2018(The Eurographics Association and John Wiley & Sons Ltd., 2018) Fu, Hongbo; Ghosh, Abhijeet; Kopf, Johannes; Fu, Hongbo and Ghosh, Abhijeet and Kopf, JohannesItem Frontmatter: Pacific Graphics 2018 - Short Papers and Posters(The Eurographics Association, 2018) Fu, Hongbo; Ghosh, Abhijeet; Kopf, Johannes; Fu, Hongbo and Ghosh, Abhijeet and Kopf, JohannesItem GA-Sketching: Shape Modeling from Multi-View Sketching with Geometry-Aligned Deep Implicit Functions(The Eurographics Association and John Wiley & Sons Ltd., 2023) Zhou, Jie; Luo, Zhongjin; Yu, Qian; Han, Xiaoguang; Fu, Hongbo; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.Sketch-based shape modeling aims to bridge the gap between 2D drawing and 3D modeling by providing an intuitive and accessible approach to create 3D shapes from 2D sketches. However, existing methods still suffer from limitations in reconstruction quality and multi-view interaction friendliness, hindering their practical application. This paper proposes a faithful and user-friendly iterative solution to tackle these limitations by learning geometry-aligned deep implicit functions from one or multiple sketches. Our method lifts 2D sketches to volume-based feature tensors, which align strongly with the output 3D shape, enabling accurate reconstruction and faithful editing. Such a geometry-aligned feature encoding technique is well-suited to iterative modeling since features from different viewpoints can be easily memorized or aggregated. Based on these advantages, we design a unified interactive system for sketch-based shape modeling. It enables users to generate the desired geometry iteratively by drawing sketches from any number of viewpoints. In addition, it allows users to edit the generated surface by making a few local modifications. We demonstrate the effectiveness and practicality of our method with extensive experiments and user studies, where we found that our method outperformed existing methods in terms of accuracy, efficiency, and user satisfaction. The source code of this project is available at https://github.com/LordLiang/GA-Sketching.Item InspireMePosing: Learn Pose and Composition from Portrait Examples(The Eurographics Association, 2018) Sheng, Bin; Jin, Yuxi; Li, Ping; Wang, Wenxiao; Fu, Hongbo; Wu, Enhua; Fu, Hongbo and Ghosh, Abhijeet and Kopf, JohannesSince people tend to build relationship with others by personal photography, capturing high quality photographs on mobile device has become a strong demand. We propose a portrait photography guidance system to guide user's photographing. We consider current scene image as our input and find professional photograph examples with similar aesthetic features for it. Deep residual network is introduced to gather scene classification information and represent common photograph rules by features, and random forest is adopted to establishing mapping relations between extracted features and examples. Besides, we implement our guidance system on a camera application and evaluate it by user study.Item Interactive Design and Preview of Colored Snapshots of Indoor Scenes(The Eurographics Association and John Wiley & Sons Ltd., 2020) Fu, Qiang; Yan, Hai; Fu, Hongbo; Li, Xueming; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-LueThis paper presents an interactive system for quickly designing and previewing colored snapshots of indoor scenes. Different from high-quality 3D indoor scene rendering, which often takes several minutes to render a moderately complicated scene under a specific color theme with high-performance computing devices, our system aims at improving the effectiveness of color theme design of indoor scenes and employs an image colorization approach to efficiently obtain high-resolution snapshots with editable colors. Given several pre-rendered, multi-layer, gray images of the same indoor scene snapshot, our system is designed to colorize and merge them into a single colored snapshot. Our system also assists users in assigning colors to certain objects/components and infers more harmonious colors for the unassigned objects based on pre-collected priors to guide the colorization. The quickly generated snapshots of indoor scenes provide previews of interior design schemes with different color themes, making it easy to determine the personalized design of indoor scenes. To demonstrate the usability and effectiveness of this system, we present a series of experimental results on indoor scenes of different types, and compare our method with a state-of-the-art method for indoor scene material and color suggestion and offline/online rendering software packages.Item Line Drawing Vectorization via Coarse‐to‐Fine Curve Network Optimization(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Bao, Bin; Fu, Hongbo; Hauser, Helwig and Alliez, PierreVectorizing line drawings is a fundamental component of the workflow in various applications such as graphic design and computer animation. A practical vectorization tool is desired to produce high‐quality curves that are faithful to the original inputs and close to the connectivity of human drawings. The existing line vectorization approaches either suffer from low geometry accuracy or incorrect connectivity for noisy inputs or detailed complex drawings. We propose a novel line drawing vectorization framework based on coarse‐to‐fine curve network optimization. Our technique starts with an initial curve network generated by an existing tracing method. It then performs a global optimization which fits the curve network to image centrelines. Finally, our method performs a finer optimization in local junction regions to achieve better connectivity and curve geometry around junctions. We qualitatively and quantitatively evaluate our system on line drawings with varying image quality and shape complexity, and show that our technique outperforms existing works in terms of curve quality and computational time.Item TAVE: Template-based Augmentation of Visual Effects to Human Actions in Videos(The Eurographics Association, 2018) Liu, Jingyuan; Zhou, Xuren; Fu, Hongbo; Tai, Chiew-Lan; Fu, Hongbo and Ghosh, Abhijeet and Kopf, JohannesWe present TAVE, a framework that allows novice users to add interesting visual effects by mimicking human actions in a given template video, in which pre-defined visual effects have already been associated with specific human actions. Our framework is mainly based on high-level features of human pose extracted from video frames, and uses low-level image features as the auxiliary information. We encode an action into a set of code sequences representing joint motion directions and use a finite state machine to recognize the action state of interest. The visual effects, possibly with occlusion masks, can be automatically transferred from the template video to a target video containing similar human actions.