ORCID Profile
0000-0002-7498-3033
Current Organisations
Monash University
,
City University of Hong Kong
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Publisher: Association for Computing Machinery (ACM)
Date: 12-08-2020
Abstract: Creating animated virtual AR characters closely interacting with real environments is interesting but difficult. Existing systems adopt video see-through approaches to indirectly control a virtual character in mobile AR, making close interaction with real environments not intuitive. In this work we use an AR-enabled mobile device to directly control the position and motion of a virtual character situated in a real environment. We conduct two guessability studies to elicit user-defined motions of a virtual character interacting with real environments, and a set of user-defined motion gestures describing specific character motions. We found that an SVM-based learning approach achieves reasonably high accuracy for gesture classification from the motion data of a mobile device. We present ARAnimator , which allows novice and casual animation users to directly represent a virtual character by an AR-enabled mobile phone and control its animation in AR scenes using motion gestures of the device, followed by animation preview and interactive editing through a video see-through interface. Our experimental results show that with ARAnimator , users are able to easily create in-situ character animations closely interacting with different real environments.
Publisher: Association for Computing Machinery (ACM)
Date: 30-11-2022
Abstract: Multiple sketch datasets have been proposed to understand how people draw 3D objects. However, such datasets are often of small scale and cover a small set of objects or categories. In addition, these datasets contain freehand sketches mostly from expert users, making it difficult to compare the drawings by expert and novice users, while such comparisons are critical in informing more effective sketch-based interfaces for either user groups. These observations motivate us to analyze how differently people with and without adequate drawing skills sketch 3D objects. We invited 70 novice users and 38 expert users to sketch 136 3D objects, which were presented as 362 images rendered from multiple views. This leads to a new dataset of 3,620 freehand multi-view sketches, which are registered with their corresponding 3D objects under certain views. Our dataset is an order of magnitude larger than the existing datasets. We analyze the collected data at three levels, i.e., sketch-level, stroke-level, and pixel-level, under both spatial and temporal characteristics, and within and across groups of creators. We found that the drawings by professionals and novices show significant differences at stroke-level, both intrinsically and extrinsically. We demonstrate the usefulness of our dataset in two applications: (i) freehand-style sketch synthesis, and (ii) posing it as a potential benchmark for sketch-based 3D reconstruction. Our dataset and code are available at chufengxiao.github.io/DifferSketching/.
Publisher: Association for Computing Machinery (ACM)
Date: 12-08-2020
Abstract: Recent deep image-to-image translation techniques allow fast generation of face images from freehand sketches. However, existing solutions tend to overfit to sketches, thus requiring professional sketches or even edge maps as input. To address this issue, our key idea is to implicitly model the shape space of plausible face images and synthesize a face image in this space to approximate an input sketch. We take a local-to-global approach. We first learn feature embeddings of key face components, and push corresponding parts of input sketches towards underlying component manifolds defined by the feature vectors of face component s les. We also propose another deep neural network to learn the mapping from the embedded component features to realistic images with multi-channel feature maps as intermediate results to improve the information flow. Our method essentially uses input sketches as soft constraints and is thus able to produce high-quality face images even from rough and/or incomplete sketches. Our tool is easy to use even for non-artists, while still supporting fine-grained control of shape details. Both qualitative and quantitative evaluations show the superior generation ability of our system to existing and alternative solutions. The usability and expressiveness of our system are confirmed by a user study.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: Wiley
Date: 12-10-2017
DOI: 10.1002/CAV.1740
Publisher: Association for Computing Machinery (ACM)
Date: 25-07-2018
DOI: 10.1145/3203186
Abstract: High-quality normal maps are important intermediates for representing complex shapes. In this paper, we propose an interactive system for generating normal maps with the help of deep learning techniques. Utilizing the Generative Adversarial Network (GAN) framework, our method produces high quality normal maps with sketch inputs. In addition, we further enhance the interactivity of our system by incorporating user-specified normals at selected points. Our method generates high quality normal maps in real time. Through comprehensive experiments, we show the effectiveness and robustness of our method. A thorough user study indicates the normal maps generated by our method achieve a lower perceptual difference from the ground truth compared to the alternative methods.
No related grants have been discovered for Wanchao SU.