Browsing by Author "Xu, Hao"

Now showing 1 - 3 of 3
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    Effective Eyebrow Matting with Domain Adaptation
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Wang, Luyuan; Zhang, Hanyuan; Xiao, Qinjie; Xu, Hao; Shen, Chunhua; Jin, Xiaogang; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    We present the first synthetic eyebrow matting datasets and a domain adaptation eyebrow matting network for learning domain-robust feature representation using synthetic eyebrow matting data and unlabeled in-the-wild images with adversarial learning. Different from existing matting methods that may suffer from the lack of ground-truth matting datasets, which are typically labor-intensive to annotate or even worse, unable to obtain, we train the matting network in a semi-supervised manner using synthetic matting datasets instead of ground-truth matting data while achieving high-quality results. Specifically, we first generate a large-scale synthetic eyebrow matting dataset by rendering avatars and collect a real-world eyebrow image dataset while maximizing the data diversity as much as possible. Then, we use the synthetic eyebrow dataset to train a multi-task network, which consists of a regression task to estimate the eyebrow alpha mattes and an adversarial task to adapt the learned features from synthetic data to real data. As a result, our method can successfully train an eyebrow matting network using synthetic data without the need to label any real data. Our method can accurately extract eyebrow alpha mattes from in-the-wild images without any additional prior and achieves state-of-the-art eyebrow matting performance. Extensive experiments demonstrate the superior performance of our method with both qualitative and quantitative results.
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    The Predictive Corridor: A Virtual Augmented Driving Assistance System for Teleoperated Autonomous Vehicles
    (The Eurographics Association, 2020) Graf, Gaetano; Abdelrahman, Yomna; Xu, Hao; Abdrabou, Yasmeen; Schitz, Dmitrij; Hußmann, Heinrich; Alt, Florian; Argelaguet, Ferran and McMahan, Ryan and Sugimoto, Maki
    Autonomous vehicles offer a driverless future, however, despite the rapid progress in ubiquitous technologies, human situational assessment continues to be required. For example, upon recognizing an obstacle on the road a request might be routed to a teleoperator, who can assess and manage the situation with the help of a dedicated workspace. A common solution to this problem is direct remote steering. Thereby a key problem in teleoperation is the time latency and low remote situational awareness. To solve this issue we present the Predictive Corridor (PC), a virtual augmented driving assistance system for teleoperated autonomous vehicles. In a user study (N =32), we evaluated the PC by employing three measures: performance, subjective and physiological measures. The results demonstrate that driving with the PC is less cognitively demanding, improves operational performance, and nonetheless can visually compensate for the effect of the time delay between the teleoperator and the vehicle. This technology, therefore, is promising for being applied in future teleoperation applications.
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    Worst-Case Rigidity Analysis and Optimization for Assemblies with Mechanical Joints
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Liu, Zhenyuan; Hu, Jingyu; Xu, Hao; Song, Peng; Zhang, Ran; Bickel, Bernd; Fu, Chi-Wing; Chaine, Raphaëlle; Kim, Min H.
    We study structural rigidity for assemblies with mechanical joints. Existing methods identify whether an assembly is structurally rigid by assuming parts are perfectly rigid. Yet, an assembly identified as rigid may not be that ''rigid'' in practice, and existing methods cannot quantify how rigid an assembly is. We address this limitation by developing a new measure, worst-case rigidity, to quantify the rigidity of an assembly as the largest possible deformation that the assembly undergoes for arbitrary external loads of fixed magnitude. Computing worst-case rigidity is non-trivial due to non-rigid parts and different joint types. We thus formulate a new computational approach by encoding parts and their connections into a stiffness matrix, in which parts are modeled as deformable objects and joints as soft constraints. Based on this, we formulate worst-case rigidity analysis as an optimization that seeks the worst-case deformation of an assembly for arbitrary external loads, and solve the optimization problem via an eigenanalysis. Furthermore, we present methods to optimize the geometry and topology of various assemblies to enhance their rigidity, as guided by our rigidity measure. In the end, we validate our method on a variety of assembly structures with physical experiments and demonstrate its effectiveness by designing and fabricating several structurally rigid assemblies.