Qianwen Chao

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We present a video-based approach to learn the specific driving characteristics of drivers in the video for advanced traffic control. Each vehicle's specific driving characteristics are calculated with an offline learning process. Given each vehicle's initial status and the personalized parameters as input, our approach can vividly reproduce the traffic(More)
We present a real-time solution for generating detailed clothing deformations from pre-computed clothing shape examples. Given an input pose, it synthesizes a clothing deformation by blending skinned clothing deformations of nearby examples controlled by the body skeleton. Observing that cloth deformation can be well modeled with sensitivity analysis driven(More)
Simulation of real-world traffic scenarios is widely needed in virtual environments. Different from many previous works on simulating vehicles or pedestrians separately, our approach aims to capture the realistic process of vehicle–pedestrian interaction for mixed traffic simulation. We model a decision-making process for their interaction based on a gap(More)
We present a novel data-driven approach to populate virtual road networks with realistic traffic flows. Specifically, given a limited set of vehicle trajectories as the input samples, our approach first synthesizes a large set of vehicle trajectories. By taking the spatio-temporal information of traffic flows as a 2D texture, the generation of new traffic(More)
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