We present a novel, purely affinity-based natural image matting algorithm.
We propose a novel calibration method based on the usage of directionally encoded light rays for estimating the intrinsic parameters.
We demonstrate that highly accurate object segmentation in videos can be enabled by using a convolutional neural network (convnet) trained with static images only.
We introduce a deep learning approach for denoising Monte Carlo-rendered images that produces high-quality results suitable for production.
We present a robust, unbiased technique for intelligent light-path construction in path-tracing algorithms.
We propose the concept of blendmaterials to give artists an intuitive means to account for changing material properties due to muscle activation.
We present a real-time multi-view facial capture system facilitated by synthetic training imagery.
We propose the first system for live dynamic augmentation of human faces.
We present the first method for capturing dynamic hair and automatically determining the physical properties for simulating the observed hairstyle in motion.
We propose that the contact pressure distribution of the grasp should be used as a hand benchmark both for naturalness and comfort, and present our initial work in this direction.
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