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Our published research
View all researchMultiple Pairwise Ranking Networks for Personalized Video Summarization
We propose a model for personalized video summaries by conditioning the summarization process with predefined categorical user labels.
HoloGAN: Unsupervised Learning of 3D Representations from Natural Images
We propose a novel generative adversarial network (GAN) for the task of unsupervised learning of 3D representations from natural images.
RenderNet: A Deep ConvNet for Differentiable Rendering from 3D Shapes
We present a differentiable rendering convolutional network with a novel projection unit that can render 2D images from 3D shapes.
Adversarial Monte Carlo Denoising with Conditioned Aux. Feature Modulation
We demonstrate that GANs can help denoiser networks produce more realistic high-frequency details and global illumination.
