CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition

ICCV 2019 Nadav SchorOren KatzirHao ZhangDaniel Cohen-Or

Data-driven generative modeling has made remarkable progress by leveraging the power of deep neural networks. A reoccurring challenge is how to enable a model to generate a rich variety of samples from the entire target distribution, rather than only from a distribution confined to the training data... (read more)

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