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  • title: Learning to Generate 3D Shapes with Generative Cellular Automata
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            Learning to Generate 3D Shapes with Generative Cellular Automata
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            Learning to Generate 3D Shapes with Generative Cellular Automata

            May 3, 2021

            Speakers

            DZ

            Dongsu Zhang

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            CC

            Changwoon Choi

            Speaker · 0 followers

            JK

            Jeonghwan Kim

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            About

            In this work, we present a probabilistic 3D generative model, named Generative Cellular Automata, which is able to produce diverse and high quality shapes. We formulate the shape generation process as sampling from the transition kernel of a Markov chain, where the sampling chain eventually evolves to the full shape of the learned distribution. The transition kernel employs the local update rules of cellular automata, effectively reducing the search space in a high-resolution 3D grid space by ex…

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            The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

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