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The ability to generate novel, diverse, and realistic 3D shapes along with associated part semantics and structure is central to many applications requiring high-quality 3D assets or large volumes of realistic training data.
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ShapeGlot: Learning Language for Shape Differentiation
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PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Learning to Infer and Execute 3D Shape Programs. In International Conference on Learning Representations
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