Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ShapeAssembly: Learning to Generate Programs for 3D Shape Structure
  Synthesis   Manually authoring 3D shapes is difficult and time consuming; generative
models of 3D shapes offer compelling alternatives. Procedural representations
are one such possibility: they offer high-quality and editable results but are
difficult to author and often produce outputs with limited diversity. On the
other extreme are deep generative models: given enough data, they can learn to
generate any class of shape but their outputs have artifacts and the
representation is not editable. In this paper, we take a step towards achieving
the best of both worlds for novel 3D shape synthesis. We propose ShapeAssembly,
a domain-specific "assembly-language" for 3D shape structures. ShapeAssembly
programs construct shapes by declaring cuboid part proxies and attaching them
to one another, in a hierarchical and symmetrical fashion. Its functions are
parameterized with free variables, so that one program structure is able to
capture a family of related shapes. We show how to extract ShapeAssembly
programs from existing shape structures in the PartNet dataset. Then we train a
deep generative model, a hierarchical sequence VAE, that learns to write novel
ShapeAssembly programs. The program captures the subset of variability that is
interpretable and editable. The deep model captures correlations across shape
collections that are hard to express procedurally. We evaluate our approach by
comparing shapes output by our generated programs to those from other recent
shape structure synthesis models. We find that our generated shapes are more
plausible and physically-valid than those of other methods. Additionally, we
assess the latent spaces of these models, and find that ours is better
structured and produces smoother interpolations. As an application, we use our
generative model and differentiable program interpreter to infer and fit shape
programs to unstructured geometry, such as point clouds.

14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: eess