Browse State-of-the-Art › Text-to-Shape Generation

Text-to-Shape Generation

4 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28

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Benchmarks archive 2025-07-28

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Datasets archive 2025-07-28

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (8 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

  • 19 Mar 2025 1 repository listed
    We show how our tokenization scheme can be used in applications for text-to-shape generation, shape-to-text generation and text-to-scene generation.
  • 14 Jun 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
    Current state-of-the-art methods for text-to-shape generation either require supervised training using a labeled dataset of pre-defined 3D shapes, or perform expensive inference-time optimization of implicit neural…
  • 8 Dec 2022 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
    To enable interactive generation, our method supports a variety of input modalities that can be easily provided by a human, including images, text, partially observed shapes and combinations of these, further allowing…
  • 6 Oct 2021 1 repository listed
    Generating shapes using natural language can enable new ways of imagining and creating the things around us.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.

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