Text-to-Shape Generation
3 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Text-to-Shape Generation
Most implemented papers
CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation
autodeskailab/clip-forge
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CVPR 2022
Generating shapes using natural language can enable new ways of imagining and creating the things around us.
SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation
yccyenchicheng/SDFusion
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CVPR 2023
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 to adjust the strength of each input.
ZeroForge: Feedforward Text-to-Shape Without 3D Supervision
km3888/zeroforge
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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 representations.
