Text-to-Shape Generation

3 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation

autodeskailab/clip-forge 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 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 14 Jun 2023

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.