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. In this work, we present ZeroForge, an approach for zero-shot text-to-shape generation that avoids both pitfalls. To achieve open-vocabulary shape generation, we require careful architectural adaptation of existing feed-forward approaches, as well as a combination of data-free CLIP-loss and contrastive losses to avoid mode collapse. Using these techniques, we are able to considerably expand the generative ability of existing feed-forward text-to-shape models such as CLIP-Forge. We support our method via extensive qualitative and quantitative evaluations
Cite
@article{arxiv.2306.08183,
title = {ZeroForge: Feedforward Text-to-Shape Without 3D Supervision},
author = {Kelly O. Marshall and Minh Pham and Ameya Joshi and Anushrut Jignasu and Aditya Balu and Adarsh Krishnamurthy and Chinmay Hegde},
journal= {arXiv preprint arXiv:2306.08183},
year = {2023}
}
Comments
19 pages, High resolution figures needed to demonstrate 3D results