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arXiv · 2306.08183

ZeroForge: Feedforward Text-to-Shape Without 3D Supervision

Kelly O. Marshall·Minh Pham·Ameya Joshi·Anushrut Jignasu·Aditya Balu·Adarsh Krishnamurthy·Chinmay Hegde
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Abstract

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

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Kelly O. Marshall, Minh Pham, Ameya Joshi, Anushrut Jignasu, Aditya Balu, Adarsh Krishnamurthy, Chinmay Hegde. 2023-06-14. ZeroForge: Feedforward Text-to-Shape Without 3D Supervision. https://arxiv.org/abs/2306.08183

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