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

• URL: http://arxiv.org/abs/2306.08183v2
• Date: Fri, 16 Jun 2023 00:48:13 GMT
• Title: ZeroForge: Feedforward Text-to-Shape Without 3D Supervision
• Authors: Kelly O. Marshall, Minh Pham, Ameya Joshi, Anushrut Jignasu, Aditya Balu, Adarsh Krishnamurthy, Chinmay Hegde
• Abstract summary: 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.
• Score: 24.558721379714694
• License: http://creativecommons.org/licenses/by/4.0/
• 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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