Zero-Shot Text-Guided Object Generation with Dream Fields

• URL: http://arxiv.org/abs/2112.01455v1
• Date: Thu, 2 Dec 2021 17:53:55 GMT
• Title: Zero-Shot Text-Guided Object Generation with Dream Fields
• Authors: Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, Ben Poole
• Abstract summary: We combine neural rendering with multi-modal image and text representations to synthesize diverse 3D objects. Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision. In experiments, Dream Fields produce realistic, multi-view consistent object geometry and color from a variety of natural language captions.
• Score: 111.06026544180398
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We combine neural rendering with multi-modal image and text representations to synthesize diverse 3D objects solely from natural language descriptions. Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision. Due to the scarcity of diverse, captioned 3D data, prior methods only generate objects from a handful of categories, such as ShapeNet. Instead, we guide generation with image-text models pre-trained on large datasets of captioned images from the web. Our method optimizes a Neural Radiance Field from many camera views so that rendered images score highly with a target caption according to a pre-trained CLIP model. To improve fidelity and visual quality, we introduce simple geometric priors, including sparsity-inducing transmittance regularization, scene bounds, and new MLP architectures. In experiments, Dream Fields produce realistic, multi-view consistent object geometry and color from a variety of natural language captions.

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