Zero-Shot Text-Guided Object Generation with Dream Fields
This addresses the challenge of 3D object generation from text for applications in graphics and AI, offering a zero-shot approach that expands beyond limited categories like ShapeNet.
The authors tackled the problem of generating 3D objects from text descriptions without 3D supervision, achieving realistic and multi-view consistent geometry and color from diverse natural language captions.
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.