Hi there. My name is Jay and I'm a student researcher at Google Brain and PhD student at UC Berkeley. I'm excited to present Dreamfields, an approach to generating digital 3D objects from natural language descriptions. In our work, we represent 3D objects with the Neural Radiance Field scene parameterization. A Neural Radiance field provides a way to differentially render a scene that's parameterized by multi-layer perceptron, encoding color, and density. Neural Radiance Fields or Nerfs are supervised per scene from 2D images. Nerfs requires multiple 3D photographs of the specific object that is to be reconstructed with known camera viewpoints for each. For this drum set, 100 photos were used for highly detailed 360 degree reconstruction. Once estimated, the Radiance Field can be rendered from novel perspectives with volume rendering, interpolating the training views smoothly. In contrast, our Dreamfields do not require any photographs, which are difficult to capture and are not available when a user wants to design a novel object. Instead, the user just provides a textual description of the desired object. A Dreamfield learns geometry and color solely with guidance from a deep neural network condition on the caption and simple regularizers. Novel perspectives are geometrically consistent and reflect the desired caption well. To train Dreamfields, we sample a camera pose uniformly around the object, render an image with volumetric rendering, and then encode it using a pre-trient, aligned representation of images and text. For example, the Clip Vision Transformer architecture is trained on paired 2D photographs and captions found on the internet. No multi-view data or 3D models are used. Maximizing the similarity of the rendered image and the captions allows us to train the Radiance Field to look like the caption from any perspective. However, if we just use this naive approach of maximizing ClipLoss on objects with white backgrounds, many artifacts emerge in the renderings such as seen saturated with content and duplicated objects. To improve quality, we augment the backgrounds of the objects with randomly generated background images and impose a transmittance loss that encourages high transparency in the renderings. In addition to sparsifying the scene, we improve quality with seen bounds and improved MLP architectures. Over 10 to 20,000 iterations of training, the geometry and texture of a Dreamfield improves in realism. Here we show different perspectives at different iterations, as well as the three types of background augmentations used in the work. On another scene with a caption, a kettle in the shape of a fossil, we show the full progression of training. Dreamfields also generate depth maps that are plausible. We don't constrain generation to a specific category of objects. Instead, our image text representations are trained on a very large diversity of web data and support generation across many types of categories. Inspired by the Dalai text image generative model, we experiment with a compositional generalization of Dreamfields. Showing that they can combine object categories like armchair and teapot with different types of styles and materials like brain corals, hibiscus, avocados and lotus roots. Users control the generation process with natural language, expressing desired stylistic effects easily like matte paintings or website like art station. Please see our paper for more technical details as well as evaluation.