The text-to-image model, similar to generative art systems like OpenAI’s own DALL-E 2 and Stable Diffusion, was trained on labeled images to understand the associations between words and visual concepts.When given a text prompt — for example, “a 3D printable gear, a single gear 3 inches in diameter and half inch thick” — Point-E’s text-to-image model generates a synthetic rendered object that’s fed to the image-to-3D model, which then generates a point cloud.“This could make it more practical for certain applications, or could allow for the discovery of higher-quality 3D object.”What are the applications, exactly?Architectural firms use them to demo proposed buildings and landscapes, for example, while engineers leverage models as designs of new devices, vehicles and structures.To their credit, the researchers do mention that they expect Point-E to suffer from other problems, like biases inherited from the training data and a lack of safeguards around models that might be used to create “dangerous objects.” That’s perhaps why they’re careful to characterize Point-E as a “starting point” that they hope will inspire “further work” in the field of text-to-3D synthesis."