Point-E Description
Recent advancements in text-based 3D object generation have yielded encouraging outcomes; however, leading methods generally need several GPU hours to create a single sample, which is a stark contrast to the latest generative image models capable of producing samples within seconds or minutes. In this study, we present a different approach to generating 3D objects that enables the creation of models in just 1-2 minutes using a single GPU. Our technique initiates by generating a synthetic view through a text-to-image diffusion model, followed by the development of a 3D point cloud using a second diffusion model that relies on the generated image for conditioning. Although our approach does not yet match the top-tier quality of existing methods, it offers a significantly faster sampling process, making it a valuable alternative for specific applications. Furthermore, we provide access to our pre-trained point cloud diffusion models, along with the evaluation code and additional models, available at this https URL. This contribution aims to facilitate further exploration and development in the realm of efficient 3D object generation.
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Company Details
Company:
OpenAI
Year Founded:
2015
Headquarters:
United States
Website:
openai.com/research/point-e
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