Shap·E: OpenAI's New Generative Model for 3D Asset Generation

Generative AI is gaining popularity as it shows massive potential for creating unique and original content. OpenAI's DALL-E generates high-quality images from textual prompts, while their new project Shap·E generates 3D assets using implicit neural representations.

Shap·E uses an encoder to map 3D assets into implicit function parameters, allowing for a thorough understanding of the underlying representation. A conditional diffusion model then generates diverse and complex 3D assets by sampling from the learned distribution.

Shap·E uses implicit neural representations (INRs), which map 3D coordinates to location-specific information to represent a 3D asset. It utilizes Neural Radiance Fields and DMTet/GET3D INRs for versatile and flexible representation of 3D assets.

Shap·E produces high-quality outputs in just seconds and has been compared to Point·E, a generative model that generates explicit representations over point clouds. Shap·E showed faster convergence and comparable or better sample quality.

In conclusion, Shap·E is a promising addition to the contributions of Generative AI for creating 3D assets with great performance and efficiency.