Shap-E: A Breakthrough in 3D Asset Generation Using Conditional Generative Models by OpenAI

Researchers have developed a new conditional generative model for 3D assets called Shap-E. Unlike previous 3D generative models that produce a single output representation, Shap-E generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields.

Shap-E is trained in two stages. First, an encoder is trained to map 3D assets into the parameters of an implicit function, and then a conditional diffusion model is trained on the encoder's outputs. When trained on a large dataset of paired 3D and text data, Shap-E can generate complex and diverse 3D assets within seconds.

Compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality, despite modeling a higher-dimensional, multi-representation output space. The researchers have released the model weights, inference code, and samples to the public, paving the way for new possibilities in 3D asset generation.

Shap-E represents a significant breakthrough in 3D asset generation and has a wide range of applications, including video games, virtual reality, and architecture. With its ability to generate complex and diverse 3D assets quickly, Shap-E has the potential to revolutionize the field of 3D asset creation.

Shap-E is a cutting-edge generative model for 3D assets developed by OpenAI. It is designed to directly generate the parameters of implicit functions that can be rendered as textured meshes and neural radiance fields, allowing for the creation of complex and diverse 3D assets in a matter of seconds. Shap-E available to the public through their Github repository, along with the model weights, inference code, and samples.

Alongside Shap-E, OpenAI already has DALLE-2, a generative model that can create images from natural language prompts. Building on the success of its predecessor, DALL·E, which was the first model to generate images from natural language prompts, DALLE-2 uses a more diverse and larger dataset of text-image pairs and a more powerful transformer architecture. With its ability to handle longer sequences and higher resolutions, DALLE-2 can generate images from natural language prompts by encoding the text into a latent vector and decoding it into an image using an autoregressive pixel decoder. Additionally, DALLE-2 can produce natural language captions from images by reversing this process and using an autoregressive text decoder to generate text from an image-encoded latent vector.