Audience
Developers and game makers in need of a solution to generate 3D objects conditioned on text or images
About Shap-E
This is the official code and model release for Shap-E. Generate 3D objects conditioned on text or images. Sample a 3D model, conditioned on a text prompt, or conditioned on a synthetic view image. To get the best result, you should remove the background from the input image. Load 3D models or a trimesh, and create a batch of multiview renders and a point cloud encode them into a latent and render it back. For this to work, install Blender version 3.3.1 or higher.
Other Popular Alternatives & Related Software
V2Fun
V2Fun is an all-in-one, browser-based AI 3D creation platform that turns text prompts, reference images, and ordinary videos into production-ready 3D models and animations. Users can generate structurally accurate, perspective-correct concept images, create high-fidelity assets from text or single- and multi-view images, and use built-in prompt optimization to enrich inputs with spatial perspective, PBR materials, and fine texture parameters. Its image-to-3D and text-to-3D engines preserve artistic style, character traits, structural detail, and natural lighting while supporting rapid iteration for characters, scenes, game assets, industrial models, and 3D-printable objects. Smart retopology automatically produces clean, lightweight, editable quad meshes without sacrificing surface detail, while the AI texture generator creates or swaps complete PBR material sets with accurate lighting, bump, gloss, and roughness information.
Learn more
GET3D
We generate a 3D SDF and a texture field via two latent codes. We utilize DMTet to extract a 3D surface mesh from the SDF and query the texture field at surface points to get colors. We train with adversarial losses defined on 2D images. In particular, we use a rasterization-based differentiable renderer to obtain RGB images and silhouettes. We utilize two 2D discriminators, each on RGB image, and silhouette, respectively, to classify whether the inputs are real or fake. The whole model is end-to-end trainable. As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train performant 3D generative models that synthesize textured meshes which can be directly consumed by 3D rendering engines, thus immediately usable in downstream applications.
Learn more
Point-E
While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to produce a single sample. This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes. In this paper, we explore an alternative method for 3D object generation which produces 3D models in only 1-2 minutes on a single GPU. Our method first generates a single synthetic view using a text-to-image diffusion model and then produces a 3D point cloud using a second diffusion model which conditions the generated image. While our method still falls short of the state-of-the-art in terms of sample quality, it is one to two orders of magnitude faster to sample from, offering a practical trade-off for some use cases. We release our pre-trained point cloud diffusion models, as well as evaluation code and models, at this https URL.
Learn more
Magic3D
Together with image conditioning techniques as well as prompt-based editing approach, we provide users with new ways to control 3D synthesis, opening up new avenues to various creative applications. Magic3D can create high-quality 3D textured mesh models from input text prompts. It utilizes a coarse-to-fine strategy leveraging both low- and high-resolution diffusion priors for learning the 3D representation of the target content. Magic3D synthesizes 3D content with 8× higher-resolution supervision than DreamFusion while also being 2× faster. Given a coarse model generated with a base text prompt, we can modify parts of the text in the prompt, and then fine-tune the NeRF and 3D mesh models to obtain an edited high-resolution 3D mesh.
Learn more
Pricing
Starting Price:
Free
Free Version:
Free Version available.
Integrations
Company Information
OpenAI
United States
github.com/openai/shap-e
Videos and Screen Captures
Other Useful Business Software
$300 Free Credits for Your Google Cloud Projects
Launch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
Product Details
Platforms Supported
Cloud
Training
Documentation
Support
Online
