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
Seed3D
Seed3D 1.0 is a foundation-model pipeline that takes a single input image and generates a simulation-ready 3D asset, including closed manifold geometry, UV-mapped textures, and physically-based rendering material maps, designed for immediate integration into physics engines and embodied-AI simulators. It uses a hybrid architecture combining a 3D variational autoencoder for latent geometry encoding, and a diffusion-transformer stack to generate detailed 3D shapes, followed by multi-view texture synthesis, PBR material estimation, and UV texture completion. The geometry branch produces watertight meshes with fine structural details (e.g., thin protrusions, holes, text), while the texture/material branch yields multi-view consistent albedo, metallic, and roughness maps at high resolution, enabling realistic appearance under varied lighting. Assets generated by Seed3D 1.0 require minimal cleanup or manual tuning.
Learn more about Seed3D
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 about GET3D
3D-Agent
3D-Agent is a Blender AI agent and AI 3D model generator that works inside your open Blender scene. Describe what you want and it builds native, editable geometry with clean quad topology and UVs, then handles materials, lighting, animation, rendering and Blender Python. Follow-up prompts refine what is already in the scene.
Key features:
- Text to 3D in Blender: real Blender objects, not a downloaded mesh
- Blender MCP server (Ultra): Claude Code, Claude Desktop or Cursor hand Blender tasks to 3D-Agent
- Roblox Studio AI: edit scripts, insert assets, run playtests
- AI retopology and UVs for meshes from other generators
- Built-in AI image and video generation
- Export OBJ, FBX, GLB, USDZ and STL for games and 3D printing
Requirements: Blender 4.2+, macOS 12+ or Windows 10+. Installs its own Blender extension; no API key.
Pricing: no free tier. Basic $19/month (100 prompts), Pro $29/month (200), Ultra $89/month (800).
Learn more about 3D-Agent
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 about Point-E
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
Custom VMs From 1 to 96 vCPUs With 99.95% Uptime
Live migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
Product Details
Platforms Supported
Cloud
Training
Documentation
Support
Online
Shap-E Frequently Asked Questions
Q: What kinds of users and organization types does Shap-E work with?
Shap-E works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does Shap-E support in their product?
Shap-E supports these languages: English.
Q: What kind of support options does Shap-E offer?
Shap-E offers support via online.
Q: What other applications or services does Shap-E integrate with?
Shap-E integrates with: Blender.
Q: What type of training does Shap-E provide?
Shap-E provides training in the form of documentation.
Q: How much does Shap-E cost?
Pricing for Shap-E starts at Free.
