Shap-E Description
This is the formal release of the Shap-E code and model, which allows users to create 3D objects based on textual descriptions or images. You can generate a 3D model by providing a text prompt or a synthetic view image, and for optimal results, it's recommended to eliminate the background from the input image. Additionally, you can load 3D models or trimeshes, produce a series of multiview renders, and encode them into a point cloud, which can then be reverted to a visual format. To utilize these features effectively, ensure that you have Blender version 3.3.1 or a more recent version installed on your system. This opens up exciting possibilities for integrating 3D modeling with AI-driven creativity.
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Picsart Enterprise
AI-powered Image & video editing for seamless integration.
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Accessible to everyone:
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Why Picsart?
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Point-E
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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Magic3D
By incorporating image conditioning techniques alongside a prompt-based editing method, we offer users innovative ways to manipulate 3D synthesis, paving the way for various creative possibilities. Magic3D excels in generating high-quality 3D textured mesh models based on textual prompts. It employs a coarse-to-fine approach that utilizes both low- and high-resolution diffusion priors to effectively learn the 3D representation of the desired content. Moreover, Magic3D produces 3D content with 8 times the resolution supervision compared to DreamFusion, while also operating at twice the speed. Once a rough model is created from an initial text prompt, we can alter elements of the prompt and subsequently fine-tune both the NeRF and 3D mesh models, resulting in an enhanced high-resolution 3D mesh. This versatility not only enhances user creativity but also streamlines the workflow for producing detailed 3D visualizations.
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Pricing
Pricing Starts At:
Free
Free Version:
Yes
Integrations
Company Details
Company:
OpenAI
Headquarters:
United States
Website:
github.com/openai/shap-e
Media
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support
Shap-E Features and Options
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