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What is Text2Mesh?

Text2Mesh creates complex geometric shapes and vibrant colors from different source meshes, all driven by a text prompt provided by the user. Our stylization method skillfully merges unique and often disparate text inputs, effectively reflecting both general meanings and detailed features tailored to specific parts of the mesh. This innovative system enhances a 3D model by predicting appropriate colors and fine geometric details that resonate with the given text prompt. We utilize a disentangled representation of a 3D object, incorporating a static mesh as content alongside a neural network that we call the neural style field network. To modify the style, we assess a similarity score between the descriptive text of the style and the resulting stylized mesh, utilizing CLIP’s powerful representational strengths. What distinguishes Text2Mesh is its capability to function without relying on any prior generative model or a dedicated dataset of 3D meshes. Additionally, it can adeptly handle lower-quality meshes, which may include problematic non-manifold structures and various topological complexities, all without requiring UV parameterization. This remarkable versatility positions Text2Mesh as a valuable resource for artists and developers eager to effortlessly produce stylized 3D models, opening up new avenues for creative exploration. Ultimately, Text2Mesh not only enhances the artistic process but also streamlines the workflow for 3D model creation, making artistic expression more accessible than ever before.

What is GET3D?

We develop a three-dimensional signed distance field (SDF) alongside a textured field using two latent codes. To extract a 3D surface mesh from the SDF, we utilize DMTet, sampling the texture field at surface points for color information. Our training process includes adversarial losses centered on 2D images, employing a rasterization-based differentiable renderer to generate both RGB visuals and silhouettes. To differentiate between real and generated inputs, we introduce two distinct 2D discriminators—one dedicated to RGB images and the other to silhouettes. The entire system is structured to enable end-to-end training. As various industries shift towards creating expansive 3D virtual environments, the necessity for scalable tools capable of generating large volumes of high-quality and diverse 3D content becomes increasingly evident. Our research aims to develop robust 3D generative models that produce textured meshes, facilitating their seamless integration into 3D rendering engines for immediate deployment in a range of applications. This strategy not only addresses the challenge of scalability but also opens up new avenues for innovative uses in fields like virtual reality and gaming. Moreover, by enhancing the quality and diversity of 3D content, we aim to push the boundaries of creativity and interactivity within these immersive environments.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Not specified

Training Options

Documentation Hub

Company Facts

Organization Name

Text2Mesh

Company Website

threedle.github.io/text2mesh/

Company Facts

Organization Name

NVIDIA

Company Location

United States

Company Website

nv-tlabs.github.io/GET3D/

Categories and Features

AI Tools

Not specified

Categories and Features

AI Tools

Not specified

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