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Description

ALBERT is a self-supervised Transformer architecture that undergoes pretraining on a vast dataset of English text, eliminating the need for manual annotations by employing an automated method to create inputs and corresponding labels from unprocessed text. This model is designed with two primary training objectives in mind. The first objective, known as Masked Language Modeling (MLM), involves randomly obscuring 15% of the words in a given sentence and challenging the model to accurately predict those masked words. This approach sets it apart from recurrent neural networks (RNNs) and autoregressive models such as GPT, as it enables ALBERT to capture bidirectional representations of sentences. The second training objective is Sentence Ordering Prediction (SOP), which focuses on the task of determining the correct sequence of two adjacent text segments during the pretraining phase. By incorporating these dual objectives, ALBERT enhances its understanding of language structure and contextual relationships. This innovative design contributes to its effectiveness in various natural language processing tasks.

Description

Text2Mesh generates intricate geometric and color details across various source meshes, guided by a specified text prompt. The results of our stylization process seamlessly integrate unique and seemingly unrelated text combinations, effectively capturing both overarching semantics and specific part-aware features. Our system, Text2Mesh, enhances a 3D mesh by predicting colors and local geometric intricacies that align with the desired text prompt. We adopt a disentangled representation of a 3D object, using a fixed mesh as content integrated with a learned neural network, which we refer to as the neural style field network. To alter the style, we compute a similarity score between the style-describing text prompt and the stylized mesh by leveraging CLIP's representational capabilities. What sets Text2Mesh apart is its independence from a pre-existing generative model or a specialized dataset of 3D meshes. Furthermore, it is capable of processing low-quality meshes, including those with non-manifold structures and arbitrary genus, without the need for UV parameterization, thus enhancing its versatility in various applications. This flexibility makes Text2Mesh a powerful tool for artists and developers looking to create stylized 3D models effortlessly.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Spark NLP

Integrations

Spark NLP

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

github.com/google-research/albert

Vendor Details

Company Name

Text2Mesh

Website

threedle.github.io/text2mesh/

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