Average Ratings 0 Ratings
Average Ratings 0 Ratings
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.
Description
WorldClaw represents a comprehensive framework that facilitates the generation of expansive, freely navigable, and modifiable 3D open worlds, all derived from open-ended text prompts. Instead of constructing an entire world in one go, it employs a strategy that transitions from a broad overview to detailed regional features, ensuring spatial coherence while enriching local attributes. Initially, planning agents convert the text prompt into a structured scene specification that includes regions, terrain, assets, materials, visual aesthetics, and spatial arrangements. It establishes a globally consistent terrain base by utilizing semantic layouts, reusable assets, generative or procedural materials, and height fields that are sensitive to regional context. For areas that demand more intricate detail, WorldClaw generates terrain-conditioned compositions, reconstructs editable textured meshes, and appropriately places them within the scene. Subsequently, render-based agents enhance the terrain geometry, fine-tune object appearances, optimize arrangements, and ensure proper interactions with the surrounding environment. This multi-layered approach allows for both the creation of vast landscapes and the intricate detailing needed for immersive exploration.
API Access
Has API
API Access
Has API
Integrations
No details available.
Integrations
No details available.
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
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
Text2Mesh
Website
threedle.github.io/text2mesh/
Vendor Details
Company Name
Tencent
Founded
1998
Country
China
Website
tencent-hunyuan.github.io/Hunyuan3D-WorldClaw/