NVIDIA Unveils LATTE3D: A Text-to-3D Modelfor Rapid Object Generation
Toronto, Canada – NVIDIA’s Toronto AIlab has made a significant leap in the field of 3D content creation with the release of LATTE3D, a revolutionary text-to-3Dmodel capable of generating high-quality 3D objects from text prompts in a mere 400 milliseconds. This groundbreaking technology leverages an amortized optimization approach, training a shared text-conditioned model on a vast dataset of text prompts, enabling it to generalize effectively to new prompts and drastically reduce the time required to generate individual 3D objects.
LATTE3D’s ability to translate textual descriptions into intricate 3D models opens up exciting possibilities for various industries, from gaming and film to architecture and design. Imagine crafting a detailed 3D model of a fluffy, pink unicorn wearing a cowboy hat simply by typing the description. LATTE3D’s speed and accuracy make it a game-changer, offering real-time feedback and allowing users to iterate their designs swiftly.
Key Features of LATTE3D:
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Text-to-3D Synthesis: LATTE3D excels atgenerating 3D models based on textual prompts, enabling users to create objects with specific features and styles. For instance, a prompt like a robotic cat with glowing eyes will result in a 3D model embodying those characteristics.
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Rapid Generation: The model’s impressive speed, generating 3Dobjects in approximately 400 milliseconds, allows for near-instantaneous responses to user input, providing real-time visual feedback and enhancing the creative process.
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High-Quality Rendering: LATTE3D combines neural fields and textured surface generation to produce highly detailed textured meshes, resulting in visually compelling 3D renderings.
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3D Stylization: Beyond generating new objects, LATTE3D can also be used as a 3D stylization tool, allowing users to apply new styles or themes to existing 3D assets, creating diverse visual representations.
Architectural Approach:
LATTE3D’s training process involves two stages:
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Texture and Geometry Training: In the first stage, volumetric rendering is used to train the texture and geometry of the 3D objects. To enhance prompt robustness, the training objective incorporates SDS gradients from 3D-aware image priors and a regularization loss that comparesthe predicted shape’s mask with 3D assets from a library.
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Texture Refinement: The second stage focuses on improving quality by using surface-based rendering and training only the texture network while keeping the geometry network frozen. Both stages employ amortized optimization across a set of prompts, ensuring rapid generation.
Network Architecture:
LATTE3D utilizes two networks: a texture network (T) and a geometry network (G), both composed of a combination of triplanes and U-Nets. In the first stage, the encoders of both networks share the same weight set. During the second stage, thegeometry network (G) is frozen, and the texture network (T) is updated. An MLP (Multilayer Perceptron) is used to further upsample the triplanes using the input text embedding.
Impact and Future Potential:
LATTE3D’s arrival marks a significant advancement in 3D content creation, offering a user-friendly and efficient method for generating realistic and detailed 3D objects. Its potential applications are vast, spanning across industries like:
- Gaming: Creating immersive environments and detailed character models.
- Film and Animation: Generating high-quality 3D assets for movies,TV shows, and video games.
- Architecture and Design: Designing and visualizing buildings, interiors, and product prototypes.
- E-commerce: Creating realistic 3D product models for online shopping.
As research and development in this field continue, we can expect even more sophisticated and powerful text-to-3D models to emerge, further revolutionizing the way we interact with and create 3D content. LATTE3D’s success demonstrates the immense potential of AI to democratize 3D creation and unlock new possibilities for creativity and innovation.
【source】https://ai-bot.cn/nvidia-latte3d/
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