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Description
Recent advancements in the realm of text-to-image synthesis have emerged from diffusion models that have been trained on vast amounts of image-text pairs. To successfully transition this methodology to 3D synthesis, it would necessitate extensive datasets of labeled 3D assets alongside effective architectures for denoising 3D information, both of which are currently lacking. In this study, we address these challenges by leveraging a pre-existing 2D text-to-image diffusion model to achieve text-to-3D synthesis. We propose a novel loss function grounded in probability density distillation that allows a 2D diffusion model to serve as a guiding principle for the optimization of a parametric image generator. By implementing this loss in a DeepDream-inspired approach, we refine a randomly initialized 3D model, specifically a Neural Radiance Field (NeRF), through gradient descent to ensure its 2D renderings from various angles exhibit a minimized loss. Consequently, the 3D representation generated from the specified text can be observed from multiple perspectives, illuminated with various lighting conditions, or seamlessly integrated into diverse 3D settings. This innovative method opens new avenues for the application of 3D modeling in creative and commercial fields.
Description
Imagen 3 represents the latest advancement in Google's innovative text-to-image AI technology. It builds upon the strengths of earlier versions and brings notable improvements in image quality, resolution, and alignment with user instructions. Utilizing advanced diffusion models alongside enhanced natural language comprehension, it generates highly realistic, high-resolution visuals characterized by detailed textures, vibrant colors, and accurate interactions between objects. In addition, Imagen 3 showcases improved capabilities in interpreting complex prompts, which encompass abstract ideas and scenes with multiple objects, all while minimizing unwanted artifacts and enhancing overall coherence. This powerful tool is set to transform various creative sectors, including advertising, design, gaming, and entertainment, offering artists, developers, and creators a seamless means to visualize their ideas and narratives. The impact of Imagen 3 on the creative process could redefine how visual content is produced and conceptualized across industries.
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Web-Based
On-Premises
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Windows
Mac
Linux
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Training Docs
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Live Training (Online)
In Person
Vendor Details
Company Name
DreamFusion
Website
dreamfusion3d.github.io
Vendor Details
Company Name
Founded
1998
Country
United States
Website
deepmind.google/technologies/imagen-3/