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HAAR
Overview :
HAAR is a text-input based generation model capable of generating realistic 3D hairstyles. It takes text prompts as input and generates 3D hairstyle assets ready for various computer graphics animation applications. Unlike current AI-based generation models, HAAR utilizes 3D hair strands as the foundation representation. It automatically annotates the generated synthetic hairstyle model using a 2D visual question answering system. We propose a text-guided generation method that uses a conditional diffusion model to generate guided hair strands in the latent hairstyle UV space and reconstructs a dense hairstyle containing hundreds of thousands of hair strands using a latent upsampling process given a textual description. The generated hairstyles can be rendered using existing computer graphics techniques.
Target Users :
HAAR can be used for generating 3D hairstyles in computer graphics animation applications, especially for scenarios requiring realistic hairstyles.
Use Cases
Game Development: Use HAAR to generate realistic 3D hairstyles for game characters.
Film VFX: Apply HAAR to generate virtual character hairstyles for film projects.
Animation Production: Utilize HAAR to create realistic 3D hairstyles for animation characters.
Features
Generates realistic 3D hairstyles based on text input
Utilizes 3D hair strands as the foundation representation
Automatically annotates synthetic hairstyle models
Generates guided hair strands using a conditional diffusion model
Reconstructs dense hairstyles using a latent upsampling process
Supports rendering with existing computer graphics techniques
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