mlvlab/DDMI
Official Implementation (Pytorch) of "DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations", ICLR 2024
This project offers a method for generating high-quality synthetic images, videos, 3D shapes, and neural radiance fields (NeRFs) from existing datasets. It takes collections of visual data (like images of animals or videos of skies) or 3D models and produces new, diverse, and realistic versions. This is designed for researchers in computer graphics or machine learning who need to create new synthetic data for experiments or applications.
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Use this if you are a researcher or practitioner working with generative AI and need to synthesize high-fidelity visual data across different modalities like 2D images, videos, 3D objects, or NeRF scenes.
Not ideal if you need a user-friendly application for direct content creation without deep technical understanding of machine learning models.
Stars
27
Forks
6
Language
Python
License
MIT
Category
Last pushed
Jun 24, 2024
Commits (30d)
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