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Awesome GitHub RepositoriesDiffusion-Based 3D Guidance
The use of pre-trained 2D diffusion models as loss functions to guide the optimization of 3D shapes.
Distinct from Diffusion Models: Focuses on using diffusion as a guidance signal for 3D geometry rather than training 2D diffusion models.
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Awesome Diffusion-Based 3D Guidance GitHub Repositories
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- ashawkey/stable-dreamfusion
ashawkey/stable-dreamfusion
8,841Voir sur GitHubThis project is a diffusion-based 3D generator and image-to-3D reconstruction system. It translates natural language descriptions or two-dimensional images into three-dimensional assets using neural radiance fields and diffusion models. The system utilizes score-distillation sampling and diffusion-based guidance to refine 3D shapes without requiring 3D training data. It includes specialized tools for transforming neural representations into exportable meshes with texture and material data, as well as a pipeline for iterative optimization of geometry and textures. The project covers a broad r
Uses a pre-trained image generation model as a loss function to refine 3D shapes without 3D training data.
Python
