cwchenwang/awesome-3d-diffusion
A categorized academic paper list covering diffusion-based 3D generation methods including text-to-3D, image-to-3D, and 3D editing approaches.
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This repository aggregates research papers on using diffusion models for 3D content generation. It organizes papers by approach including 2D diffusion with pretraining, 2D diffusion without pretraining, diffusion in 3D space, and diffusion for motion. The collection includes a linked survey paper providing a systematic overview of the field and serves as a reference for researchers working on generative AI for 3D.
Frequently asked
- What is cwchenwang/awesome-3d-diffusion?
- A categorized academic paper list covering diffusion-based 3D generation methods including text-to-3D, image-to-3D, and 3D editing approaches.
- Is awesome-3d-diffusion open source?
- Yes — cwchenwang/awesome-3d-diffusion is open source, released under the MIT license.
- How popular is awesome-3d-diffusion?
- cwchenwang/awesome-3d-diffusion has 1.3k stars on GitHub.
- Where can I find awesome-3d-diffusion?
- cwchenwang/awesome-3d-diffusion is on GitHub at https://github.com/cwchenwang/awesome-3d-diffusion.