Initial commit: generat3D, Daz DSON evaluator for Linux

generat3D evaluates Daz DSON scenes (.duf plus a content library)
natively on Linux and writes the .dbz file used by Blender's
Diffeomorphic DAZ Importer, removing the need for Daz Studio.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

1 files changed, 49 insertions, 0 deletions

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+# generat3D
+
+Use Daz 3D characters on Linux without Daz Studio. generat3D reads a Daz
+scene (`.duf`) and your installed content library, evaluates the figure's
+morphs and formulas the way Daz does, and writes the `.dbz` file that the
+[Diffeomorphic DAZ Importer](https://github.com/Diffeomorphic/import_daz)
+for Blender uses for exact mesh fitting. Normally that file can only be
+produced by running a script inside Daz Studio, which has no Linux version.
+
+Status: in design. Version 1 targets Genesis 9 figures: shape morphs,
+formula links between dials, and joint centers. Clothing fitting, HD morphs
+and geografts come later.
+
+generat3D is not affiliated with or endorsed by Daz 3D. It ships no Daz
+content: you need your own licensed content library.
+
+## Requirements
+
+- Python 3.11 or newer
+- numpy
+- A Daz content library (product zips extracted into a library folder)
+
+## Usage
+
+Planned interface:
+
+```
+python -m generat3d scene.duf [-L ROOT ...] [-o OUT.dbz]
+```
+
+Library roots come from `-L` or from `~/.config/generat3d/config.toml`:
+
+```toml
+library_roots = ["/home/you/Daz/My Library"]
+```
+
+## License
+
+Copyright (C) 2026 Danilo M. <danix@danix.xyz>
+
+GPLv2 or later. See [LICENSE](LICENSE) for the full text.
+
+## Development Approach
+
+This project is developed using AI-assisted tools. Code is generated with the help of AI based on human-provided specifications, design decisions, and iterative feedback.
+
+All contributions are reviewed, tested, and curated by the maintainer before being included in the codebase. AI is used as a productivity and exploration tool, while human oversight remains central to all decisions.
+
+The goal is to combine the flexibility of AI-assisted development with standard open-source practices such as transparency, review, and accountability.