Procedural Generation of Tree Models in Blender
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AI applications are only beginning to materialize. It's so exciting what neural networks will do to art workflows in the coming years.

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Amazing... Congratulations for the new way to show information.. I hope they could use this to teaching.

Procedural Generation of Tree Models in Blender
13 July, 2017
News

Charlie Hewitt is working on a project that can change the way you work on vegetation in Blender. The developer wants to build on the principles set out by Lindenmayer in order to procedurally generate realistic 3D models of trees, incorporating modern graphics techniques to enhance the output of conventional Lindenmayer systems. What this means is that the talent is working on a fully usable Blender plugin that can drastically optimize your workflow. 

Here is what Charlie posted on Github

Tree Samples

Read the write up here.

I’d love to develop this into a fully usable blender plugin but don’t currently have the time, if anyone would like to I’d very much support this.

If you want to try out the code as is:

  • Copy ch_trees into the blender addons folder (…/blender.app/Contents/Resources/2.78/scripts/addons on mac)
  • Restart blender
  • Start a new blend file and in the text editor panel open …/ch_trees/parametric/gen.py or …/ch_trees/lsystems/treegen.py
  • Uncomment the final few lines including construct(…)
  • Run the script (it will take a few seconds, or more, to generate the tree so don’t worry if blender appears to freeze – if you open blender through the command line then you can track the progress)
  • You can change the quaking_aspen bit inside construct(…) to generate different types of trees – see …/parametric/tree_params and …/lsystems/sys_defs for available presets. You can also edit/create new tree types.

(apologies for such a protracted process, this is why I’d love to incorporate it into an actual plugin – hopefully I or someone else will get around to this soon)

Charlie Hewitt

You can get the files here. Want to know more? Here is the full paper with all the details on the model. You can even use the data to create your own plugin and share it with us. 

Source: GitHub

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