DeepTree: Modeling Trees with Situated Latents
Xiaochen Zhou Bosheng Li Bedrich Benes Songlin Fei Sören Pirk
In this paper, we propose DeepTree, a novel method for modeling trees based
on learning developmental rules for branching structures instead of manually
defining them. We call our deep neural model situated latent because its
behavior is determined by the intrinsic state -- encoded as a latent space of a
deep neural model -- and by the extrinsic (environmental) data that is situated
as the location in the 3D space and on the tree structure. We use a neural
network pipeline to train a situated latent space that allows us to locally
predict branch growth only based on a single node in the branch graph of a tree
model. We use this representation to progressively develop new branch nodes,
thereby mimicking the growth process of trees. Starting from a root node, a
tree is generated by iteratively querying the neural network on the newly added
nodes resulting in the branching structure of the whole tree. Our method
enables generating a wide variety of tree shapes without the need to define
intricate parameters that control their growth and behavior. Furthermore, we
show that the situated latents can also be used to encode the environmental
response of tree models, e.g., when trees grow next to obstacles. We validate
the effectiveness of our method by measuring the similarity of our tree models
and by procedurally generated ones based on a number of established metrics for
tree form.