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.

本文引入了 DeepTree 方法，该方法利用深度神经网络对树木的生长规则进行建模，从而生成各种形态的树木，同时展示了这种方法的有效性。

DeepTree：利用情境潜变量建模树木

DeepTree: Modeling Trees with Situated Latents

We present an interactive approach to synthesizing realistic variations in
facial hair in images, ranging from subtle edits to existing hair to the
addition of complex and challenging hair in images of clean-shaven subjects. To
circumvent the tedious and computationally expensive tasks of modeling,
rendering and compositing the 3D geometry of the target hairstyle using the
traditional graphics pipeline, we employ a neural network pipeline that
synthesizes realistic and detailed images of facial hair directly in the target
image in under one second. The synthesis is controlled by simple and sparse
guide strokes from the user defining the general structural and color
properties of the target hairstyle. We qualitatively and quantitatively
evaluate our chosen method compared to several alternative approaches. We show
compelling interactive editing results with a prototype user interface that
allows novice users to progressively refine the generated image to match their
desired hairstyle, and demonstrate that our approach also allows for flexible
and high-fidelity scalp hair synthesis.

采用神经网络处理的图像合成方法，通过人工繁琐的建模，渲染和复合形成目标发型的三维几何形状来合成真实的面部毛发变化，使用户能够通过简单的引导笔画来进一步指导和修整所生成的图像。此方法不仅可以控制结构和颜色特征，而且发夹名单可以灵活地扩展和生成高保真头皮发。