论文标题
DualSDF：使用两级表示的语义形状操纵
DualSDF: Semantic Shape Manipulation using a Two-Level Representation
论文作者
论文摘要
我们看到3D形状表示的寒武纪爆炸，以用于机器学习。一些代表在捕获高分辨率细节时寻求高表达能力。其他方法试图将形状表示为简单零件的组成，这些零件是直观的，人们可以理解和易于编辑和操纵。但是，很难在同一表示中实现忠诚和解释性。我们提出了DualSDF，这是一种表示形状的表示形状的两个层次，一个捕获细节，另一个代表使用简单和语义上一致的形状原始素代表抽象的代理形状。为了在两个表示之间实现紧密的耦合，我们在共享潜在空间上使用一个变异目标。我们的两级模型产生了一种新的形状操纵技术，在该技术中，用户可以交互性地操纵粗糙的代理形状，并立即以高分辨率形状镜像。此外，我们的模型会积极增加并指导操纵以产生语义有意义的形状，从而使用户输入最少的复杂操作成为可能。
We are seeing a Cambrian explosion of 3D shape representations for use in machine learning. Some representations seek high expressive power in capturing high-resolution detail. Other approaches seek to represent shapes as compositions of simple parts, which are intuitive for people to understand and easy to edit and manipulate. However, it is difficult to achieve both fidelity and interpretability in the same representation. We propose DualSDF, a representation expressing shapes at two levels of granularity, one capturing fine details and the other representing an abstracted proxy shape using simple and semantically consistent shape primitives. To achieve a tight coupling between the two representations, we use a variational objective over a shared latent space. Our two-level model gives rise to a new shape manipulation technique in which a user can interactively manipulate the coarse proxy shape and see the changes instantly mirrored in the high-resolution shape. Moreover, our model actively augments and guides the manipulation towards producing semantically meaningful shapes, making complex manipulations possible with minimal user input.
