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README.md

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 ![Overview](./images/teaser3.jpg)
 **Figure 1.** Our deep generative network DSM-Net encodes 3D shapes with complex structure and fine geometry in a representation that leverages the synergy between geometry and structure, while disentangling these two aspects as much as possible. This enables novel modes of controllable generation for high-quality shapes. Left: results of disentangled interpolation. Here, the top left and bottom right chairs (highlighted with red rectangles) are the input shapes. The remaining chairs are generated automatically with our DSM-Net, where in each row, the `structure` of the shapes is interpolated while keeping the geometry unchanged, whereas in each column, the `geometry` is interpolated while retaining the structure. Right: shape generation results with complex structure and fine geometry details by our DSM-Net. We show close-up views in dashed yellow rectangles to highlight local details.
 
-## Introduction
+## Introduction 
 We introduce DSM-Net, a deep neural network that learns a disentangled structured mesh representation for 3D shapes, where two key aspects of shapes, geometry and structure, are encoded in a synergistic manner to ensure plausibility of the generated shapes, while also being disentangled as much as possible. This supports a range of novel shape generation applications with intuitive control, such as interpolation of structure (geometry) while keeping geometry (structure) unchanged.