Live Programming Environment for Deep Learning with Instant and Editable Neural Network Visualization

Artificial intelligence (AI) such as deep learning has achieved significant success in a variety of application domains. Several visualization techniques have been proposed for understanding the overall behavior of the neural network defined by deep learning code. However, they show visualization only after the code or network definition is written and it remains complicated and unfriendly for newbies to build deep neural network models on a code editor. In this paper, to help user better understand the behavior of networks, we augment a code editor with instant and editable visualization of network model, inspired by live programming which provides continuous feedback to the programmer

PerfectTailor: Scale-Preserving 2D Pattern Adjustment Driven by 3D Garment Editing

We address the problem of modifying a given well-designed 2D sewing pattern to accommodate garment edits in the 3D space. Existing methods usually adjust the sewing pattern by applying uniform flattening to the 3D garment. The problems are twofold: first, it ignores local scaling of the 2D sewing pattern such as shrinking ribs of cuffs; second, it does not respect the implicit design rules and conventions of the industry, such as the use of straight edges for simplicity and precision in sewing. To address those problems, we present a pattern adjustment method that considers the non-uniform local scaling of the 2D sewing pattern by utilizing the intrinsic scale matrix. In addition, we preserve the original boundary shape by an as-similar-as-possible geometric constraint when desirable. We build a prototype with a set of commonly used alteration operations and showcase the capability of our method via a number of alteration examples throughout the paper.Comment: 7 page

A User Study Comparing 3D Modeling with Silhouettes and Google SketchUp

We describe a user study comparing 3D Modeling with Silhouettes and Google SketchUp. In the user study, ten users were asked to create 3D models of three different objects, using either 3D Modeling with Silhouettes or Google SketchUp. Ten different users were then asked to rank images of the models produced by the first group. We show that the models made with 3D Modeling with Silhouettes were ranked significantly higher on average than those made with Google SketchUp

2.5D cartoon models

We present a way to bring cartoon objects and characters into the third dimension, by giving them the ability to rotate and be viewed from any angle. We show how 2D vector art drawings of a cartoon from different views can be used to generate a novel structure, the 2.5D cartoon model, which can be used to simulate 3D rotations and generate plausible renderings of the cartoon from any view. 2.5D cartoon models are easier to create than a full 3D model, and retain the 2D nature of hand-drawn vector art, supporting a wide range of stylizations that need not correspond to any real 3D shape.MathWorks, Inc. (Fellowship