Vidya Narayanan

I am a fourth year graduate student in the computer science department at Carnegie Mellon University, advised by Jim McCann. I am associated with the Textiles lab at CMU. I am broadly interested in fabrication, graphics and visualization.

Before joining CMU, I was a research associate at Disney Research, Pittsburgh advised by Jim McCann. I earned my masters degree at the Indian Institute of Science, focusing on graphics and scientific visualization and was advised by Vijay Natarajan.

Email | CV

Research Interests

I am interested in computational tools for fabrication, computer graphics, and visualization. I believe existing fabrication machinery such as knitting machines and weaving looms have been largely overlooked as technology that can be used for custom and rapid fabrication much like 3D printers. My current research looks at automatic and semi-automatic tools for fabricating textiles, particularly with computational machine knitting.


Visual Knitting Machine Programming
Vidya Narayanan , Kui Wu (co-first author), Cem Yuksel and James McCann
paper | project page

In this paper, we present the first general visual programming interface for creating 3D objects with complex surface finishes on industrial knitting machines. At the core of our interface is a new, augmented stitch mesh datastructure that stores low-level knitting operations per-face and encodes the dependencies between faces using directed edge labels. Our system can generate knittable augmented stitch meshes from 3D models, allows users to edit these meshes in a way that preserves their knittability, and can schedule the execution order and location of each face for production on a knitting machine. We demonstrate the power and flexibility of our pipeline by using it to create and knit objects featuring a wide range of patterns and textures, including intarsia and Fair Isle colorwork; knit and purl textures; cable patterns; and laces.


Efficient Transfer Planning for Flat Knitting
Jenny Lin, Vidya Narayanan and James McCann
ACM Symposium on Computational Fabrication, 2018
paper | project page

Industrial knitting machines form fabric by manipulating loops of yarn held on hundreds of hook-shaped needles. Transfer planning algorithms generate a sequence of machine instructions that move loops between their current needles and given target needles. In this paper we describe how to compute the run-time cost of a transfer plan and compare the plans generated by several existing and new transfer planning algorithms.


Automatic Machine Knitting of 3D Meshes
Vidya Narayanan , Lea Albaugh, Jessica Hodgins, Stelian Coros and James McCann
ACM Transactions on Graphics, 2018
paper | project page

We present the first computational approach that can transform 3D meshes, created by traditional modeling programs, directly into instructions for a computer-controlled knitting machine.


A compiler for 3D Machine Knitting
James McCann, Lea Albaugh, Vidya Narayanan , April Grow,Wojciech Matusik, Jen Mankoff, Jessica Hodgins
ACM Transactions on Graphics (SIGGRAPH), 2016
paper | project page

Industrial knitting machines can produce finely detailed 3D surfaces but. programming them requires in depth knowledge of low-level knitting operations. In this work, we built a compiler to convert high level design primitives into knitting machine instructions.


An exploratory framework for cyclone identification and tracking
Akash Anil Valsangkar, Joy Merwin Monteiro, Vidya Narayanan , Ingrid Hotz, Vijay Natarajan
IEEE Transactions on Visualization and Computer Graphics, 2018
paper | project page

Analyzing depressions plays an important role in meteorology, especially in the study of cyclones. In particular, the study of the temporal evolution of cyclones requires a robust depression tracking framework. We propose a pipeline for the exploration of cyclones and their temporal evolution that combines the robustness of topological approaches and the detailed tracking information from optical flow analysis.


Distance between extremum graphs
Vidya Narayanan , Dilip Thomas, Vijay Natarajan
IEEE Pacific Visualization Symposium(PacificVis), 2015
paper | project page

Scientific phenomena are often studied through collections of related scalar fields. Exploration of such data requires a robust distance measure to compare scalar fields for tasks such as identifying key events and establishing correspondence between features in the data. We propose a topological data structure called the complete extremum graph and define a distance measure on it for comparing scalar fields in a feature-aware manner.

Other Projects
A bunch of knitting experiments and tutorials can be found here

Instruction reordering for optimizing machine knitting (with Laxman Dhulipala, Spring 2018)

For a compilers class, we optimized knitting machine programs written in knitout -- a machine knitting language -- to improve efficiency for flat transfer planning by reordering instructions (poster). Some of these ideas were used in the visual knitting machine programming system.


Modeling knittable geometry (Fall 2017)

For a computational geometry class, I built an interactive modeler for machine knittable geometry which could be refined using a modified subdivision scheme (report). Some of these ideas went into building the visual knitting machine programming system.


2D Games as Cyber Physical Systems (Spring 2017)

For a cyber-physical systems class, I explored treating simple single player 2D game as a cyber physical system where playability and non-triviality can be gauranteed by a level design system (slides).


Computational String Art (Fall 2016)

String or pin-thread art is a popular craft that involves winding a string around a set of nails to generate an artifact. An important task in automatic fabrication of such art work is planning the string layout to achieve the target representation. We explored this planning problem for generating string-art from images automatically. Motivated by artists (see Petros Vrellis , Kumi Yamashita ), we built an automatic framework to design such artifacts. Turns out that various people have been looking at similar ideas. Here is a poster I made for a class project on this topic.


Tactile learning (with Hannah Rosen and Gary Li, Fall 2016)

We compared various approaches to generate tactile versions of images to effectively communicate illustrations with individuals with visual impairment. Here are the slides from our presentation for the applied fabrication techniques class.

Last updated March 2020.

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