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Computational Design of Knit Templates

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We present an interactive design system for knitting that allows users to create template patterns that can be fabricated using an industrial knitting machine. Our interactive design tool is novel in that it allows direct control of key knitting design axes we have identified in our formative study and does so consistently across the variations of an input parametric template geometry. This is achieved with two key technical advances. First, we present an interactive meshing tool that lets users build a coarse quadrilateral mesh that adheres to their knit design guidelines. This solution ensures consistency across the parameter space for further customization over shape variations and avoids helices, promoting knittability. Second, we lift and formalize low-level machine knitting constraints to the level of this coarse quad mesh. This enables us to not only guarantee hand- and machine-knittability, but also provides automatic design assistance through auto-completion and suggestions. We show the capabilities through a set of fabricated examples that illustrate the effectiveness of our approach in creating a wide variety of objects and interactively exploring the space of design variations.

Benjamin Jones, Yuxuan Mei, Haisen Zhao, Taylor Gotfrid, Jennifer Mankoff, Adriana Schulz:
Computational Design of Knit Templates. ACM Trans. Graph. 41(2): 16:1-16:16 (2022)

Four pink knit dresses mounted on four mannekins. each showing different styles of neckline and skirt. Behind each dress is the pattern used to create that dress. The shape of the quads in the pattern demonstrate their relationship to typical knitting patterns -- for example a collar knit in the round has quads that narrow as they go up.

Our interactive design system helps users explore key design axes for knitting to generate highly customized patterns from input shape templates; e.g., a seamless yoke dress with princess-cut apparent seams (a), and drop shoulder dresses with textures on the arms and skirt (b–d). The output of our system is a knit pattern template that lets users vary the shape while preserving the design, for example, creating a child’s dress with short sleeves (d) that matches an adult dress (b), or varying skirt texture and angle, and sleeve knitting direction (c). The system guarantees that all results and variations are machine knittable.

A diagram showing four differently shaped duck faces (a) which all have the same mesh, which can react easily to different shapes by adjusting quad shapes. The final product of a duck with a short, and a long, snout, is shown knitted in lavendar at the right.

Overview of our framework. (a) Triangle meshes from a parametric template (the system deals with a single mesh at a time). (b) Input triangle mesh with user annotations of composition, layout, and direction guidelines. (c) Generated quad mesh patches, which are consistent across template variations. (d) Quad mesh annotated for knitting the body tube in the round using short rows to curve the tube. Blue lines indicate seams. The same design applies to all template variations (two shown here). (e) Duck knit with short rows. (f ) Quad mesh annotated with different textures and orientations; the body is knit as seamed sheets with decreases. (g) Duck knit with textures and a large head from template (f ).

Understanding Disabled Knitters

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Taylor Gotfrid, Kelly Mack, Kathryn J. Lum, Evelyn Yang, Jessica K. Hodgins, Scott E. Hudson, Jennifer Mankoff: Stitching Together the Experiences of Disabled Knitters. CHI 2021: 488:1-488:14

Knitting is a popular craft that can be used to create customized fabric objects such as household items, clothing and toys. Additionally, many knitters find knitting to be a relaxing and calming exercise. Little is known about how disabled knitters use and benefit from knitting, and what accessibility solutions and challenges they create and encounter. We conducted interviews with 16 experienced, disabled knitters and analyzed 20 threads from six forums that discussed accessible knitting to identify how and why disabled knitters knit, and what accessibility concerns remain. We additionally conducted an iterative design case study developing knitting tools for a knitter who found existing solutions insufficient. Our innovations improved the range of stitches she could produce. We conclude by arguing for the importance of improving tools for both pattern generation and modification as well as adaptations or modifications to existing tools such as looms to make it easier to track progress

KnitGIST: Generative Texture Design

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Hofmann, M., Mankoff, J., & Hudson, S. E. (2020, October). KnitGIST: A Programming Synthesis Toolkit for Generating Functional Machine-Knitting Textures. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (pp. 1234-1247).

Automatic knitting machines are robust, digital fabrication devices that enable rapid and reliable production of attractive, functional objects by combining stitches to produce unique physical properties. However, no existing design tools support optimization for desirable physical and aesthetic knitted properties. We present KnitGIST (Generative Instantiation Synthesis Toolkit for knitting), a program synthesis pipeline and library for generating hand- and machine-knitting patterns by intuitively mapping objectives to tactics for texture design. KnitGIST generates a machine-knittable program in a domain-specific programming language.

Picture of three different textures and how they look compressed and stretched.
Picture showing Stockinette, Cables, Seed stitch and Ribbing
A picture of a knit lampshade in blue and green surrounding a lit lamp on an orange table.

KnitPick: Manipulating Texture

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Knitting creates complex, soft objects with unique and controllable texture properties that can be used to create interactive objects. However, little work addresses the challenges of using knitted textures. We present KnitPick: a pipeline for interpreting pre-existing hand-knitting texture patterns into a directed-graph representation of knittable structures (KnitGraphs) which can be output to machine and hand-knitting instructions. Using KnitPick, we contribute a measured and photographed data set of 300 knitted textures. Based on findings from this data set, we contribute two algorithms for manipulating KnitGraphs. KnitCarving shapes a graph while respecting a texture, and KnitPatching combines graphs with disparate textures while maintaining a consistent shape. Using these algorithms and textures in our data set we are able to create three Knitting based interactions: roll, tug, and slide. KnitPick is the first system to bridge the gap between hand- and machine-knitting when creating complex knitted textures.

KnitPick: Programming and Modifying Complex Knitted Textures for Machine and Hand Knitting, Megan Hofmann, Lea Albaugh, Ticha Sethapakdi, Jessica Hodgins, Scott e. Hudson, James McCann, Jennifer Mankoff. UIST 2019. The KnitPick Data set can be found here.

A picture of a knit speak file which is compiled into a knit graph (which can be modified using carving and patching) and then compiled to knitout, which can be printed on a knitting machine. Below the graph is a picture of different sorts of lace textures supported by knitpick.
KnitPick converts KnitSpeak into KnitGraphs which can be carved, patched and output to knitted results
A photograph of the table with our data measurement setup, along with piles of patches that are about to be measured and have recently been measured. One patch is attached to the rods and clips used for stretching.
Data set measurement setup, including camera, scale, and stretching rig
A series of five images, each progressively skinnier than the previous. Each image is a knitted texture with 4 stars on it. They are labeled (a) original swatch (b) 6 columns removed (c) 9 columns removed (d) 12 columns removed (e) 15 columns removed
The above images show a progression from the original Star texture to the same texture with 15 columns removed by texture carving. These photographs were shown to crowd-workers who rated their similarity. Even with a whole repetition width removed from the Stars, the pattern remains a recognizable star pattern.