Stefanie Mueller

Recent developments in 3D generative AI enable users to create bespoke 3D models from text or image prompts. However, language is often insufficient to express spatial design intent, and customization of generative output remains limited to surface appearance, making it difficult to personalize designs to the geometry and constraints of a user’s environment. Bridging this gap typically requires returning to conventional CAD tools to measure, edit, and adapt geometry, tasks that remain time-consuming and error-prone.

We propose a method to fabricate objects composed of 3D printed flattened pieces with integrated zipper-like structures. The object is manually assembled into a 3D shape by connecting the zipper components. By employing a zipper design that allows for angle-independent connections between patches, our method enables both the surface and zipper components to be printed in the same orientation, resulting in high-quality reconstruction of the input model with a faster 3D printing process that wastes less material. We implement a fully automated pipeline that takes a 3D model as input, converts it into developable patches, generates the zipper structures, and flattens them for subsequent 3D printing. We demonstrate that our approach significantly reduces the fabrication time and support material consumption. We also present application examples that highlight the versatility of our method.

We present Xspine, a design and fabrication method for creating motion-capable, self-sensing structures using multi-material FDM 3D printing with conductive filaments. Our method embeds compliant mechanisms and circuits directly into geometries, enabling the detection of large deformations in a single, assembly-free print. Specifically, we design printable components and circuit layouts aligned with the layer-by-layer nature of FDM 3D printing. Furthermore, we explore physical and digital augmentation strategies to enhance the interactive potential of the structures. To simplify the workflow, we develop an interactive design tool that allows users to configure motion behaviors, preview structural responses, and generate printable circuits. Finally, we demonstrate several application examples that highlight the potential of Xspine for customizable and interactive 3D-printed devices.

We present VisiPrint, a tool for appearance-first previews of 3D-printed objects. Existing print preview slicers focus on toolpaths, not appearance, while pure rendering software is complex and cannot automatically reproduce slicing patterns. Prior work highlights persistent gaps between digital previews and printed results, such as color shifts, gloss/translucency changes, and layer-line highlights, motivating the creation of VisiPrint, an appearance-focused support tool. The VisiPrint algorithm combines slicer screenshots with filament photos via a custom diffusion-based synthesis pipeline. We present both a standalone user interface for VisiPrint compatible with any slicer and an Ultimaker Cura Plugin. We evaluate VisiPrint through a user study showing it is significantly faster, easier to use, and more faithful than alternatives: within a time-limit, participants completed 100% of preview tasks with VisiPrint, versus 63% with Cura and 13% with Blender. VisiPrint narrows the gap between design intent and printed appearance, complementing settings-centric tools with appearance-driven decision support.

We present Y-zipper, a novel three-sided 3D-printed zipper structure that enables three flexible strips to interlock and transform into a rigid rod-like form. Building on this flex–rigid transition mechanism, we further design a specialized slider to achieve rapid and reversible zipping interactions. This slider serves as the basis for three actuation methods—manual, dynamic mechanical, and static mechanical—which enable both remote control and automated closure and release. In addition, Y-zipper provides four motion primitives: straight, bend, coil, and screw, whose combinations extend the flex–rigid transition mechanism to spatial curve structures. To support customization, we develop a computational design tool that automatically generates zipper geometry based on input primitives, unfolds the structure for 3D printing, and embeds both teeth and compliant bridges. Controlled experiments evaluate its mechanical properties, repeatability, and actuation speed, demonstrating robustness and reliability. Finally, we showcase a series of functional prototypes, including a medical wrist brace, a kinetic art installation, and a rapidly deployable tent structure.

The growing adoption of 3D printing presents significant environmental challenges, including material waste, high energy consumption, and the use of non-biodegradable polymers. To address this, we introduce a prototype tool for more sustainable fabrication. Our system features a user interface that reports key fabrication metrics, including material consumption and allows users to balance these against structural requirements such as mechanical strength. This is enabled by a multi-objective optimization framework driven by a differentiable simulator, which supports interactive refinement of designs toward improved resource efficiency. We evaluate the prototype in a simulation benchmark, demonstrating that it surfaces meaningful material–strength trade-offs across a range of functional shapes.

Academic writing still predominantly relies on text and static images, which limits the effective communication of dynamic and process-oriented research. We present !nteractivesPub, an accessible authoring and publishing workflow that enables authors to embed multimedia assets and interactive components directly into academic manuscripts, while remaining compatible with existing publication formats. The system addresses the high effort required to integrate interactivity, the fragmentation of interactive artifacts across external platforms, and their misalignment with established scholarly workflows. Through a field deployment and two user studies, we found that reading interactive content is associated with higher perceived comprehension and broader content exploration, while authoring interactive content is associated with lower reported expressive effort. This work frames interactivity as an expressive resource in scholarly communication, suggesting its potential to redistribute expressive and interpretive effort across authors and readers and to support the institutional adoption of interactive academic publishing.

Color-changing materials, such as photochromic pigments, allow objects to have reprogrammable multicolor surface images. Existing systems that reprogram these images are based on projectors and LEDs, each with advantages and limitations in device portability and image resolution. In this paper, we present ChromoLCD, a surface reprogrammer that uses a liquid crystal display (LCD) to achieve a compact handheld device without sacrificing image resolution. ChromoLCD consists of an LCD panel with a custom backlight containing R,G,B and UV LEDs, forming high-resolution light patterns with the required wavelengths. The compact form factor of ChromoLCD enables on-the-fly reprogramming of everyday surfaces. Our technical evaluation shows that ChromoLCD achieves a resolution of 25 ppi, which is 8 times better than the prior work. We demonstrate ChromoLCD with three applications, including the stamping of reprogrammable AR markers on a kitchen counter, on-the-fly designs on personal accessories, and reference pictures on a whiteboard.

We present a computational approach for designing freeform structures that can be rapidly assembled from initially flat configurations by a single string pull. The target structures are decomposed into rigid spatially varied quad tiles that are optimized to approximate the user-provided surface, forming a flat mechanical linkage. Our algorithm then uses a two-step method to find a physically realizable string path that controls only a subset of tiles to smoothly actuate the structure from flat to assembled configuration. We initially compute the minimal subset of tiles that are required to be controlled with the string considering the geometry of the structure and interaction among the tiles. We then find a valid string path through these tiles that minimizes friction, which will assemble the flat linkage into the target 3D structure upon tightening a single string. The resulting designs can be easily manufactured with computational fabrication techniques such as 3D printing, CNC milling, molding, etc. in flat configuration that, in addition to manufacturing, facilitates storage and transportation. We validate our approach by developing a series of physical prototypes and showcasing various application case studies, ranging from medical devices, space shelters, to architectural designs.

Recent developments in Generative AI enable creators to stylize 3D models based on text prompts. These methods change the 3D model geometry, which can compromise the model’s structural integrity once fabricated. We present MechStyle, a system that enables creators to stylize 3D printable models while preserving their structural integrity. MechStyle accomplishes this by augmenting the Generative AI-based stylization process with feedback from a Finite Element Analysis (FEA) simulation. As the stylization process modifies the geometry to approximate the desired style, feedback from the FEA simulation reduces modifications to regions with increased stress. We evaluate the effectiveness of FEA simulation feedback in the augmented stylization process by comparing three stylization control strategies. We also investigate the time efficiency of our approach by comparing three adaptive scheduling strategies. Finally, we demonstrate MechStyle’s user interface that allows users to generate stylized and structurally viable 3D models and provide five example applications.

Maker Education and STEM+ initiatives are often celebrated for fostering creativity, problem-solving, and innovation. However, as these approaches gain global traction and the Maker Education movement matures, its technocentric underpinnings are increasingly critiqued for overshadowing deeper pedagogical aims. This paper examines initial insights from qualitative interviews with Maker educators and researchers, highlighting challenges and strategies for shifting Maker Education from a focus on tools and spaces towards pedagogies that prioritise the significance and centrality of community for learning, as well as a mindset of resistance to failure and a resilient approach to problem-solving. By re-centering constructionist ideas of creativity, iteration, and human connection, the findings demonstrate how community-centric practices can transform makerspaces into inclusive ecosystems of collaboration and growth. This work contributes actionable recommendations for embedding Maker Education into diverse educational settings, in the context of contemporary constructionism and in line with the conference’s theme of “Building Communities, Bridging Ideas.”

CHI ’25, April 26–May 01, 2025, Yokohama, Japan

CHI ’25, Yokohama, Japan

CHI ’25, Yokohama, Japan

Recent work in Generative AI enables the stylization of 3D models based on image prompts. However, these methods do not incorporate tactile information, leading to designs that lack the expected tactile properties. We present TactStyle, a system that allows creators to stylize 3D models with images while incorporating the expected tactile properties. TactStyle accomplishes this using a modified image-generation model fine-tuned to generate heightfields for given surface textures. By optimizing 3D model surfaces to embody a generated texture, TactStyle creates models that match the desired style and replicate the tactile experience. We present TactStyle’s user interface to create stylized 3D models with accurate textures, present multiple textured tiles for blind perception experience, and present five application scenarios with fabricated 3D models.

In this Demo, we present Xstrings, a method for designing and fabricating 3D printed objects with integrated cable-driven mechanisms that can be printed in one go without the need for manual assembly. Xstrings supports four types of cable-driven interactions—bend, coil, screw and compress—which are activated by applying an input force to the cables. To facilitate the design of Xstrings objects, we developed a design tool that allows users to embed cable-driven mechanisms into the object geometry based on the desired interaction by automatically placing joints and cables at the respective locations. The application potential of Xstrings is demonstrated through examples such as manipulable gripping, bionic robot manufacturing, and dynamic prototyping.

We demonstrate Thermochromorph, a novel relief printing technique that produces multicolored images that transition into each other through changes in temperature. Our process utilizes two sets of CMYK thermochromic inks that exhibit complementary color-changing behaviors: one shifting from color to transparency, the other from transparency to color at the same activation temperature. We describe our printmaking workflow, provide an open-source software toolkit, showcase prints made with our system, and explore how our system can be used in creative practice through an artist workshop. By incorporating new materials and technology with the rich history of printmaking, our work extends the expressive capabilities of relief printing as the medium continues to evolve.

SA Art Papers ’24, December 03–06, 2024, Tokyo, Japan

The commonly used cut-and-sew garment construction process, in which 2D fabric panels are cut from sheets of fabric and assembled into 3D garments, contributes to widespread textile waste in the fashion industry. There is often a significant divide between the design of the garment and the layout of the panels. One opportunity for bridging this gap is the emerging study and practice of zero waste fashion design, which involves creating clothing designs with maximum layout efficiency. Enforcing the strict constraints of zero waste sewing is challenging, as edits to one region of the garment necessarily affect neighboring panels. Based on our formative work to understand this emerging area within fashion design, we present WasteBanned, a tool that combines CAM and CAD to help users prioritize efficient material usage, work within these zero waste constraints, and edit existing zero waste garment patterns. Our user evaluation indicates that our tool helps fashion designers edit zero waste patterns to fit different bodies and add stylistic variation, while creating highly efficient fabric layouts.

In this paper, we present PortaChrome, a portable light source that can be attached to everyday objects to reprogram the color and texture of surfaces that come in contact with them. When PortaChrome makes contact with objects previously coated with photochromic dye, the UV and RGB LEDs inside PortaChrome create multi-color textures on the objects. In contrast to prior work, which used projectors for the color-change, PortaChrome has a thin and flexible form factor, which allows the color-change process to be integrated into everyday user interaction. Because of the close distance between the light source and the photochromic object, PortaChrome creates color textures in less than 4 minutes on average, which is 8 times faster than prior work. We demonstrate PortaChrome with four application examples, including data visualizations on textiles and dynamic designs on wearables.

We present Speed-Modulated Ironing, a new fabrication method for programming visual and tactile properties in single-material 3D printing. We use one nozzle to 3D print and a second nozzle to reheat printed areas at varying speeds, controlling the material’s temperature-response. The rapid adjustments of speed allow for fine-grained reheating, enabling high-resolution color and texture variations. We implemented our method in a tool that allows users to assign desired properties to 3D models and creates corresponding 3D printing instructions. We demonstrate our method with three temperature-responsive materials: a foaming filament, a filament with wood fibers, and a filament with cork particles. These filaments respond to temperature by changing color, roughness, transparency, and gloss. Our technical evaluation reveals the capabilities of our method in achieving sufficient resolution and color shade range that allows surface details such as small text, photos, and QR codes on 3D-printed objects. Finally, we provide application examples demonstrating the new design capabilities enabled by Speed-Modulated Ironing.

Wearables have long been integral to human culture and daily life. Recent advances in intelligent soft wearables have dramatically transformed how we interact with the world, enhancing our health, productivity, and overall well-being. These innovations, combining advanced sensor design, fabrication, and computational power, offer unprecedented opportunities for monitoring, assistance, and augmentation. However, the benefits of these advancements are not yet universally accessible. Economic and technical barriers often limit the reach of these technologies to domain-specific experts. There is a growing need for democratizing intelligent wearables that are scalable, seamlessly integrated, customized, and adaptive. By bringing researchers from relevant disciplines together, this workshop aims to identify the challenges and investigate opportunities for democratizing intelligent soft wearables within the HCI community via interactive demos, invited keynotes, and focused panel discussions.

This paper introduces MouthIO, the first customizable intraoral user interface that can be equipped with various sensors and output components. MouthIO consists of an SLA-printed brace that houses a flexible PCB within a bite-proof enclosure positioned between the molar teeth and inner cheeks. Our MouthIO design and fabrication technique enables makers to customize the oral user interfaces in both form and function at low cost. All parts in contact with the oral cavity are made of bio-compatible materials to ensure safety, while the design takes into account both comfort and portability. We demonstrate MouthIO through three application examples ranging from beverage consumption monitoring, health monitoring, to assistive technology. Results from our full-day user study indicate high wearability and social acceptance levels, while our technical evaluation demonstrates the device’s ability to withstand adult bite forces.

MouthIO is the first customizable intraoral user interface that can be equipped with various sensors and output components. It consists of an SLA-printed brace that houses a flexible PCB within a bite-proof enclosure positioned between the molar teeth and inner cheeks. All parts in contact with the oral cavity are made of bio-compatible materials to ensure safety, while the design takes into account both comfort and portability. We demonstrate MouthIO through three application examples ranging from beverage consumption monitoring, health monitoring, to assistive technology.

In this demo, we present PortaChrome, a portable light source that can be attached to everyday objects to reprogram the color of surfaces in contact with them. When PortaChrome makes contact with objects that were previously coated with photochromic dyes, the UV and RGBs LEDs inside PortaChrome create multi-color textures on the objects. In contrast to prior work, which used projectors for the color-change, PortaChrome has a thin and flexible form factor, which allows the color-change process to be integrated into daily user interactions. Because of the close distance between the light source and the photochromic object, PortaChrome creates color textures in less than 4 minutes on average, which is 8 times faster than prior work. We demonstrate PortaChrome with four application examples, including data visualizations on textiles and personalized wearables.

We present Speed-Modulated Ironing, a new fabrication method for programming visual and tactile properties in single-material 3D printing. We use one nozzle to 3D print and a second nozzle to reheat printed areas at varying speeds, controlling the material’s temperature-response. The rapid adjustments of speed allow for fine-grained reheating, enabling high-resolution color and texture variations. We implemented our method in a tool that allows users to assign desired properties to 3D models and creates corresponding 3D printing instructions. We demonstrate our method with three temperature-responsive materials: a foaming filament, a filament with wood fibers, and a filament with cork particles. These filaments respond to temperature by changing color, roughness, transparency, and gloss. Our method is able to achieve sufficient resolution and color shade range that allows surface details such as small text, photos, and QR codes on 3D-printed objects. Finally, we provide application examples demonstrating the new design capabilities enabled by Speed-Modulated Ironing.

Self-reflection during maker activities is known to enhance conceptual comprehension and lead to better skill learning. While educational makerspaces commonly leverage reflective exercises guided by instructors, this practice often goes amiss in scenarios when makers interested in self-directed learning use online tutorials like Instructables. In this short paper, we explore the approach of using Large Language Models (LLMs), specifically OpenAI's GPT-4 for generating reflection prompts with the existing Instructable tutorials and aligning them with a list of learning goals adapted from prior work on maker skills learning. We built a system to generate Reflectables: Instructables designed for self-reflection while making. To exemplify the approach, we generated 9 Reflectables consisting of 128 prompts and evaluated them on seven attributes: goal-oriented, timely, contextual, sequential, multilevel, clear, and personalized. Our analysis highlights the benefits and limitations of this approach and points to further research directions for designing AI-based systems for reflection-focused self-directed learning in makerspaces.

MouthIO is the first customizable intraoral user interface that can be equipped with various sensors and output components. It consists of an SLA-printed brace that houses a flexible PCB within a bite-proof enclosure positioned between the molar teeth and inner cheeks. All parts in contact with the oral cavity are made of bio-compatible materials to ensure safety, while the design takes into account both comfort and portability. We demonstrate MouthIO through three application examples ranging from beverage consumption monitoring, health monitoring, to assistive technology.

Despite recent exponential advancements in computer vision and reinforcement learning, it remains challenging for robots to interact with liquids. These challenges are particularly pronounced due to the limitations imposed by opaque containers, transparent liquids, fine-grained splashes, and visual obstructions arising from the robot’s own manipulation activities. Yet, there exists a substantial opportunity for robotics to excel in liquid identification and manipulation, given its potential role in chemical handling in laboratories and various manufacturing sectors such as pharmaceuticals or beverages. In this work, we present a novel approach for liquid class identification and state estimation leveraging electrical impedance sensing. We design and mount a digitally embroidered electrode array to a commercial robot gripper. Coupled with a customized impedance sensing board, we collect data on liquid manipulation with a swept frequency sensing mode and a frequency-specific impedance measuring mode. Our developed learning-based model achieves an accuracy of 93.33% in classifying 9 different types of liquids (8 liquids + air), and 97.65% in estimating the liquid state. We investigate the effectiveness of our system with a series of ablation studies. These findings highlight our work as a promising solution for enhancing robotic manipulation in liquid-related tasks.

Thermal interfaces in HCI have been widely explored. Previous work either used electronics that continuously consume energy to actively control the temperatures, or relied on materials that passively create only one predefined temperature behavior, which both leave the gap of exploring objects that allow self-contained programmable temperature behaviors, utilizing materials’ dynamic thermal properties.

Advances in Generative AI tools have allowed designers to manipulate existing 3D models using text or image-based prompts, enabling creators to explore different design goals. Photochromic color-changing systems, on the other hand, allow for the reprogramming of surface texture of 3D models, enabling easy customization of physical objects and opening up the possibility of using object surfaces for data display. However, existing photochromic systems require the user to manually design the desired texture, inspect the simulation of the pattern on the object, and verify the efficacy of the generated pattern. These manual design, inspection, and verification steps prevent the user from efficiently exploring the design space of possible patterns. Thus, by designing an automated workflow desired for an end-to-end texture application process, we can allow rapid iteration on different practicable patterns. In this workshop paper, we discuss the possibilities of extending generative AI systems, with material and design constraints for reprogrammable surfaces with photochromic materials. By constraining generative AI systems to colors and materials possible to be physically realized with photochromic dyes, we can create tools that would allow users to explore different viable patterns, with text and image-based prompts. We identify two focus areas in this topic: photochromic material constraints and design constraints for data-encoded textures. We highlight the current limitations of using generative AI tools to create viable textures using photochromic material. Finally, we present possible approaches to augment generative AI methods to take into account the photochromic material constraints, allowing for the creation of viable photochromic textures rapidly and easily.

Generative AI tools are becoming more prevalent in 3D modeling, enabling users to manipulate or create new models with text or images as inputs. This makes it easier for users to rapidly customize and iterate on their 3D designs and explore new creative ideas. These methods focus on the aesthetic quality of the 3D models, refining them to look similar to the prompts provided by the user. However, when creating 3D models intended for fabrication, designers need to trade-off the aesthetic qualities of a 3D model with their intended physical properties. To be functional post-fabrication, 3D models have to satisfy structural constraints informed by physical principles. Currently, such requirements are not enforced by generative AI tools. This leads to the development of aesthetically appealing, but potentially non-functional 3D geometry, that would be hard to fabricate and use in the real world. This workshop paper highlights the limitations of generative AI tools in translating digital creations into the physical world and proposes new augmentations to generative AI tools for creating physically viable 3D models. We advocate for the development of tools that manipulate or generate 3D models by considering not only the aesthetic appearance but also using physical properties as constraints. This exploration seeks to bridge the gap between digital creativity and real-world applicability, extending the creative potential of generative AI into the tangible domain.

We present FabRobotics, a digital fabrication pipeline that combines traditional 3D printing with mobile robots. By integrating these two technologies, we aim to create new opportunities for 3D printers to fabricate objects quickly and efficiently, and for mobile robots to enhance their adaptability and interactivity. To explore this novel research opportunity, we have developed a proof-of-concept implementation pipeline, allowing users to execute hybrid turn-taking control of a 3D printer and mobile robots to autonomously 3D print objects on/with mobile robots. The system was implemented with commercially available 3D printers (Prusa MINI) and mobile robots (toio), and we share various techniques and knowledge specific to fusing 3D printers and mobile robots (e.g. printing mobile robot docks for stable prints on robots). Based on the proof-of-concept system, we demonstrate various application usages and functionalities, showcasing how 3D printing and mobile robots can mutually advance each other for novel fabrication and interaction. Lastly, we share our further exploration of extended prototypes (e.g. fusing two printers) and discuss future technical challenges and research opportunities.

Abstract Makerspaces persist as formal and informal spaces of learning for youth, promoting continued interest in studying how design can support the variety of learning opportunities within these spaces. However, much of the current research examining learning in makerspaces neglects the perspectives of educators. This not only hinders our understanding of educators’ goals and how educators navigate makerspaces but also constrains how we frame the design space of the learning experiences and environments. To address this, we engaged in a set of semi-structured interviews to examine the contexts, goals, values, and practices of seven educators across five makerspaces. A thematic analysis of the data identified six key categories of competencies that these educators prioritize including a range of skills, practices, and knowledge, such as technical proficiency, communication, and contextual reflection. The analysis also identified five categories of strategies to accomplish certain goals, such as scaffolding, collaboration, and relationship building. Last, it also shed light on three categories of challenges faced at the student-level, teacher-level, and institutional level. We conclude with a discussion on our insights into how we can broaden the problem space in the design of educational technologies to support learning in makerspaces.

In this demo, we present ChromoCloth, a flexible and portable light source for reprogrammable multi-color texture on photochromic objects, whose color can be reprogrammed with external light sources. While prior work used external projectors to trigger the color change, ChromoCloth initiates the color change by covering the object. ChromoCloth consists of a textile substrate, 3D printed diffusive housing glued on top of the substrate and a flexible LED strip that is weaved through the housings.

In this demonstration, we showcase BrightMarker, a fabrication method that uses fluorescent filaments to embed easily trackable markers in 3D printed color objects. By employing an infrared-fluorescent filament that emits light at a wavelength higher than the incident light, our optical detection setup filters out all the noise to only have the markers present in the infrared camera image. The high contrast of the markers allows us to robustly track them when objects are in motion.

With recent advances in Generative AI, it is becoming easier to automatically manipulate 3D models. However, current methods tend to apply edits to models globally, which risks compromising the intended functionality of the 3D model when fabricated in the physical world. For example, modifying functional segments in 3D models, such as the base of a vase, could break the original functionality of the model, thus causing the vase to fall over. We introduce Style2Fab, a system for automatically segmenting 3D models into functional and aesthetic elements, and selectively modifying the aesthetic segments, without affecting the functional segments. Style2Fab uses a semi-automatic classification method to decompose 3D models into functional and aesthetic elements, and differentiable rendering to selectively stylize the functional segments. We demonstrate the functionality of this tool with six application examples across domains of Home Interior Design, Medical Applications, and Personal Accessories.

With recent advances in Generative AI, it is becoming easier to automatically manipulate 3D models. However, current methods tend to apply edits to models globally, which risks compromising the intended functionality of the 3D model when fabricated in the physical world. For example, modifying functional segments in 3D models, such as the base of a vase, could break the original functionality of the model, thus causing the vase to fall over. We introduce a method for automatically segmenting 3D models into functional and aesthetic elements. This method allows users to selectively modify aesthetic segments of 3D models, without affecting the functional segments. To develop this method we first create a taxonomy of functionality in 3D models by qualitatively analyzing 1000 models sourced from a popular 3D printing repository, Thingiverse. With this taxonomy, we develop a semi-automatic classification method to decompose 3D models into functional and aesthetic elements. We propose a system called Style2Fab that allows users to selectively stylize 3D models without compromising their functionality. We evaluate the effectiveness of our classification method compared to human-annotated data, and demonstrate the utility of Style2Fab with a user study to show that functionality-aware segmentation helps preserve model functionality.

In this paper, we introduce MagKnitic, a novel approach to integrate passive force feedback and binary sensing into fabrics via digital machine knitting. Our approach utilizes digital fabrication technology to enable haptic interfaces that are soft, flexible, lightweight, and conform to the user’s body shape. Despite these characteristics, our interfaces provide diverse, interactive, and responsive force feedback, expanding the design space for haptic experiences.MagKnitic provides scalable and customizable passive haptic sensations by utilizing the attractive force between ferromagnetic yarns and permanent magnets, both of which are seamlessly integrated into knitted fabrics. Moreover, we present a binary sensing capability based on the resistance drop resulting from the activated electrical path between the integrated magnets and ferromagnetic yarn upon direct contact. We offer parametric design templates for users to customize MagKnitic layouts and patterns. With various design layouts and combinations, MagKnitic supports passive haptics interactions of linear, polar, angular, planar, radial, and user-defined motions. We perform a technical evaluation of the passive force feedback and the binary sensing capabilities with different machine knitting layouts and patterns, embedded magnet sizes, and interaction distances. In addition, we conduct two user studies to validate the effectiveness of MagKnitic. Finally, we demonstrate various application scenarios, including wearable input interfaces, game controllers, passive VR/AR wearables, and interactive furniture coverings.