Augmentation allows rapid reconfiguration of passive physical interfaces to improve accessibility, support independent living through domestic automation, and more. However, its potential is largely unrealized for novice users due to several key barriers. First, users rarely identify latent interaction problems within their built environments. Second, they often lack the knowledge to clearly express design intent. Third, many innovative solutions remain in research prototypes, limiting access.

Instance detection (InsDet) aims to localize specific object instances within a novel scene imagery based on given visual references. Technically, it requires proposal detection to identify all possible object instances, followed by instance-level matching to pinpoint the ones of interest. Its open-world nature supports its broad applications from robotics to AR/VR but also presents significant challenges: methods must generalize to unknown testing data distributions because (1) the testing scene imagery is unseen during training, and (2) there are domain gaps between visual references and detected proposals. Existing methods tackle these challenges by synthesizing diverse training examples or utilizing off-the-shelf foundation models (FMs). However, they only partially capitalize the available open-world information. In contrast, we approach InsDet from an Open-World perspective, introducing our method IDOW. We find that, while pretrained FMs yield high recall in instance detection, they are not specifically optimized for instance-level feature matching. Therefore, we adapt pretrained FMs for improved instance-level matching using open-world data. Our approach incorporates metric learning along with novel data augmentations, which sample distractors as negative examples and synthesize novel-view instances to enrich the visual references. Extensive experiments demonstrate that our method significantly outperforms prior works, achieving >10 AP over previous results on two recently released challenging benchmark datasets in both conventional and novel instance detection settings.

Sign language serves individuals with hearing impairments as a crucial communication mode operating through visual-manual means. While there has been established theory and agreement about embodiment in multiple fields, only limited research has deeply engaged to lower access to the physical body for spatial perception and engagement. Embodied robots are often cost-prohibitive, and existing open-source robot fabrication packages are limited in their ability to fully address communication nuances, typically running only on predefined programs. Reprogramming for broader bodily interactions, such as gestures in various domains (e.g., construction), is nearly impossible unless expertise precedes. We introduce FABRIC, an end-to-end toolkit for fabricating and programming bodily language for unique human-robot interactions. The toolkit includes a fully 3D-printable robot, designed for consumer-grade FDM machinery, that learns from demonstration (LfD) to capture and translate users’ bodily expressions through its upper torso (arms and hands) movements. A visual programming interface enables appending or sequencing demonstrations from various sources, i.e., videos, cameras, and expandable word/phrase/sentence libraries.

Reconfigurable physical interfaces empower users to swiftly adapt to tailored design requirements or preferences. Shape-changing interfaces enable such reconfigurability, avoiding the cost of refabrication or part replacements. Nonetheless, reconfigurable interfaces are often bulky, expensive, or inaccessible. We propose a reversible shape-changing mechanism that enables reconfigurable 3D printed structures via translations and rotations of parts. We investigate fabrication techniques that enable reconfiguration using magnets and the thermoplasticity of heated polymer. Proposed interfaces achieve tunable haptic feedback and adjustment of different user affordances by reconfiguring input motions. The design space is demonstrated through applications in rehabilitation, embodied communication, accessibility, safety, and gaming.

In our increasingly diverse society, everyday physical interfaces often present barriers, impacting individuals across various contexts. This oversight, from small cabinet knobs to identical wall switches that can pose different contextual challenges, highlights an imperative need for solutions. Leveraging low-cost 3D-printed augmentations such as knob magnifiers and tactile labels seems promising, yet the process of discovering unrecognized barriers remains challenging because disability is context-dependent. We introduce AccessLens, an end-to-end system designed to identify inaccessible interfaces in daily objects, and recommend 3D-printable augmentations for accessibility enhancement. Our approach involves training a detector using the novel AccessDB dataset designed to automatically recognize 21 distinct Inaccessibility Classes (e.g., bar-small and round-rotate) within 6 common object categories (e.g., handle and knob). AccessMeta serves as a robust way to build a comprehensive dictionary linking these accessibility classes to open-source 3D augmentation designs. Experiments demonstrate our detector’s performance in detecting inaccessible objects.

The widespread consumer-grade 3D printers and learning resources online enable novices to self-train in remote settings. While troubleshooting plays an essential part of 3D printing, the process remains challenging for many remote novices even with the help of well-developed online sources, such as online troubleshooting archives and online community help. We conducted a formative study with 76 active 3D printing users to learn how remote novices leverage online resources in troubleshooting and their challenges. We found that remote novices cannot fully utilize online resources. For example, the online archives statically provide general information, making it hard to search and relate their unique cases with existing descriptions. Online communities can potentially ease their struggles by providing more targeted suggestions, but a helper who can provide custom help is rather scarce, making it hard to obtain timely assistance. We propose 3DPFIX, an interactive 3D troubleshooting system powered by the pipeline to facilitate Human-AI Collaboration, designed to improve novices' 3D printing experiences and thus help them easily accumulate their domain knowledge. We built 3DPFIX that supports automated diagnosis and solution-seeking. 3DPFIX was built upon shared dialogues about failure cases from Q&A discourses accumulated in online communities. We leverage social annotations (i.e., comments) to build an annotated failure image dataset for AI classifiers and extract a solution pool. Our summative study revealed that using 3DPFIX helped participants spend significantly less effort in diagnosing failures and finding a more accurate solution than relying on their common practice. We also found that 3DPFIX users learn about 3D printing domain-specific knowledge. We discuss the implications of leveraging community-driven data in developing future Human-AI Collaboration designs.

Within the domain of fabrication, the recent strides in Fused Deposition Modeling (FDM) have sparked growing interest in its sustainability. In this work, we analyze the contemporary lifecycle of polymers consumed in FDM, a common and accessible fabrication technique. Then we outline the points of design intervention to reduce wasted polymers in fabrication. Specifically, we discuss the design intervention of Filament Wiring , a set of hybrid craft techniques to promote sustainable prototyping and robust applications by highlighting left-over filaments. Our techniques aim to enhance the understanding of filaments as a unique material for hybrid fabrication, fostering creativity. Through our computational design system, end users can generate 3D printable frames, for exploring the possibilities of filament-based fabrication beyond 3D printing. We hope to provoke thought about filament as its own form of material, having capabilities to be made, unmade, and remade repeatedly into various artifacts. With this outlook, we discuss future research avenues and urge makers and practitioners to value material in any form, quantity, or stage of its lifecycle.

The majority of errors in making processes can be tracked back to errors in dimensional specifications. While technical aspects of measurement, such as precision and speed have been extensively studied in metrology, the user aspects of measurement received significantly less attention. While little research exists that specifically addresses the user aspects of handling dimensions, various systems have been built that embed new interactive modalities, processes, and techniques which significantly impact how users deal with dimensions or conduct measurements. However, these features are mostly hidden in larger system contributions. To uncover and articulate these techniques, we conducted a holistic literature survey on measurement practices in crafting techniques and systems for rapid prototyping. Based on this survey, we contribute 10 measurement patterns, which describe reusable elements and solutions for common difficulties when dealing with dimensions throughout workflows for making physical artifacts.

Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene. Despite its practical significance, its advancement is overshadowed by Object Detection, which aims to detect objects belonging to some predefined classes. One major reason is that current InsDet datasets are too small in scale by today's standards. For example, the popular InsDet dataset GMU (published in 2016) has only 23 instances, far less than COCO (80 classes), a well-known object detection dataset published in 2014. We are motivated to introduce a new InsDet dataset and protocol. First, we define a realistic setup for InsDet: training data consists of multi-view instance captures, along with diverse scene images allowing synthesizing training images by pasting instance images on them with free box annotations. Second, we release a real-world database, which contains multi-view capture of 100 object instances, and high-resolution (6k$\times$8k) testing images. Third, we extensively study baseline methods for InsDet on our dataset, analyze their performance and suggest future work. Somewhat surprisingly, using the off-the-shelf class-agnostic segmentation model (Segment Anything Model, SAM) and the self-supervised feature representation DINOv2 performs the best, achieving $>$10 AP better than end-to-end trained InsDet models that repurpose object detectors (e.g., FasterRCNN and RetinaNet).

Online 3D model repositories such as Thingiverse offer millions of open source designs that are shared for reuse and remix. Many of the designs are customizable to adapt to real-world objects upon personal needs of varying tasks and physical dimensions. However, it is challenging for novices to discover such designs using text-based search queries, comprehend what each parameter means for customization, locate these parameters on the target objects for measurement, and conduct measurements correctly. These challenges may cause the designs to be incorrectly adjusted, thus failing to function as expected and requiring users to start over, which costs additional time and material. We present CustomizAR, a pipeline for facilitating the interactive exploration of adaptive designs and the measurement of real-world constraints to fabricate them correctly. CustomizAR supports the search and discovery of adaptive 3D designs using an object-centric graph-based data structure, and guides users through an interactive measurement process leveraging computer vision techniques. Our technical evaluations and user studies demonstrate that CustomizAR facilitates effective discovery, adjustment, and reuse of adaptive designs that are shared online.

Recent advancements in personal fabrication have brought novices closer to a reality, where they can automate routine tasks with mobilized everyday objects. However, the overall process remains challenging- from capturing design requirements and motion planning to authoring them to creating 3D models of mechanical parts to programming electronics, as it demands expertise.

One important vision of robotics is to provide physical assistance by manipulating different everyday objects, e.g., hand tools, kitchen utensils. However, many objects designed for dexterous hand-control are not easily manipulable by a single robotic arm with a generic parallel gripper. Complementary to existing research on developing grippers and control algorithms, we present Roman, a suite of hardware design and software tool support for robotic engineers to create 3D printable mechanisms attached to everyday handheld objects, making them easier to be manipulated by conventional robotic arms. The Roman hardware comes with a versatile magnetic gripper that can snap on/off handheld objects and drive add-on mechanisms to perform tasks. Roman also provides software support to register and author control programs. To validate our approach, we designed and fabricated Roman mechanisms for 14 everyday objects/tasks presented within a design space and conducted expert interviews with robotic engineers indicating that Roman serves as a practical alternative for enabling robotic manipulation of everyday objects.

4D printing is a new technique for human-computer interaction to create self bending and actuating interfaces, leveraging the properties of 3D printed materials for shape change over time, triggered by external factors such as heat. Given the ubiquity of low-cost 3D printers, we see an opportunity to translate 4D printing from the research lab into makerspaces and educational settings. In this work, we explore low cost 4D printing with shape memory polymers and desktop 3D printers, tackling hybrid fabrication approach to lower the barrier for non-experts. We see heat as one promising way of creating shape changing behaviors using common triggering methods such as oven, hair dryer, hot water. We present the initial design space for hybrid fabrication with factors that help characterize and control bending behaviors, showcasing potential design contexts with 4D printed applications.

The unique behaviors of thermoplastic polymers enable shape-changing interfaces made of 3D printed objects that do not require complex electronics integration. While existing techniques rely on external trigger, such as heat, applied globally on a 3D printed object initiating all at once the shape-changing response (e.g., hot water, heat gun, oven), independent control of multiple parts of the object becomes nearly impossible. We introduce ShrinkCells, a set of shape-changing actuators that enables localized heat to shrink or bend, through combining the properties of two materials — conductive PLA is used to generate localized heat which selectively triggers the shrinking of a Shape Memory Polymer. The unique benefit of ShrinkCells is their capability of triggering simultaneous or sequential shape transformations for different geometries using a single power supply. This results in 3D printed rigid structures that actuate in sequence, avoiding self-collisions when unfolding. We contribute to the body of literature on 4D fabrication by a systematic investigation of selective heating with two different materials, the design and evaluation of the ShrinkCells shape-changing primitives, and applications demonstrating the usage of these actuators.

Hand-weaving is a beloved craft in history, holding promise for many opportunities in making from flat sheet fabrics to smart textiles. To afford new weaving experiences, we explore how 3D printed custom weaving tools interplay with different materiality, augmenting the design space of weaving. We propose novel weaving techniques enabled by 3D printed custom tools: (1) water-soluble draft to synchronize design intention and practice, (2) flexible warps to guide complex patterns and to shape resulting object, and (3) rigid global geometry for woven artifacts in 3D. EscapeLoom as a computational design tool enables users to employ various parameters in their computational design, and showcases many creative possibilities that move away from the traditional definition of a loom to dive into what more it can be.

The promise of anyone being able to 3D print anywhere relies on both technological advances and incremental shifts in social organizations to trigger changes in human behavior. While much research has focused on how people learn aspects of predefined printing processes, such as expressively utilizing particular design-software (e.g. CAD) and fabrication-machinery (e.g. 3D Printers), this work explores how anyone may gain an understanding of what can be 3D printed through dynamic-processes in computationally-guided exploration of online resources and 3D printing facilities. Investigations surrounding online printing services reveal accessible 3D printing processes that do not require end-users to have experience with design-software or fabrication-machinery, only requiring end-users to specify printable ideas. We present these accessible printing processes alongside associated technologies in a meta-design framework for supporting end-users’ specification of 3D printing ideas. Informed by this framework and a series of formative studies, we designed the website HowDIY to introduce anyone to 3D printing by encouraging and facilitating the intelligent exploration of various online resources. HowDIY was deployed over several weeks with diverse newcomers to 3D printing, validating that intelligent user interfaces can support anyone to participate in the utilization and design of 3D printing tools and processes.

Softness is one of the most important factors in human tactile perception. With recent advances in 3Dprinting, there has been significant progress in fabricating compliant objects. However, existing methods typically remain inaccessible to end-users, mainly due to the separation between designing shapes and setting printing parameters to achieve desired softness, resulting in the exclusion of its customization in early design processes. In this work, we contribute an end-to-end design tool that takes a design-by-example approach: given a 3D model, a user can specify the region of interest and a level of softness, by shopping everyday objects as a reference. The tool then generates both geometry and 3D printing parameters to reproduce the desired softness, which can be fabricated using low-cost FDM 3D printing and materials for it. We also provide a data-driven pipeline to enable other compliance modeling methods to be generalized within our design tool. In two user studies, we demonstrated that users could easily locate existing reference objects’ softness to a 3D printed object. In a design session, end-users successfully used OmniSoft to design augmented functions.

Reconfiguring shapes of objects enables transforming existing passive objects with robotic functionalities, e.g., a transformable coffee cup holder can be attached to a chair's armrest, a piggy bank can reach out an arm to 'steal' coins. Despite the advance in end-user 3D design and fabrication, it remains challenging for non-experts to create such 'transformables' using existing tools due to the requirement of specific engineering knowledge such as mechanisms and robotic design.

From full-color objects to functional capacitive artifacts, 3D printing multi-materials became essential to broaden the application areas of digital fabrication. We present Programmable Filament, a novel technique that enables multi-material printing using a commodity FDM 3D printer, requiring no hardware upgrades. Our technique builds upon an existing printing technique in which multiple filament segments are printed and spliced into a single threaded filament. We propose an end-to-end pipeline for 3D printing an object in multi-materials, with an introduction of the design systems for end-users. Optimized for low-cost, single-nozzle FDM 3D printers, the system is built upon our computational analysis and experiments to enhance its validity over various printers and materials to design and produce a programmable filament. Finally, we discuss application examples and speculate the future with its potential, such as custom filament manufacturing on-demand.

The emergence of a 3D pen brings 3D modeling from a screen-based computer-aided design (CAD) system and 3D printing to direct and rapid crafting by 3D doodling. However, 3D doodling remains challenging, requiring craft skills to rapidly express an idea, which is critical in creative making. We explore a new process of 3D modeling using 3D pen + 3D printer. Our pilot study shows that users need support to reduce the number of non-creative tasks to explore a wide design strategy. With the opportunity to invent a new 3D modeling process that needs to incorporate both a pen and printer, we propose techniques and a system that empower users to print while doodling to focus on creative exploration. Our user study shows that users can create diverse 3D models using a pen and printer. We discuss the roles of the human and fabrication machine for the future of fabrication.

We present Mechamagnets, a technique for facilitating the design and fabrication of haptic and functional inputs for physical interfaces. This technique consists of a set of 3D printed spatial constraints which facilitate different physical movements, as well as unpowered haptic profiles created by embedding static magnets in 3D printed parts. We propose the Mechamagnets taxonomy to map the design space of this technique for designers and makers. Furthermore, we leverage the use of magnets by instrumenting these objects with linear Hall effect sensors to create functional digital inputs. We showcase Mechamagnets with a series of novel physical interfaces made with this technique.

Users can now easily communicate digital information with an Internet of Things; in contrast, there remains a lack of support to automate physical tasks that involve legacy static objects, e.g. adjusting a desk lamp's angle for optimal brightness, turning on/off a manual faucet when washing dishes, sliding a window to maintain a preferred indoor temperature. Automating these simple physical tasks has the potential to improve people's quality of life, which is particularly important for people with a disability or in situational impairment.

We present a technique for fabricating soft and flexible textiles using a consumer grade fused deposition modeling (FDM) 3D printer. By controlling the movement of the print header, the FDM alternately weaves the stringing fibers across a row of pillars. Owing to the structure of the fibers, which supports and strengthens the pillars from each side, a 3D printer can print a thin sheet of fabric in an upright position while the fibers are being woven. In addition, this technique enables users to employ materials with various colors and/or properties when designing a pattern, and to prototype an interactive object using a variety of off-the-shelf materials such as a conductive filament. We also describe a technique for weaving textiles and introduce a list of parameters that enable users to design their own textile variations. Finally, we demonstrate examples showing the feasibility of our approach as well as numerous applications integrating printed textiles in a custom object design.

We explore web programming as a new paradigm for programmatic 3D modeling. Most existing approaches subscribe to the imperative programming paradigm. While useful, there exists a gulf of evaluation between procedural steps and the intended structure. We present CraftML, a language providing a declarative syntax where the code is the structure. CraftML offers a rich set of programming features familiar to web developers of all skill levels, such as tags, hyperlinks, document object model, cascade style sheet, JQuery, string interpolation, template engine, data injection, and scalable vector graphics. We develop an online IDE to support CraftML development, with features such as live preview, search, module import, and parameterization. Using examples and case studies, we demonstrate that CraftML offers a low floor for beginners to make simple designs, a high ceiling for experts to build complex computational models, and wide walls to support many application domains such as education, data physicalization, tactile graphics, assistive devices, and mechanical components.

The growing accessibility of 3D printing to everyday users has led to the rapid adoption, sharing of 3D models on sites such as Thingiverse.com, and visions of a future in which customization is a norm and 3D printing can solve a variety of real-world problems. However, in practice, creating models is difficult and many end users simply print models created by others. In this paper, we explore a specific area of model design that is a challenge for end users' measurement. When a model must conform to a specific real world goal once printed, it is important that that goal is precisely specified. We demonstrate that measurement errors are a significant (yet often overlooked) challenge for end users through a systematic study of the sources and types of measurement errors. We argue for a new design principle--accommodating measurement error--that designers, as well as novice modelers, should to use at design time. We offer two strategies--buffer insertion and replacement of minimal parts--to help designers, as well as novice modelers, to build models that are robust to measurement error. We argue that these strategies can reduce the need for and costs of iteration and demonstrate their use in a series of printed objects.

Common appliances have shifted toward flat interface panels, making them inaccessible to blind people. Although blind people can label appliances with Braille stickers, doing so generally requires sighted assistance to identify the original functions and apply the labels. We introduce Facade - a crowdsourced fabrication pipeline to help blind people independently make physical interfaces accessible by adding a 3D printed augmentation of tactile buttons overlaying the original panel. Facade users capture a photo of the appliance with a readily available fiducial marker (a dollar bill) for recovering size information. This image is sent to multiple crowd workers, who work in parallel to quickly label and describe elements of the interface. Facade then generates a 3D model for a layer of tactile and pressable buttons that fits over the original controls. Finally, a home 3D printer or commercial service fabricates the layer, which is then aligned and attached to the interface by the blind person. We demonstrate the viability of Facade in a study with 11 blind participants.

We present FoldMecha, a computer-aided design (CAD) system for exploratory construction of mechanical papercraft. FoldMecha enables students to (a) design their own movements with simple mechanisms by modifying parameters and (b) build physical prototypes. This paper describes the system, as well as associated prototyping methods that make the construction process easier and more adaptable to widely different creations. The paper also discusses a week-long workshop that we held with six teenagers using FoldMecha. The teens successfully designed and built their own mechanisms, and adapted them to a variety of creations. Throughout the workshop, they progressively achieved an advanced level of skill and understanding about mechanical movements.

Recent advances in 3D printing allowed end users to easily utilize desktop 3D printers. However, these printers mainly consume molten plastic, due to limited materiality. While high interests on creating interactive 3D objects with electronics are ubiquitous, commercial 3D printers have its own boundaries of not integrating electronics. On the other hand, paper as flexible medium has shown its possibility to be a channel to host interactive objects, enabling interaction triggered by user. In this paper, we introduce a process of integrating paper circuit into a 3D printed object, combining two universal fabrication techniques, 3D printing and paper crafting: 1) design a hollow 3D model, 2) construct circuits on the 2D planar cutout of this figure, 3) pause 3D printing to insert folded circuits in, 4) resume to continue printing. We empower makers and tinkerers to employ pervasive technology to build interactive 3D objects without hacking machine, obtaining professional utilities, or buying expensive materials.

Digital keypads have proliferated on common appliances, from microwaves and refrigerators to printers and remote controls. For blind people, such interfaces are inaccessible. We conducted a formative study with 6 blind people which demonstrated a need for custom designs for tactile labels without dependence on sighted assistance. To address this need, we introduce Facade - a crowdsourced fabrication pipeline to make physical interfaces accessible by adding a 3D printed augmentation of tactile buttons overlaying the original panel. Blind users capture a photo of an inaccessible interface with a standard marker for absolute measurements using perspective transformation. Then this image is sent to multiple crowd workers, who work in parallel to quickly label and describe elements of the interface. These labels are then used to generate 3D models for a layer of tactile and pressable buttons that fits over the original controls. Users can customize the shape and labels of the buttons using a web interface. Finally, a consumer-grade 3D printer fabricates the layer, which is then attached to the interface using adhesives. Such fabricated overlay is an inexpensive ($10) and more general solution to making physical interfaces accessible.

Everyday tools and objects often need to be customized for an unplanned use or adapted for specific user, such as adding a bigger pull to a zipper or a larger grip for a pen. The advent of low-cost 3D printing offers the possibility to rapidly construct a wide range of such adaptations. However, while 3D printers are now affordable enough for even home use, the tools needed to design custom adaptations normally require skills that are beyond users with limited 3D modeling experience.

In this paper, we propose a prototype of music visualization system that captures and records the music component into digital data form, and then displays the data in visual form for children with hearing impairments. The analog sound data of music played physically is scaled into a binary matrix and scalar values that is then used as data structures for transcribing the output. We designed a system that detects tune and speed from a physical violin, and demonstrated three tangible music visualizations that children see in their daily lives, employing a flowerpot, plants and a picture of frame. We describe how the data captured from physical musical instruments can be seen through these objects, and suggest future possibilities for interactive sound visualization in music education for children with hearing impairments.

Many children's books contain movable pictures with elements that can be physically opened, closed, pushed, pulled, spun, flipped, or swung. But these tangible, interactive reading experiences are inaccessible to children with visual impairments. This paper presents a set of 3D-printable models designed as building blocks for creating movable tactile pictures that can be touched, moved, and understood by children with visual impairments. Examples of these models are canvases, connectors, hinges, spinners, sliders, lifts, walls, and cutouts. They can be used to compose movable tactile pictures to convey a range of spatial concepts, such as in/out, up/down, and high/low. The design and development of these models were informed by three formative studies including 1) a survey on popular moving mechanisms in children's books and 3D-printed parts to implement them, 2) two workshops on the process creating movable tactile pictures by hand (e.g., Lego, Play-Doh), and 3) creation of wood-based prototypes and an informal testing on sighted preschoolers. Also, we propose a design language based on XML and CSS for specifying the content and structure of a movable tactile picture. Given a specification, our system can generate a 3D-printable model. We evaluate our approach by 1) transcribing six children's books, and 2) conducting six interviews on domain experts including four teachers for the visually impaired, one blind adult, two publishers at the National Braille Press, a renowned tactile artist, and a librarian.

3D Printing has shown a great potential to print tactile picture books, in order to cultivate emergent literacy for children with visual impairments. However, currently available 3D design tools are hard to learn, resulting in children to be excluded from the participatory design of tactile pictures. Also, existing 3D design software lacks of functionality to incorporate mobility and rich textures, which is critical aspect of the effective tactile picture. In this paper, we review our formative studies, presenting a hands-on design process for children to empower their own creativities into 3D tactile pictures design, and to engage them to bring other materials to enhance touch experiences.

Young children with visual impairments greatly benefit from tactile graphics (illustrations, images, puzzles, objects) during their learning processes. In this paper we present insight about using a 3D printed tactile picture book as a design probe. This has allowed us to identify and engage stakeholders in our research on improving the technical and human processes required for creating 3D printed tactile pictures, and cultivate a community of practice around these processes. We also contribute insight about how our inperson and digital methods of interacting with teachers, parents, and other professionals dedicated to supporting children with visual impairments contributes to research practices.