David Kim
I’m a Staff Software Engineer & Manager at Google AR and work on creating new ways to seamlessly interact with computing devices by combining immersive displays, sensing devices and computer graphics and vision techniques. My goal is to enable effortless and dexterous interactions using our hands and the world around us in VR and AR.
Before joining Google, I was a founding team member and Senior Technology Scientist at perceptiveIO and worked on freeform 3D interaction technology as a Researcher at Microsoft Research in Redmond and in Cambridge UK.
I hold a Ph.D. in Computing Science from Newcastle University UK and a Diplom (MSc) in Media Informatics from Ludwig-Maximilian-University (LMU) in Munich Germany.
Personal website: www.davidkim.de
Google Scholar: Scholar
Before joining Google, I was a founding team member and Senior Technology Scientist at perceptiveIO and worked on freeform 3D interaction technology as a Researcher at Microsoft Research in Redmond and in Cambridge UK.
I hold a Ph.D. in Computing Science from Newcastle University UK and a Diplom (MSc) in Media Informatics from Ludwig-Maximilian-University (LMU) in Munich Germany.
Personal website: www.davidkim.de
Google Scholar: Scholar
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Experiencing Thing2Reality: Transforming 2D Content into Conditioned Multiviews and 3D Gaussian Objects for XR Communication
Erzhen Hu
Mingyi Li
Seongkook Heo
Adjunct Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, ACM (2024)
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During remote communication, participants share both digital and physical content, such as product designs, digital assets, and environments, to enhance mutual understanding. Recent advances in augmented communication have facilitated users to swiftly create and share digital 2D copies of physical objects from video feeds into a shared space. However, the conventional 2D representation of digital objects restricts users’ ability to spatially reference items in a shared immersive environment. To address these challenges, we propose Thing2Reality, an Extended Reality (XR) communication platform designed to enhance spontaneous discussions regard-ing both digital and physical items during remote sessions. WithThing2Reality, users can quickly materialize ideas or physical objects in immersive environments and share them as conditioned multiview renderings or 3D Gaussians. Our system enables users to interact with remote objects or discuss concepts in a collaborative manner.
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Augmented Object Intelligence with XR-Objects
Mustafa Doga Dogan
Karan Ahuja
Andrea Colaco
Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST), ACM (2024), pp. 1-15
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Seamless integration of physical objects as interactive digital entities remains a challenge for spatial computing. This paper explores Augmented Object Intelligence (AOI) in the context of XR, an interaction paradigm that aims to blur the lines between digital and physical by equipping real-world objects with the ability to interact as if they were digital, where every object has the potential to serve as a portal to digital functionalities. Our approach utilizes real-time object segmentation and classification, combined with the power of Multimodal Large Language Models (MLLMs), to facilitate these interactions without the need for object pre-registration. We implement the AOI concept in the form of XR-Objects, an open-source prototype system that provides a platform for users to engage with their physical environment in contextually relevant ways using object-based context menus. This system enables analog objects to not only convey information but also to initiate digital actions, such as querying for details or executing tasks. Our contributions are threefold: (1) we define the AOI concept and detail its advantages over traditional AI assistants, (2) detail the XR-Objects system’s open-source design and implementation, and (3) show its versatility through various use cases and a user study.
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ChatDirector: Enhancing Video Conferencing with Space-Aware Scene Rendering and Speech-Driven Layout Transition
Brian Moreno Collins
Karthik Ramani
Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, ACM, pp. 16 (to appear)
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Remote video conferencing systems (RVCS) are widely adopted in personal and professional communication. However, they often lack the co-presence experience of in-person meetings. This is largely due to the absence of intuitive visual cues and clear spatial relationships among remote participants, which can lead to speech interruptions and loss of attention. This paper presents ChatDirector, a novel RVCS that overcomes these limitations by incorporating space-aware visual presence and speech-aware attention transition assistance. ChatDirector employs a real-time pipeline that converts participants' RGB video streams into 3D portrait avatars and renders them in a virtual 3D scene. We also contribute a decision tree algorithm that directs the avatar layouts and behaviors based on participants' speech states. We report on results from a user study (N=16) where we evaluated ChatDirector. The satisfactory algorithm performance and complimentary subject user feedback imply that ChatDirector significantly enhances communication efficacy and user engagement.
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Situationally Induced Impairments and Disabilities (SIIDs) can significantly hinder user experience in everyday activities. Despite their prevalence, existing adaptive systems predominantly cater to specific tasks or environments and fail to accommodate the diverse and dynamic nature of SIIDs. We introduce Human I/O, a real-time system that detects SIIDs by gauging the availability of human input/output channels. Leveraging egocentric vision, multimodal sensing and reasoning with large language models, Human I/O achieves good performance in availability prediction across 60 in-the-wild egocentric videos in 32 different scenarios. Further, while the core focus of our work is on the detection of SIIDs rather than the creation of adaptive user interfaces, we showcase the utility of our prototype via a user study with 10 participants. Findings suggest that Human I/O significantly reduces effort and improves user experience in the presence of SIIDs, paving the way for more adaptive and accessible interactive systems in the future.
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UI Mobility Control in XR: Switching UI Positionings between Static, Dynamic, and Self Entities
Siyou Pei
Yang Zhang
Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, ACM, pp. 12 (to appear)
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Extended reality (XR) has the potential for seamless user interface (UI) transitions across people, objects, and environments. However, the design space, applications, and common practices of 3D UI transitions remain underexplored. To address this gap, we conducted a need-finding study with 11 participants, identifying and distilling a taxonomy based on three types of UI placements --- affixed to static, dynamic, or self entities. We further surveyed 113 commercial applications to understand the common practices of 3D UI mobility control, where only 6.2% of these applications allowed users to transition UI between entities. In response, we built interaction prototypes to facilitate UI transitions between entities. We report on results from a qualitative user study (N=14) on 3D UI mobility control using our FingerSwitches technique, which suggests that perceived usefulness is affected by types of entities and environments. We aspire to tackle a vital need in UI mobility within XR.
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Experiencing InstructPipe: Building Multi-modal AI Pipelines via Prompting LLMs and Visual Programming
Zhongyi Zhou
Jing Jin
Xiuxiu Yuan
Jun Jiang
Jingtao Zhou
Yiyi Huang
Kristen Wright
Jason Mayes
Mark Sherwood
Ram Iyengar
Na Li
Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, ACM, pp. 5
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Foundational multi-modal models have democratized AI access, yet the construction of complex, customizable machine learning pipelines by novice users remains a grand challenge. This paper demonstrates a visual programming system that allows novices to rapidly prototype multimodal AI pipelines. We first conducted a formative study with 58 contributors and collected 236 proposals of multimodal AI pipelines that served various practical needs. We then distilled our findings into a design matrix of primitive nodes for prototyping multimodal AI visual programming pipelines, and implemented a system with 65 nodes. To support users' rapid prototyping experience, we built InstructPipe, an AI assistant based on large language models (LLMs) that allows users to generate a pipeline by writing text-based instructions. We believe InstructPipe enhances novice users onboarding experience of visual programming and the controllability of LLMs by offering non-experts a platform to easily update the generation.
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InstructPipe: Building Visual Programming Pipelines with Human Instructions
Zhongyi Zhou
Jing Jin
Xiuxiu Yuan
Jun Jiang
Jingtao Zhou
Yiyi Huang
Kristen Wright
Jason Mayes
Mark Sherwood
Ram Iyengar
Na Li
arXiv, 2312.09672 (2023)
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Visual programming provides beginner-level programmers with a coding-free experience to build their customized pipelines. Existing systems require users to build a pipeline entirely from scratch, implying that novice users need to set up and link appropriate nodes all by themselves, starting from a blank workspace. We present InstructPipe, an AI assistant that enables users to start prototyping machine learning (ML) pipelines with text instructions. We designed two LLM modules and a code interpreter to execute our solution. LLM modules generate pseudocode of a target pipeline, and the interpreter renders a pipeline in the node-graph editor for further human-AI collaboration. Technical evaluations reveal that InstructPipe reduces user interactions by 81.1% compared to traditional methods. Our user study (N=16) showed that InstructPipe empowers novice users to streamline their workflow in creating desired ML pipelines, reduce their learning curve, and spark innovative ideas with open-ended commands.
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Advanced AR/VR headsets often have a dedicated depth sensor or multiple cameras, high processing power, and a highcapacity battery to track hands or controllers. However, these approaches are not compatible with the small form factor and limited thermal capacity of lightweight AR devices. In this paper, we present RetroSphere, a self-contained 6 degree of freedom (6DoF) controller tracker that can be integrated with almost any device. RetroSphere tracks a passive controller with just 3 retroreflective spheres using a stereo pair of mass-produced infrared blob trackers, each with its own infrared LED emitters. As the sphere is completely passive, no electronics or recharging is required. Each object tracking camera provides a tiny Arduino-compatible ESP32 microcontroller with the 2D position of the spheres. A lightweight stereo depth estimation algorithm that runs on the ESP32 performs 6DoF tracking of the passive controller. Also, RetroSphere provides an auto-calibration procedure to calibrate the stereo IR tracker setup. Our work builds upon Johnny Lee’s Wii remote hacks and aims to enable a community of researchers, designers, and makers to use 3D input in their projects with affordable off-the-shelf components. RetroSphere achieves a tracking accuracy of about 96.5% with errors as low as ∼3.5 cm over a 100 cm tracking range, validated with ground truth 3D data obtained using a LIDAR camera while consuming around 400 mW. We provide implementation details, evaluate the accuracy of our system, and demonstrate example applications, such as mobile AR drawing, 3D measurement, etc. with our Retrosphere-enabled AR glass prototype.
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Opportunistic Interfaces for Augmented Reality: Transforming Everyday Objects into Tangible 6DoF Interfaces Using Ad hoc UI
Mathieu Le Goc
Shengzhi Wu
Danhang "Danny" Tang
Jun Zhang
David Joseph New Tan
Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, ACM
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Real-time environmental tracking has become a fundamental capability in modern mobile phones and AR/VR devices. However, it only allows user interfaces to be anchored at a static location. Although fiducial and natural-feature tracking overlays interfaces with specific visual features, they typically require developers to define the pattern before deployment. In this paper, we introduce opportunistic interfaces to grant users complete freedom to summon virtual interfaces on everyday objects via voice commands or tapping gestures. We present the workflow and technical details of Ad hoc UI (AhUI), a prototyping toolkit to empower users to turn everyday objects into opportunistic interfaces on the fly. We showcase a set of demos with real-time tracking, voice activation, 6DoF interactions, and mid-air gestures and prospect the future of opportunistic interfaces.
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Experiencing Real-time 3D Interaction with Depth Maps for Mobile Augmented Reality in DepthLab
Maksym Dzitsiuk
Luca Prasso
Ivo Duarte
Jason Dourgarian
Joao Afonso
Jose Pascoal
Josh Gladstone
Nuno Moura e Silva Cruces
Shahram Izadi
Konstantine Nicholas John Tsotsos
Adjunct Publication of the 33rd Annual ACM Symposium on User Interface Software and Technology, ACM (2020), pp. 108-110
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We demonstrate DepthLab, a wide range of experiences using the ARCore Depth API that allows users to detect the shape and depth in the physical environment with a mobile phone. DepthLab encapsulates a variety of depth-based UI/UX paradigms, including geometry-aware rendering (occlusion, shadows, texture decals), surface interaction behaviors (physics, collision detection, avatar path planning), and visual effects (relighting, 3D-anchored focus and aperture effects, 3D photos). We have open-sourced our software at https://github.com/googlesamples/arcore-depth-lab to facilitate future research and development in depth-aware mobile AR experiences. With DepthLab, we aim to help mobile developers to effortlessly integrate depth into their AR experiences and amplify the expression of their creative vision.
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