pith. machine review for the scientific record. sign in

arxiv: 2605.00804 · v2 · submitted 2026-05-01 · 💻 cs.HC

Recognition: unknown

Prop-Chromeleon: Adaptive Haptic Props in Mixed Reality through Generative Artificial Intelligence

Haoyu Wang , Fengyuan Zhu , Bingjian Huang , Zhecheng Wang , Ludwig Sidenmark
Authors on Pith no claims yet

Pith reviewed 2026-05-09 18:33 UTC · model grok-4.3

classification 💻 cs.HC
keywords mixed realityhaptic feedbackgenerative AIpassive hapticsadaptive propsshape alignmentuser studyimmersion
0
0 comments X

The pith

Generative AI can align virtual assets to physical object shapes to turn them into adaptive passive haptic props for mixed reality.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a mixed reality system that uses text prompts and generative AI to create virtual overlays matching the geometry of everyday physical items, supplying passive haptic feedback where visual-tactile consistency is otherwise missing. It evaluates this through a generation study measuring shape similarity and prompt fidelity across varied objects and prompts, followed by a user study comparing the adaptive approach against static virtual content. The work establishes that shape-aware generation can produce believable interactions while also supporting creative, prompt-driven changes on the same physical base. If the alignment holds, mixed reality experiences could become more immersive without requiring specialized hardware for every virtual element.

Core claim

Prop-Chromeleon is an MR system in which a generative AI pipeline creates and anchors virtual assets to conform to the shapes of physical props according to user text prompts. A generation study with quantitative shape similarity metrics and qualitative prompt analysis, together with a user study, shows higher perceived realism, immersion, and enjoyment than static baselines. The results indicate that shape-aware generation enables both effective passive haptic feedback and creative engagement.

What carries the argument

The generative AI pipeline that generates and geometrically anchors virtual assets to match physical prop shapes under prompt-based control.

Load-bearing premise

Generative AI models can reliably create virtual assets whose geometry aligns closely enough with arbitrary physical props to produce effective passive haptic feedback without unacceptable visual-tactile mismatches.

What would settle it

A user study in which participants rate the adaptive system no higher than static baselines in realism and immersion, or report frequent visual-tactile mismatches when interacting with common objects.

Figures

Figures reproduced from arXiv: 2605.00804 by Bingjian Huang, Fengyuan Zhu, Haoyu Wang, Ludwig Sidenmark, Zhecheng Wang.

Figure 1
Figure 1. Figure 1: Prop-Chromeleon transforms physical objects into adaptive passive haptic props: A) transforming a Paddington view at source ↗
Figure 2
Figure 2. Figure 2: The Prop-Chromeleon processing pipeline combines depth and object tracking, and integrates the user’s prompt by view at source ↗
Figure 3
Figure 3. Figure 3: Prop-Chromeleon from the user’s point of view. view at source ↗
Figure 4
Figure 4. Figure 4: User-generated prompts from preliminary study. Symbols indicate the corresponding prop template specified in view at source ↗
Figure 5
Figure 5. Figure 5: Comparison between generated meshes (blue) and view at source ↗
Figure 6
Figure 6. Figure 6: Prompts, original physical objects, Prop view at source ↗
Figure 7
Figure 7. Figure 7: Participants interacting with different condition view at source ↗
Figure 8
Figure 8. Figure 8: Questionnaire results for individual questions, and haptic factors. We found a significant difference for all questions view at source ↗
Figure 9
Figure 9. Figure 9: Examples of user-generated prompts and their cor view at source ↗
read the original abstract

Mixed Reality (MR) aims to blend digital and physical worlds, but the absence of haptic feedback often breaks visual-tactile consistency. We introduce Prop-Chromeleon, a MR system based on generative artificial intelligence (AI) that dynamically transforms everyday objects into adaptive passive haptic props through user-provided text prompts. Our AI pipeline performs generation and anchoring of virtual assets that align with the shape of physical props, allowing us to study how virtual content generation behaves under geometric and prompt-based constraints. We evaluate Prop-Chromeleon's effectiveness through a generation study using varied object shapes and user prompts, combining quantitative shape similarity metrics with qualitative prompt fidelity analysis. Our user study further showcases Prop-Chromeleon's improvements in perceived realism, immersion, and enjoyment compared to static baselines. These results show that shape-aware generation can support both believable haptic interaction and creative engagement in MR.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces Prop-Chromeleon, an MR system that uses generative AI to dynamically transform everyday physical objects into adaptive passive haptic props via user text prompts. The AI pipeline generates and anchors virtual assets to match prop geometry under geometric and prompt constraints. Evaluation includes a generation study with quantitative shape similarity metrics and qualitative prompt fidelity analysis, plus a user study showing gains in perceived realism, immersion, and enjoyment over static baselines. The central claim is that shape-aware generation enables believable haptic interaction and creative engagement in MR.

Significance. If the results hold with proper validation, the work could advance MR haptics by demonstrating how generative AI enables flexible, prompt-driven adaptation of arbitrary physical props without specialized hardware. Strengths include the empirical focus on both technical metrics (shape similarity) and experiential outcomes (user study), providing a concrete test of alignment between virtual generation and passive feedback. This could support broader applications in creative MR interfaces.

major comments (2)
  1. [Abstract and Generation Study] Abstract and Generation Study section: The manuscript reports quantitative shape similarity metrics (e.g., for varied object shapes) but provides no numerical thresholds (such as maximum Chamfer distance or surface deviation) that would still support believable passive haptic feedback, nor any perceptual calibration linking metrics to tactile mismatch tolerance. This is load-bearing for the central claim, as global metrics may miss local protrusions salient to touch, preventing clear attribution of user-study gains to successful shape-aware haptics rather than visual novelty.
  2. [User Study] User Study section: No sample sizes, statistical tests, or details on alignment error measurement are reported, despite claims of improvements over static baselines in realism and immersion. Without these, the evidence for effective haptic interaction remains difficult to assess and weakens support for the conclusion that the system delivers believable passive haptics.
minor comments (1)
  1. [Abstract] The abstract would be strengthened by including at least one key numerical result (e.g., average shape similarity score) to summarize the quantitative findings.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. The comments highlight important areas for strengthening the link between our technical metrics and haptic outcomes, as well as improving the reporting of our user study. We address each major comment below and indicate the corresponding revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract and Generation Study] Abstract and Generation Study section: The manuscript reports quantitative shape similarity metrics (e.g., for varied object shapes) but provides no numerical thresholds (such as maximum Chamfer distance or surface deviation) that would still support believable passive haptic feedback, nor any perceptual calibration linking metrics to tactile mismatch tolerance. This is load-bearing for the central claim, as global metrics may miss local protrusions salient to touch, preventing clear attribution of user-study gains to successful shape-aware haptics rather than visual novelty.

    Authors: We agree that explicit numerical thresholds and a direct perceptual calibration study would make the connection between shape similarity metrics and believable passive haptics more robust. Our generation study uses established metrics (e.g., Chamfer distance) drawn from the 3D generation literature, supplemented by qualitative inspection of local geometry and prompt fidelity. The user study then supplies the perceptual validation through direct interaction and ratings. To address the concern, we will revise the Generation Study section to discuss the range of observed metric values, cite prior passive-haptics work on tolerable surface deviations, and explicitly note the limitations of global metrics while describing how local features were reviewed qualitatively. This addition clarifies the attribution of user-study gains to shape-aware generation rather than visual novelty alone. revision: yes

  2. Referee: [User Study] User Study section: No sample sizes, statistical tests, or details on alignment error measurement are reported, despite claims of improvements over static baselines in realism and immersion. Without these, the evidence for effective haptic interaction remains difficult to assess and weakens support for the conclusion that the system delivers believable passive haptics.

    Authors: We acknowledge the reporting omission. The submitted manuscript did not include these details in the User Study section. In the revised version we have expanded the section to report the sample size, the statistical tests performed (including test type and significance values), and a full description of the alignment error measurement method together with the observed error statistics. These additions supply the quantitative foundation needed to evaluate the improvements in realism, immersion, and enjoyment relative to the static baselines. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical system evaluation with no derivations or self-referential fits

full rationale

The paper introduces an MR system using generative AI to create virtual assets aligned to physical props, evaluated through a generation study (shape similarity metrics plus prompt fidelity) and a user study (perceived realism, immersion, enjoyment vs. static baselines). No equations, fitted parameters, predictions, or derivation chains appear in the abstract or described content. Central claims rest on external empirical results from user studies and quantitative metrics rather than any quantity defined in terms of itself or reduced by construction to inputs. Self-citations are not invoked as load-bearing uniqueness theorems or ansatzes. The work is self-contained against its reported benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper introduces no new mathematical entities or free parameters. It relies on existing generative AI models whose internal weights are treated as black-box inputs from prior literature. No new physical quantities or conserved entities are postulated.

pith-pipeline@v0.9.0 · 5458 in / 1291 out tokens · 29954 ms · 2026-05-09T18:33:22.100917+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

89 extracted references · 58 canonical work pages · 2 internal anchors

  1. [1]

    Adilzhan Adilkhanov, Matteo Rubagotti, and Zhanat Kappassov. 2022. Haptic Devices: Wearability-Based Taxonomy and Literature Review.IEEE Access10 (2022), 91923–91947. doi:10.1109/ACCESS.2022.3202986 Prop-Chromeleon DIS ’26, June 13–17, 2026, Singapore, Singapore

    work page doi:10.1109/access.2022.3202986 2022

  2. [2]

    Setareh Aghel Manesh, Tianyi Zhang, Yuki Onishi, Kotaro Hara, Scott Bateman, Jiannan Li, and Anthony Tang. 2024. How People Prompt Generative AI to Create Interactive VR Scenes. InDesigning Interactive Systems Conference (DIS ’24, Vol. 7). ACM, 2319–2340. doi:10.1145/3643834.3661547

    work page doi:10.1145/3643834.3661547 2024

  3. [3]

    Ahmed Anwar, Tianzheng Shi, and Oliver Schneider. 2023. Factors of Haptic Experience across Multiple Haptic Modalities. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 260, 12 pages. doi:10.1145/3544548.3581514

    work page doi:10.1145/3544548.3581514 2023

  4. [4]

    Mahdi Azmandian, Mark Hancock, Hrvoje Benko, Eyal Ofek, and Andrew D. Wilson. 2016. Haptic Retargeting: Dynamic Repurposing of Passive Haptics for Enhanced Virtual Reality Experiences. InProceedings of the 2016 CHI Conference on Human Factors in Computing Systems(San Jose, California, USA)(CHI ’16). Association for Computing Machinery, New York, NY, USA, ...

    work page arXiv 2016

  5. [5]

    2025.Blender Python API Documentation

    Blender Foundation. 2025.Blender Python API Documentation. Blender Founda- tion. https://docs.blender.org/api/current/ Accessed: 2025-03-29

    2025

  6. [6]

    Borgefors

    G. Borgefors. 1988. Hierarchical chamfer matching: a parametric edge matching algorithm.IEEE Transactions on Pattern Analysis and Machine Intelligence10, 6 (1988), 849–865. doi:10.1109/34.9107

    work page doi:10.1109/34.9107 1988

  7. [7]

    can i touch this?

    Elodie Bouzbib, Gilles Bailly, Sinan Haliyo, and Pascal Frey. 2021. “can i touch this?”: Survey of virtual reality interactions via haptic solutions: Revue de littéra- ture des interactions en réalité virtuelle par le biais de solutions haptiques. In Proceedings of the 32nd Conference on l’Interaction Homme-Machine. 1–16

    2021

  8. [8]

    Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng- Ann Heng, and Stan Z. Li. 2022. A Survey on Generative Diffusion Models. IEEE Transactions on Knowledge and Data Engineering36 (2022), 2814–2830. https://api.semanticscholar.org/CorpusID:265039918

    2022

  9. [9]

    Parth Chandak. 2023. Leveraging Haptic Feedback in Mixed Reality: Enhancing Training, Skill Acquisition and Robotic Simulation.Journal of Engineering and Applied Sciences Technology(08 2023), 1–6. doi:10.47363/JEAST/2023(5)E170

    work page doi:10.47363/jeast/2023(5)e170 2023

  10. [10]

    ShapeNet: An Information-Rich 3D Model Repository

    Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianx- iong Xiao, Li Yi, and Fisher Yu. 2015.ShapeNet: An Information-Rich 3D Model Repository. Technical Report arXiv:1512.03012 [cs.GR]. Stanford University — Princeton University — Toyota Technological Institute ...

    work page internal anchor Pith review arXiv 2015

  11. [11]

    Lung-Pan Cheng, Li Chang, Sebastian Marwecki, and Patrick Baudisch. 2018. iTurk: Turning Passive Haptics into Active Haptics by Making Users Reconfigure Props in Virtual Reality. InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems(Montreal QC, Canada)(CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–10. doi:10...

    work page doi:10.1145/3173574.3173663 2018

  12. [12]

    Lung-Pan Cheng, Eyal Ofek, Christian Holz, Hrvoje Benko, and Andrew D. Wilson. 2017. Sparse Haptic Proxy: Touch Feedback in Virtual Environments Using a General Passive Prop. InProceedings of the 2017 CHI Conference on Human Factors in Computing Systems(Denver, Colorado, USA)(CHI ’17). Association for Computing Machinery, New York, NY, USA, 3718–3728. doi...

    work page doi:10.1145/3025453 2017

  13. [13]

    ComfyAnonymous. 2025. ComfyUI: A powerful and modular GUI for Stable Diffusion. https://github.com/comfyanonymous/ComfyUI

    2025

  14. [14]

    CompVis. 2022. Stable Diffusion v1.4. https://huggingface.co/CompVis/stable- diffusion-v-1-4-original. Accessed: 2025-03-23

    2022

  15. [15]

    Florian Daiber, Donald Degraen, André Zenner, Tanja Döring, Frank Steinicke, Oscar Javier Ariza Nunez, and Adalberto L. Simeone. 2021. Everyday Proxy Objects for Virtual Reality. InExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems(Yokohama, Japan)(CHI EA ’21). Association for Computing Machinery, New York, NY, USA, Articl...

    work page arXiv 2021

  16. [16]

    Fernanda De La Torre, Cathy Mengying Fang, Han Huang, Andrzej Banburski- Fahey, Judith Amores Fernandez, and Jaron Lanier. 2024. LLMR: Real-time Prompting of Interactive Worlds using Large Language Models. InProceedings of the CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA) (CHI ’24). Association for Computing Machinery, New York, ...

    work page doi:10.1145/3613904.3642579 2024

  17. [17]

    Benjamin Eckstein, Eva Krapp, Anne Elsässer, and Birgit Lugrin. 2019. Smart sub- stitutional reality: Integrating the smart home into virtual reality.Entertainment Computing31 (2019), 100306. doi:10.1016/j.entcom.2019.100306

    work page doi:10.1016/j.entcom.2019.100306 2019

  18. [18]

    Frank, Matthew Groh, Laura Herman, Neil Leach, Robert Mahari, Alex “Sandy” Pentland, Olga Russakovsky, Hope Schroeder, and Amy Smith

    Ziv Epstein, Aaron Hertzmann, L. Herman, Robert Mahari, M. Frank, Matthew Groh, Hope Schroeder, Amy Smith, Memo Akten, Jessica Fjeld, H. Farid, Neil Leach, A. Pentland, and Olga Russakovsky. 2023. Art and the science of generative AI.Science380 (2023), 1110 – 1111. doi:10.1126/science.adh4451

    work page doi:10.1126/science.adh4451 2023

  19. [19]

    Cathy Mengying Fang, Ryo Suzuki, and Daniel Leithinger. 2023. VR Haptics at Home: Repurposing Everyday Objects and Environment for Casual and On- Demand VR Haptic Experiences. InExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(CHI EA ’23). Association for Computing Machinery, New York, NY, USA, Article ...

    work page doi:10.1145/3544549.3585871 2023

  20. [20]

    Mehrad Faridan, Bheesha Kumari, and Ryo Suzuki. 2023. ChameleonControl: Teleoperating Real Human Surrogates through Mixed Reality Gestural Guidance for Remote Hands-on Classrooms. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 203, 1...

    work page arXiv 2023

  21. [21]

    Daniel Gatis. 2023. rembg. https://github.com/danielgatis/rembg Accessed: 2023-09-12

    2023

  22. [22]

    Roberto Gozalo-Brizuela and Eduardo Eduardo Garrido Merchan. 2024. A Survey of Generative AI Applications.Journal of Computer Science20, 8 (May 2024), 801–818. doi:10.3844/jcssp.2024.801.818

    work page doi:10.3844/jcssp.2024.801.818 2024

  23. [23]

    GPT-4o. 2024. GPT-4o: OpenAI’s New Multimodal Flagship Model. https://openai. com/index/gpt-4o. Accessed: 2025-03-29

    2024

  24. [24]

    Khronos Group. 2017. UnityGLTF: Runtime glTF 2.0 Loader for Unity. https: //github.com/KhronosGroup/UnityGLTF. Accessed: 2025-03-29

    2017

  25. [25]

    Ranjan, W

    Rishabh Gupta, Jianjun He, R. Ranjan, W. Gan, Florian Klein, C. Schneiderwind, Annika Neidhardt, K. Brandenburg, and V. Välimäki. 2022. Augmented/Mixed Re- ality Audio for Hearables: Sensing, control, and rendering.IEEE Signal Processing Magazine39 (2022), 63–89. doi:10.1109/MSP.2021.3110108

    work page doi:10.1109/msp.2021.3110108 2022

  26. [26]

    Violet Yinuo Han, Hyunsung Cho, Kiyosu Maeda, Alexandra Ion, and David Lindlbauer. 2023. BlendMR: A Computational Method to Create Ambient Mixed Reality Interfaces.Proc. ACM Hum.-Comput. Interact.7, ISS, Article 436 (Nov. 2023), 25 pages. doi:10.1145/3626472

    work page doi:10.1145/3626472 2023

  27. [27]

    Henderson and Steven Feiner

    Steven J. Henderson and Steven Feiner. 2008. Opportunistic controls: leveraging natural affordances as tangible user interfaces for augmented reality. InPro- ceedings of the 2008 ACM Symposium on Virtual Reality Software and Technology (Bordeaux, France)(VRST ’08). Association for Computing Machinery, New York, NY, USA, 211–218. doi:10.1145/1450579.1450625

    work page doi:10.1145/1450579.1450625 2008

  28. [28]

    Anuruddha Hettiarachchi and Daniel Wigdor. 2016. Annexing Reality: Enabling Opportunistic Use of Everyday Objects as Tangible Proxies in Augmented Reality. InProceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA)(CHI ’16). Association for Computing Machinery, New York, NY, USA, 1957–1967. doi:10.1145/28580...

    work page doi:10.1145/2858036.2858134 2016

  29. [29]

    Goble, and Neal F

    Ken Hinckley, Randy Pausch, John C. Goble, and Neal F. Kassell. 1994. Passive real- world interface props for neurosurgical visualization. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems(Boston, Massachusetts, USA) (CHI ’94). Association for Computing Machinery, New York, NY, USA, 452–458. doi:10.1145/191666.191821

    work page doi:10.1145/191666.191821 1994

  30. [31]

    H.G. Hoffman. 1998. Physically touching virtual objects using tactile augmenta- tion enhances the realism of virtual environments. InProceedings. IEEE 1998 Virtual Reality Annual International Symposium (Cat. No.98CB36180). 59–63. doi:10.1109/VRAIS.1998.658423

    work page doi:10.1109/vrais.1998.658423 1998

  31. [32]

    Junlei Hong, Tobias Langlotz, Jonathan Sutton, and Holger Regenbrecht. 2024. Exploring Visual Discomfort and Opportunities for Vision Augmentations: Vi- sual Noise Cancellation and Head-worn LCD Light Actuators for Perception Modulation. InAdjunct Proceedings of the 26th International Conference on Mobile Human-Computer Interaction(Melbourne, VIC, Austral...

    work page doi:10.1145/3640471.3686642 2024

  32. [33]

    Junlei Hong, Tobias Langlotz, Jonathan Sutton, and Holger Regenbrecht. 2024. Visual Noise Cancellation: Exploring Visual Discomfort and Opportunities for Vision Augmentations.ACM Trans. Comput.-Hum. Interact.31, 2, Article 22 (Jan. 2024), 26 pages. doi:10.1145/3634699

    work page doi:10.1145/3634699 2024

  33. [34]

    Erzhen Hu, Mingyi Li, Jungtaek Hong, Xun Qian, Alex Olwal, David Kim, Seongkook Heo, and Ruofei Du. 2025. Thing2Reality: Enabling Spontaneous Creation of 3D Objects from 2D Content using Generative AI in XR Meetings. InProceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST ’25). Association for Computing Machinery, Ne...

    work page doi:10.1145/3746059.3747621 2025

  34. [35]

    Zhihua Hu, Bo Duan, Yanfeng Zhang, Mingwei Sun, and Jingwei Huang. 2022. MVLayoutNet: 3D Layout Reconstruction with Multi-view Panoramas. InProceed- ings of the 30th ACM International Conference on Multimedia(Lisboa, Portugal) (MM ’22). Association for Computing Machinery, New York, NY, USA, 1289–1298. doi:10.1145/3503161.3548071

    work page doi:10.1145/3503161.3548071 2022

  35. [36]

    Weihan Huang, Stephanie Bourgeois, Yun Suen Pai, and Kouta Minamizawa

  36. [37]

    InSIGGRAPH Asia 2021 Posters(Tokyo, Japan)(SA ’21 Posters)

    ARMixer: Live Stage Monitor Mixing through Gestural Interaction in Augmented Reality. InSIGGRAPH Asia 2021 Posters(Tokyo, Japan)(SA ’21 Posters). Association for Computing Machinery, New York, NY, USA, Article 18, 2 pages. doi:10.1145/3476124.3488632

    work page doi:10.1145/3476124.3488632 2021

  37. [38]

    Xincheng Huang, Michael Yin, Ziyi Xia, and Robert Xiao. 2024. VirtualNexus: Enhancing 360-Degree Video AR/VR Collaboration with Environment Cutouts and Virtual Replicas. InProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology(Pittsburgh, PA, USA)(UIST ’24). Association for Computing Machinery, New York, NY, USA, Article 55...

    work page arXiv 2024

  38. [39]

    Hugging Face. 2024. Hugging Face Spaces. https://huggingface.co/spaces. Ac- cessed: 2025-03-22

    2024

  39. [40]

    Comparing images using the Hausdorff distance

    D.P. Huttenlocher, G.A. Klanderman, and W.J. Rucklidge. 1993. Comparing images using the Hausdorff distance.IEEE Transactions on Pattern Analysis and Machine Intelligence15, 9 (1993), 850–863. doi:10.1109/34.232073

    work page doi:10.1109/34.232073 1993

  40. [41]

    Lukas Höllein, Aljaž Božič, Norman Müller, David Novotny, Hung-Yu Tseng, Christian Richardt, Michael Zollhöfer, and Matthias Nießner. 2024. ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models. In2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5043–5052. doi:10. 1109/CVPR52733.2024.00482

    work page arXiv 2024

  41. [42]

    Apple Inc. [n. d.]. Apple Vision Pro. https://www.apple.com/apple-vision-pro/

  42. [43]

    2001.Passive haptics significantly enhances virtual environ- ments

    Brent Edward Insko. 2001.Passive haptics significantly enhances virtual environ- ments. Ph. D. Dissertation. The University of North Carolina. Advisor(s) Brooks, Frederick P. AAI3007820

    2001

  43. [44]

    Shahram Izadi, David Kim, Otmar Hilliges, David Molyneaux, Richard Newcombe, Pushmeet Kohli, Jamie Shotton, Steve Hodges, Dustin Freeman, Andrew Davison, and Andrew Fitzgibbon. 2011. KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. InProceedings of the 24th Annual ACM Symposium on User Interface Software and Technolog...

    work page doi:10.1145/2047196.2047270 2011

  44. [45]

    Rahul Jain, Jingyu Shi, Runlin Duan, Zhengzhe Zhu, Xun Qian, and Karthik Ramani. 2023. Ubi-TOUCH: Ubiquitous Tangible Object Utilization through Consistent Hand-object interaction in Augmented Reality. InProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology(San Francisco, CA, USA)(UIST ’23). Association for Computing Machi...

    work page doi:10.1145/3586183.3606793 2023

  45. [46]

    Yu Jiang, Zhipeng Li, Mufei He, David Lindlbauer, and Yukang Yan. 2023. HandA- vatar: Embodying Non-Humanoid Virtual Avatars through Hands. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 309, 17 pages. doi:10.1145/3544548.3581027

    work page doi:10.1145/3544548.3581027 2023

  46. [47]

    Mohamed Kari, Tobias Grosse-Puppendahl, Luis Falconeri Coelho, Andreas Rene Fender, David Bethge, Reinhard Schütte, and Christian Holz. 2021. TransforMR: Pose-Aware Object Substitution for Composing Alternate Mixed Realities. In 2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). 69–79. doi:10.1109/ISMAR52148.2021.00021

    work page doi:10.1109/ismar52148.2021.00021 2021

  47. [48]

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuehler, and George Drettakis

  48. [49]

    https://doi.org/10.1145/3592433 Xiaonan Kong and Riley G

    3D Gaussian Splatting for Real-Time Radiance Field Rendering.ACM Trans. Graph.42, 4, Article 139 (jul 2023), 14 pages. doi:10.1145/3592433

    work page doi:10.1145/3592433 2023

  49. [50]

    S. J. Lederman and R. L. Klatzky. 2009. Haptic perception: A tutorial.Attention, Perception, & Psychophysics71, 7 (01 Oct 2009), 1439–1459. doi:10.3758/APP.71.7. 1439

    work page doi:10.3758/app.71.7 2009

  50. [51]

    Tjahjadi, Jiho Kim, Junpu Yu, Minji Park, Jiawen Zhang, Jon E

    Jaewook Lee, Andrew D. Tjahjadi, Jiho Kim, Junpu Yu, Minji Park, Jiawen Zhang, Jon E. Froehlich, Yapeng Tian, and Yuhang Zhao. 2024. CookAR: Affordance Augmentations in Wearable AR to Support Kitchen Tool Interactions for People with Low Vision. InProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology(Pittsburgh, PA, USA)(U...

    work page arXiv 2024

  51. [52]

    Boyu Li, Linping Yuan, Zhe Yan, Qianxi Liu, Yulin Shen, and Zeyu Wang. 2024. AniCraft: Crafting Everyday Objects as Physical Proxies for Prototyping 3D Character Animation in Mixed Reality. InProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology(Pittsburgh, PA, USA) (UIST ’24). Association for Computing Machinery, New York...

    work page doi:10.1145/3654777.3676325 2024

  52. [53]

    Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg Shakhnarovich, and Sai Bi. 2024. Instant3D: Fast Text-to-3D with Sparse-view Generation and Large Reconstruction Model. InThe Twelfth International Conference on Learning Representations. https: //openreview.net/forum?id=2lDQLiH1W4

    2024

  53. [54]

    Zisu Li, Jiawei Li, Zeyu Xiong, Shumeng Zhang, Faraz Faruqi, Stefanie Mueller, Chen Liang, Xiaojuan Ma, and Mingming Fan. 2025. InteRecon: Towards Re- constructing Interactivity of Personal Memorable Items in Mixed Reality. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New Y...

    work page doi:10.1145/3706598.3713882 2025

  54. [55]

    Chuan-en Lin, Ta Ying Cheng, and Xiaojuan Ma. 2020. ARchitect: Building Interactive Virtual Experiences from Physical Affordances by Bringing Human- in-the-Loop. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. doi:10.1145/3313831.3376614

    work page doi:10.1145/3313831.3376614 2020

  55. [56]

    David Lindlbauer and Andy D. Wilson. 2018. Remixed Reality: Manipulat- ing Space and Time in Augmented Reality. InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems(Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–13. doi:10.1145/3173574.3173703

    work page doi:10.1145/3173574.3173703 2018

  56. [57]

    Zhengzhe Liu, Peng Dai, Ruihui Li, Xiaojuan Qi, and Chi-Wing Fu. 2023. DreamStone: Image as a Stepping Stone for Text-Guided 3D Shape Genera- tion.IEEE Trans. Pattern Anal. Mach. Intell.45, 12 (Dec. 2023), 14385–14403. doi:10.1109/TPAMI.2023.3321329

    work page doi:10.1109/tpami.2023.3321329 2023

  57. [58]

    Meta Platforms, Inc. 2024. Introducing Orion, Our First True Augmented Reality Glasses. https://about.fb.com/news/2024/09/introducing-orion-our-first-true- augmented-reality-glasses/ Accessed: 2025-03-23

    2024

  58. [59]

    Microsoft. 2023. Discover Mixed Reality. https://learn.microsoft.com/en-us/ windows/mixed-reality/discover/mixed-reality. Accessed: 2023-07-30

    2023

  59. [60]

    Midjourney. [n. d.]. Midjourney: An independent research lab. https://www. midjourney.com/home. Accessed: 2024-08-06

    2024

  60. [61]

    Srinivasan, Matthew Tancik, Jonathan T

    Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. 2021. NeRF: representing scenes as neural radiance fields for view synthesis.Commun. ACM65, 1 (dec 2021), 99–106. doi:10.1145/ 3503250

    2021

  61. [62]

    OBS Project. 2012. OBS Studio – Open Broadcaster Software. https://obsproject. com/. Version 30.0.2, accessed 2025-03-29

    2012

  62. [63]

    OpenAI. 2023. ChatGPT: Optimizing Language Models for Dialogue. https: //www.openai.com/chatgpt

    2023

  63. [64]

    Navaneeth Prabha, Naeema Ziyad, Navya Prasad, J. P. Abraham, Pristy Paul T, and Rini T Paul. 2024. Enhanced Medical Analysis: Leveraging 3D Visualization and VR-AR Technology.Journal of Sensor Networks and Data Communications (2024). doi:10.33140/jsndc.04.03.03

    work page doi:10.33140/jsndc.04.03.03 2024

  64. [65]

    PTC. 2023. Vuforia Engine Product Page. https://www.ptc.com/en/products/ vuforia/vuforia-engine. Accessed: 2023-07-30

    2023

  65. [66]

    Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen

  66. [67]

    Hierarchical Text-Conditional Image Generation with CLIP Latents

    Hierarchical Text-Conditional Image Generation with CLIP Latents.ArXiv abs/2204.06125 (2022). https://api.semanticscholar.org/CorpusID:248097655

    work page internal anchor Pith review arXiv 2022

  67. [68]

    Patrick Reipschlager, Tamara Flemisch, and Raimund Dachselt. 2021. Personal Augmented Reality for Information Visualization on Large Interactive Displays. IEEE Transactions on Visualization and Computer Graphics27, 2 (2021), 1182–1192. doi:10.1109/TVCG.2020.3030460

    work page doi:10.1109/tvcg.2020.3030460 2021

  68. [69]

    Replicate. 2024. Replicate: Run machine learning models in the cloud. https: //replicate.com. Accessed: 2025-03-22

    2024

  69. [70]

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-Resolution Image Synthesis With Latent Diffusion Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 10684–10695

    2022

  70. [71]

    Sara Mah- davi, Raphael Gontijo-Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Lit, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mah- davi, Raphael Gontijo-Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi. 2022. Photorealistic text-to-image diffusion models with deep language understanding. InProceedings of the 36th Inter...

    2022

  71. [72]

    Yulin Shen, Yifei Shen, Jiawen Cheng, Chutian Jiang, Mingming Fan, and Zeyu Wang. 2024. Neural Canvas: Supporting Scenic Design Prototyping by Integrating 3D Sketching and Generative AI. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI ’24). Association for Computing Machinery, New York, NY, USA, Articl...

    work page arXiv 2024

  72. [73]

    Simeone, Eduardo Velloso, and Hans Gellersen

    Adalberto L. Simeone, Eduardo Velloso, and Hans Gellersen. 2015. Substitutional Reality: Using the Physical Environment to Design Virtual Reality Experiences. InProceedings of the 33rd Annual ACM Conference on Human Factors in Com- puting Systems(Seoul, Republic of Korea)(CHI ’15). Association for Computing Machinery, New York, NY, USA, 3307–3316. doi:10....

    work page doi:10.1145/2702123.2702389 2015

  73. [74]

    Misha Sra, Sergio Garrido-Jurado, Chris Schmandt, and Pattie Maes. 2016. Pro- cedurally generated virtual reality from 3D reconstructed physical space. In Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technol- ogy(Munich, Germany)(VRST ’16). Association for Computing Machinery, New York, NY, USA, 191–200. doi:10.1145/2993369.2993372

    work page doi:10.1145/2993369.2993372 2016

  74. [75]

    Misha Sra, Prashanth Vijayaraghavan, Ognjen Rudovic, Pattie Maes, and Deb Roy. 2017. DeepSpace: Mood-Based Image Texture Generation for Virtual Reality from Music. In2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2289–2298. doi:10.1109/CVPRW.2017.283

    work page doi:10.1109/cvprw.2017.283 2017

  75. [76]

    Stability AI. 2023. Stability AI Official Website. https://stability.ai. Accessed: 2023-07-30

    2023

  76. [77]

    2024.Camera Controls - Stereolabs

    Stereolabs. 2024.Camera Controls - Stereolabs. https://www.stereolabs.com/ docs/video/camera-controls Accessed: 2025-03-22

    2024

  77. [78]

    Tashev and H

    I. Tashev and H. Gamper. 2017. Head-related transfer function personalization for the needs of spatial audio in mixed and virtual reality.Journal of the Acoustical Society of America141 (2017), 3536–3536. doi:10.1121/1.4987470

    work page doi:10.1121/1.4987470 2017

  78. [79]

    Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang, Adam Letts, Yangguang Li, Ding Liang, Christian Laforte, Varun Jampani, and Yan-Pei Cao

  79. [80]

    arXiv preprint arXiv:2403.02151 , year=

    TripoSR: Fast 3D Object Reconstruction from a Single Image.ArXiv abs/2403.02151 (2024). https://api.semanticscholar.org/CorpusID:268248244 Prop-Chromeleon DIS ’26, June 13–17, 2026, Singapore, Singapore

    work page arXiv 2024

  80. [81]

    Vuforia. 2024. Model Targets - Vuforia Library. https://developer.vuforia.com/ library/vuforia-engine/images-and-objects/model-targets/model-targets/. Ac- cessed: 2025-03-22

    2024

Showing first 80 references.