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REVIEW 3 major objections 7 minor 75 references

CoGen3D: An Agentic Human-AI Co-Design Pipeline for 3D Asset Generation for Virtual Reality

T0 review · 3 major / 7 minor · reviewed 2026-07-12 · grok-4.5

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Pith's one-line read A staged conversation-to-2D-to-3D pipeline lets non-experts co-create VR assets that raise scene engagement and shift affect.

desk verdict Useful staged VR co-design system with a large study and clean quality/IKEA findings, but the headline engagement and affect claims rest on a between-cohort contrast that confounds asset presence with cohort and novelty. read the letter →

arxiv 2607.03731 v1 pith:QF5KJL5J submitted 2026-07-04 cs.HC cs.AI

Weiwei Jiang , Wanyu He , Zheyu Tan , Zheyuan Kuang , Difeng Yu , Shinobu Hasegawa , Sven Mayer , Zhanna Sarsenbayeva This is my paper · ORCID
classification cs.HCcs.AI
keywords human-AIco-designagenticsystem3DcontentgenerationvirtualrealityaffectivecomputingcreativitysupporttoolsgenerativeAIimage-to-3D
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

CoGen3D argues that immersive 3D authoring is bottlenecked less by generative model quality than by the interaction model: unconstrained command prompting leaves non-experts unable to articulate intent or validate designs before expensive 3D generation. The system therefore inserts a deliberate three-stage agentic pipeline—LLM-guided conversational elicitation of object semantics, style, and scene fit; mandatory confirmation of a 2D concept image; then image-to-3D conversion and direct Unity VR deployment. In a between-subjects study with 120 participants across six previously validated affective VR scenes, assets co-designed this way were associated with substantially longer scene dwell times and with scene-dependent shifts in valence, arousal, and dominance, most clearly in neutral or negative environments. Both co-designers and independent validators preferred the intermediate concept images over the final 3D meshes and showed no authorship-driven leniency toward the quality drop. The paper’s larger claim is that staging human judgment before 3D rendering can democratize VR content creation and reframe it as collaborative spatial design rather than technical modeling.

What carries the argument

The three-stage CoGen3D pipeline: agentic conversational intent elicitation, mandatory 2D concept-image confirmation gate, and deferred image-to-3D generation with direct VR deployment. The 2D gate is the load-bearing design choice that fronts human judgment before high-latency 3D synthesis.

What would settle it

A within-subjects or fully crossed design in which the same participants experience both asset-free and asset-populated versions of the same scenes (or a pure between-subjects arm that also measures validators on asset-free baselines) and still recovers the reported dwell-time doubling and SAM shifts would support the claim; disappearance of those effects would falsify the attribution to co-designed assets.

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Extended reading notes

Core claim

A staged agentic pipeline that elicits intent conversationally, requires user confirmation of a 2D concept image, then generates and deploys a 3D asset into VR enables non-experts to produce scene-congruent props whose presence is associated with higher engagement and shifted affective responses, while both co-designers and independent raters prefer concept images over final meshes with no IKEA-style ownership leniency.

Load-bearing premise

The main affective and engagement contrasts treat designers’ asset-free pre-design ratings as a clean baseline for validators who only ever saw scenes already populated by other people’s assets, so any cohort, order, or novelty differences could be misread as effects of the assets themselves.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. The paper presents CoGen3D, an agentic human–AI co-design pipeline that scaffolds non-expert VR asset authoring through conversational intent elicitation, mandatory 2D concept-image confirmation, and image-to-3D generation with direct Unity deployment. A between-subjects study (N=120) uses six validated affective VR scenes: 60 Design participants co-create one asset per scene after an asset-free baseline, and 60 Validation participants experience scenes populated with those assets. Analyses (CLMM for ordinal satisfaction, ART ANOVA for SAM, Gamma GLMMs for dwell and interaction telemetry, conversation logs) report higher scene engagement and scene-dependent SAM shifts when assets are present, a systematic preference for 2D concept images over final 3D meshes with no Group×Modality authorship leniency, and environment-shaped conversational pacing. The authors argue that staged, intent-based co-design can democratize VR authoring.

Significance. If the claims hold under tighter causal framing, this is a solid systems-and-evaluation contribution to HCI/XR authoring: an open, instrumented multi-stage pipeline; a large study on validated affective stimuli with appropriate mixed models, FDR post-hocs, and covariates; and actionable design implications (2D confirmation gates, scene-aware spawn heuristics, limits of psychological ownership in generative co-creation). Strengths include end-to-end deployability, dual designer/validator evaluation of the same assets, rich telemetry (≈477k spatial updates), and conversation/prompt analyses that link target environments to design reasoning. The work is timely given rapid text-to-image and image-to-3D progress and the persistent gap between unconstrained prompting and usable immersive authoring.

major comments (3)
  1. §4.1–4.2, §4.4, and primary models in §5.3–5.4: The headline engagement and affective claims attribute higher dwell (Group χ²(1)=82.19, p<.001; ~1.8–2.3×) and Group×Scene SAM interactions to insertion of co-designed assets, but the primary contrast is Designers’ pre-design (asset-free) measures versus an entirely separate Validation cohort that only ever saw populated scenes. Covariates (age, gender, VR experience) do not remove unmeasured cohort differences, order, or novelty/exploration of any novel objects. Designers’ own post-design (with-asset) data exist but are de-emphasized for the primary models. Soften causal language to between-cohort association, or add and foreground within-designer pre/post analyses (and/or validators with a true asset-free arm) so asset presence is not confounded with participant and protocol differences.
  2. §5.3 and Discussion §6.1 (RQ3): Relatedly, the paper states that assets “shifted emotional responses” and “significantly altered user behavior,” while Limitations §6.6 correctly flags novelty and cross-sectional design. Align the Results and Discussion wording with the Limitations: either report additional controls that isolate asset presence, or restate findings as associations under the current between-cohort design rather than as effects of generated assets per se.
  3. §5.2 / Table 2–3: The no-IKEA / no Group×Modality conclusion is important and currently rests on non-significant interactions. Given the ordinal CLMM and multiple scenes, please report effect sizes or equivalence-style bounds (or power considerations) for Group and Group×Modality so readers can judge whether absence of authorship leniency is well supported versus underpowered, not only that p-values exceed .05.
minor comments (7)
  1. §4.4 heading: typo “Measuremetns” → “Measurements”.
  2. §5.3: “thaDesigner group’s” → “the Designer group’s”.
  3. §3.1.2 / abstract vs body: model naming is inconsistent (DeepSeek V3 vs V3.2); standardize throughout.
  4. Figure 5–8: ensure all panels have readable axis labels and that color encodings for Design vs Validation are consistent across figures; some density plots are hard to parse in grayscale.
  5. §5.5: generation-time SDs are large for 3D (194±124 s); briefly note how timeouts/retries were handled in the user-facing protocol so latency claims are interpretable.
  6. Related Work: a short explicit comparison table (or paragraph) against VRCopilot, ImaginateAR, and Dreamcrafter on stages (conversation / 2D gate / deploy / evaluation) would sharpen the claimed gap.
  7. Ethics/data: state whether conversation transcripts and assets will be released with the open-source pipeline, and any de-identification steps.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: empirical HCI systems paper with independently measured outcomes; self-citations supply validated stimuli, not load-bearing proofs of co-design claims.

full rationale

CoGen3D is an empirical systems/HCI paper. Its central claims (pipeline enables non-expert co-design; co-designed assets associate with higher dwell and scene-dependent SAM shifts; 2D>3D satisfaction without Group×Modality authorship leniency; scene shapes chat pacing) rest on a user study (N=120) with ordinal CLMMs, ART ANOVA, Gamma GLMMs, telemetry, and chat logs. These outcomes are measured independently of how the pipeline is defined; there is no fitted theoretical quantity re-labeled as a first-principles prediction, no uniqueness theorem imported from the authors, and no ansatz smuggled in via self-citation. Citations to the authors’ prior affective VR stimulus sets [24, 32] establish scene baselines and protocol continuity, which is standard stimulus reuse rather than a circular derivation of the co-design results. Between-cohort confounds (Design pre vs Validation) are a causal-identification concern, not circularity. Score 0; steps empty.

Assumptions & free parameters 4 free parameters · 4 assumptions · 1 invented entities

Load-bearing premises are standard HCI/affective-VR assumptions plus engineering configuration choices for generative endpoints. No new physical entities are postulated. The central empirical claims rest on validity of SAM and the six-scene stimulus set, the between-group asset-presence contrast, and fixed generative hyperparameters chosen from latency/quality sweeps rather than fitted to user outcomes.

free parameters (4)
  • FLUX.1 study config (resolution, steps, guidance, seed)
    Fixed at 512×512, 16 steps, guidance 1.2, seed 42 after latency sweeps; these choices shape concept-image quality and thus downstream satisfaction, but are not fitted to the user-study dependent variables.
  • Hunyuan3D-2 study config (steps, octree, face count, guidance, seed)
    Fixed at 100 steps, octree 256, 100k faces, guidance 5.5, seed 42 as a quality-latency trade-off; directly affects the 3D quality bottleneck central to RQ2.
  • DeepSeek V3.2 dialogue hyperparameters
    Temperature 0.7 and max tokens 2000/1000 chosen to balance creativity and parseability; influences elicitation quality.
  • Default VR spawn scale/orientation heuristic
    Assets load with a default scale/orientation; users then correct scale systematically by scene, so the heuristic is an unlearned free choice that shapes interaction effort.
assumptions (4)
  • domain assumption Self-Assessment Manikin (valence, arousal, dominance) validly captures affective response in these VR scenes.
    Used as primary affective instrument after each scene (§4.4, §5.3); standard in affective VR but still an assumption about construct validity.
  • domain assumption The six open-source scenes span Russell’s valence–arousal circumplex and provide stable emotional baselines.
    Study design anchors on prior validated stimulus sets [24,32] (Table 1); scene main effects are large, but generalizability beyond this set is assumed.
  • ad hoc to paper Between-group comparison of Designers’ pre-design (asset-free) measures vs. Validators’ with-asset measures isolates effects of generated assets.
    Primary affective/engagement contrast (§4.4, §5.3–5.4) is not within-subject for validators and confounds cohort with condition.
  • domain assumption Image-to-3D conversion quality is an inherent pipeline property rather than primarily a function of user skill or prompt engineering.
    Used to interpret the modality gap and absence of IKEA effect (§5.2, §6.3).
invented entities (1)
  • CoGen3D staged agentic co-design pipeline
    purpose: Name and operationalize the conversation → 2D confirmation → image-to-3D → Unity deploy workflow evaluated in the study.
    System contribution of the paper; composed of existing models (DeepSeek, FLUX.1, Hunyuan3D-2) plus orchestration and VR client. Independent evidence is the user-study outcomes and claimed open-source release, not an external physical prediction.

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Cite this review

Pith. "Pith review of CoGen3D: An Agentic Human-AI Co-Design Pipeline for 3D Asset Generation for Virtual Reality." pith.science (2026). https://pith.science/paper/QF5KJL5J

@misc{pith2026260703731,
  author       = {Pith},
  title        = {Pith review of: CoGen3D: An Agentic Human-AI Co-Design Pipeline for 3D Asset Generation for Virtual Reality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QF5KJL5J}},
  note         = {Machine review of arXiv:2607.03731}
}
read the original abstract

Creating 3D assets for virtual reality requires modeling expertise, which restricts the authorship of immersive experiences. Existing generative AI tools rely on unconstrained, command-driven prompting, lacking the conversational scaffolding needed for users to articulate their intent and validate designs prior to rendering. To address this, we introduce CoGen3D, an agentic human-AI co-design pipeline that proactively guides users through conversational intent elicitation, a concept image confirmation, and image-to-3D generation that directly deploys to immersive scenes. We evaluated this system through a user study (N=120) across six affectively diverse immersive scenes, observing 60 Design group participants who co-created 3D assets for the scenes, and 60 Validation group participants who experienced the scenes with generated assets. Our findings show that co-designed assets are associated with higher scene engagement and shifted affective responses, while participants generally preferred concept images over the final 3D assets, with no increased leniency toward degradation in their own creations. Analysis of the human-AI conversations further shows that target environments shape users' conversational patterns. Our results suggest that our staged, intent-based co-design can democratize virtual reality authoring and shift immersive content creation from technical execution toward collaborative spatial design.

Figures

Figures reproduced from arXiv: 2607.03731 by the authors.

Figure 1
Figure 1. System design of CoGen3D. Top: the design pipeline of our system, compared with a conventional manual design process. [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Generation endpoint benchmarks. Left: FLUX.1 wall-clock latency versus diffusion steps, faceted by output resolution. Right Hunyuan3D-2 wall-clock latency with parameters between pairs of inference steps, octree resolution, and number of faces. The bottom parts of both panels show example outputs at the selected parameter settings. palette, and narrative fit, and only proposes a draft text-to-image prompt after the … view at source ↗
Figure 3
Figure 3. Study protocol for the experiments. For participants in the [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Examples of generated text-to-image prompts, images, and 3D assets for each of the six scenes. The left column shows the [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: Generation quality assessments. a)–b) Kernel density plots of the joint distribution of image vs. asset satisfaction ratings [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: SAM results for Design group and Validation group. a) Scatter plot of mean SAM ratings for each scene and group. b)-d) Violin plots showing the distribution of SAM ratings for each scene and group across the three dimensions (valence, arousal, dominance). The plots ill…
Figure 7
Figure 7. Figure 7: Scene engagement and validator interaction metrics. a) scene engagement time across environments. b)-d) Total path length [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: Temporal characteristics of the co-design pipeline across scenes. a) Number of messages in the co-design session. b) AI and [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]
Figure 9
Figure 9. Figure 9: Text-to-image prompt analysis. a) the Top-5 objects designed by participants for each scene; b) the Top-5 categories of the [PITH_FULL_IMAGE:figures/full_fig_p022_9.png]
Figure 10
Figure 10. Figure 10: Design experience feedback: a) Designer-reported ease, helpfulness, creativity support, and engagement with the co-design [PITH_FULL_IMAGE:figures/full_fig_p024_10.png]

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Works this paper leans on

75 extracted references · 19 canonical work pages

  1. [1]

    Narges Ashtari, Andrea Bunt, Joanna McGrenere, Michael Nebeling, and Parmit K. Chilana. 2020. Creating Augmented and Virtual Reality Applications: Current Practices, Challenges, and Opportunities. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA)(CHI ’20). Association for Computing Machinery, New York, NY, ...

    doi:10.1145/3313831.3376722 2020

  2. [2]

    Yanqi Bao, Tianyu Ding, Jing Huo, Yaoli Liu, Yuxin Li, Wenbin Li, Yang Gao, and Jiebo Luo. 2025. 3d gaussian splatting: Survey, technologies, challenges, and opportunities.IEEE Transactions on Circuits and Systems for Video Technology35, 7 (2025), 6832–6852. doi:10.1109/tcsvt.2025.3538684

    work page doi:10.1109/tcsvt.2025.3538684 2025

  3. [3]

    Lyn Bartram, Abhisekh Patra, and Maureen Stone. 2017. Affective color in visualization. InProceedings of the 2017 CHI conference on human factors in computing systems. 1364–1374

    2017

  4. [4]

    Bradley and Peter J

    Margaret M. Bradley and Peter J. Lang. 1994. Measuring Emotion: The Self-Assessment Manikin and the Semantic Differential.Journal of Behavior Therapy and Experimental Psychiatry25, 1 (1994), 49–59

    1994

  5. [5]

    Arthur Caetano, Kavya Verma, Atieh Taheri, Radha Kumaran, Zichen Chen, Jiaao Chen, Tobias Höllerer, and Misha Sra. 2025. Agentic workflows for conversational human-ai interaction design.arXiv preprint arXiv:2501.18002(2025)

    arXiv 2025

  6. [6]

    Ang Cai, Chao Yao, Hongjun Liu, Changsheng Li, Yongyue Zhang, Yu Guo, Xiaokun Wang, and Xiaojuan Ban. 2025. HPIPainting: A Hand-Pen Interaction for VR Painting.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.9, 3, Article 71 (Sept. 2025), 26 pages. doi:10.1145/3749538

    work page doi:10.1145/3749538 2025

  7. [7]

    May Kristine Jonson Carlon, Julian Rodney Matthews, and Yasuo Kuniyoshi. 2025. A Bayesian Exploration on the Motivational and Behavioral Impacts of Chatbots in Language Learning. InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–7. doi:10.1145/3706599.3720088

    doi:10.1145/3706599.3720088 2025

  8. [8]

    Hansheng Chen, Jiatao Gu, Anpei Chen, Wei Tian, Zhuowen Tu, Lingjie Liu, and Hao Su. 2023. Single-stage diffusion nerf: A unified approach to 3d generation and reconstruction. InProceedings of the IEEE/CVF international conference on computer vision. 2416–2425. doi:10.1109/iccv51070.2023.00229

    doi:10.1109/iccv51070.2023.00229 2023

Show all 75 references
  1. [9]

    Junlong Chen. 2026. AI4XR: AI in Extended Reality for 3D Scene Editing and Accessibility Design. InProceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26). Association for Computing Machinery, New York, NY, USA, Artic...

    2026 doi

  2. [10]

    Alice Chirico, Francesco Ferrise, Lorenzo Cordella, and Andrea Gaggioli. 2018. Designing Awe in Virtual Reality: An Experimental Study.Frontiers in Psychology8 (2018), 2351

    2018

  3. [11]

    Lars Rune Christensen and Anette CM Petersen. 2026. Discretionary Freedom in Social Work? Co-Design of AI-Enabled Case Management System in Trouble.Computer Supported Cooperative Work (CSCW)35, 2 (2026), 3. doi:10.1007/s10606-026-09539-3

    2026 doi

  4. [12]

    Mihaly Csikszentmihalyi and Reed Larson. 1987. Validity and reliability of the experience-sampling method.The Journal of nervous and mental disease175, 9 (1987), 526–536

    1987

  5. [13]

    Nicholas Davis. 2013. Human-Computer Co-Creativity: Blending Human and Computational Creativity. InProceedings of the AAAI Workshop on Intelligent Narrative Technologies

    2013

  6. [14]

    Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J

    DeepSeek-AI, Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei L...

    2025 arXiv

  7. [15]

    Birgitta Dresp-Langley. 2016. Affine geometry, visual sensation, and preference for symmetry of things in a thing.Symmetry8, 11 (2016), 127

    2016

  8. [16]

    Aviv Elor, Asiiah Song, and Sri Kurniawan. 2021. Understanding emotional expression with haptic feedback vest patterns and immersive virtual reality. In2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). 183–188

    2021

  9. [17]

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. 2024. Scaling Rectified Flow Transformers for High-Resolution Image Synthesis. InProceedings of the 41st International Confe...

    2024

  10. [18]

    Sergio Estupiñán, Francisco Rebelo, Paulo Noriega, Carlos Ferreira, and Emília Duarte. 2014. Can virtual reality increase emotional responses (arousal and valence)? A pilot study. InInternational conference of design, user experience, and usability. 541–549

    2014

  11. [19]

    Shereen Fouad, Lilit Hakobyan, Izegbua E Ihongbe, Manolya Kavakli-Thorne, Sarah Atkins, and Bahadar Bhatia. 2026. Human-Centered User Interface Design for Explainable AI in Chest Radiology: A Multi-Phase Co-Design Approach.IEEE Access(2026). doi:10.1109/access.2026.3653233

    2026 doi

  12. [20]

    Frederic Gmeiner, Humphrey Yang, Lining Yao, Kenneth Holstein, and Nikolas Martelaro. 2023. Exploring challenges and opportunities to support designers in learning to co-create with AI-based manufacturing design tools. InProceedings of the 2023 CHI conference on human factors ...

    2023 doi

  13. [21]

    Paul Henderson, Vagia Tsiminaki, and Christoph H Lampert. 2020. Leveraging 2d data to learn textured 3d mesh generation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 7498–7507. doi:10.1109/cvpr42600.2020.00752

    2020 doi

  14. [22]

    Lukas Höllein, Ang Cao, Andrew Owens, Justin Johnson, and Matthias Nießner. 2023. Text2room: Extracting textured 3d meshes from 2d text-to-image models. InProceedings of the IEEE/CVF International Conference on Computer Vision. 7909–7920. doi:10.1109/iccv51070.2023.00727

    2023 doi

  15. [23]

    Xincheng Huang and Robert Xiao. 2024. SurfShare: Lightweight Spatially Consistent Physical Surface and Virtual Replica Sharing with Head-mounted Mixed-Reality.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.7, 4, Article 162 (Jan. 2024), 24 pages. doi:10.1145/3631418

    2024 doi

  16. [24]

    Weiwei Jiang, Maximiliane Windl, Benjamin Tag, Zhanna Sarsenbayeva, and Sven Mayer. 2024. An Immersive and Interactive VR Dataset to Elicit Emotions.IEEE Transactions on Visualization and Computer Graphics(2024)

    2024

  17. [25]

    Crescent Jicol, Chun Hin Wan, Benjamin Doling, Caitlin H Illingworth, Jinha Yoon, Charlotte Headey, Christof Lutteroth, Michael J Proulx, Karin Petrini, and Eamonn O’Neill. 2021. Effects of emotion and agency on presence in virtual reality. InProceedings of the 2021 CHI confer...

    2021

  18. [26]

    Qianzhi Jing, Hankai Lu, Shuojin Huang, Peter Childs, and Liuqing Chen. 2026. Req2CAD: bridging functional requirements and parametric CAD models to support conceptual 3D design. InProceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26). Associat...

    2026 doi

  19. [27]

    Joohee Jun, Myeongul Jung, So-Yeon Kim, and Kwanguk Kim. 2018. Full-body ownership illusion can change our emotion. InProceedings of the 2018 CHI conference on human factors in computing systems. 1–11

    2018

  20. [28]

    Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzciński, and Andrea Tagliasacchi. 2022. Conerf: Controllable neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18623–18632. doi:10.1109/cvpr52688.2022.01807

    2022 doi

  21. [29]

    Angelika C Kern, Wolfgang Ellermeier, and Lina Jost. 2020. The influence of mood induction by music or a soundscape on presence and emotions in a virtual reality park scenario. InProceedings of the 15th International Conference on Audio Mostly. 233–236

    2020

  22. [30]

    Seokyoung Kim, Dooyoung Kim, Taejun Son, and Woontack Woo. 2025. RealityCrafter: User-guided Editable 3D Scene Generation from a Single Image in Mixed Reality. InAdjunct Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST Adjunct ’25). ...

    2025 doi

  23. [31]

    Stephen James Krol, Maria Teresa Llano Rodriguez, and Miguel J Loor Paredes. 2025. Exploring the needs of practising musicians in co-creative ai through co-design. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–13. doi:10.1145/3706598.3713894

    2025 doi

  24. [32]

    Salim, Benjamin Tag, Anusha Withana, and Zhanna Sarsenbayeva

    Zheyuan Kuang, Tinghui Li, Weiwei Jiang, Sven Mayer, Flora D. Salim, Benjamin Tag, Anusha Withana, and Zhanna Sarsenbayeva. 2026. Under- standing the Effects of Interaction on Emotional Experiences in VR. InProceedings of the 2026 CHI Conference on Human Factors in Computing S...

    2026 doi

  25. [33]

    Black Forest Labs, Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, Sumith Kulal, Kyle Lacey, Yam Levi, Cheng Li, Dominik Lorenz, Jonas Müller, Dustin Podell, Robin Rombach, Harry Saini,...

    2025 arXiv

  26. [34]

    Froehlich, Gabriel Brostow, and Jessica Van Brummelen

    Jaewook Lee, Filippo Aleotti, Diego Mazala, Guillermo Garcia-Hernando, Sara Vicente, Oliver James Johnston, Isabel Kraus-Liang, Jakub Powierza, Donghoon Shin, Jon E. Froehlich, Gabriel Brostow, and Jessica Van Brummelen. 2025. ImaginateAR: AI-Assisted In-Situ Authoring in Augm...

    2025 doi

  27. [35]

    Li, Jeremy N

    Benjamin J. Li, Jeremy N. Bailenson, Adam Pines, Walter J. Greenleaf, and Leanne M. Williams. 2017. A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self-Report Measures.Frontiers in Psychology...

    2017

  28. [36]

    Dong-Yang Li, Yi-Long Liu, Zi-Xian Liu, Yan-Pei Cao, Meng-Hao Guo, and Shi-Min Hu. 2026. SeparateGen: Semantic Component-based 3D Character Generation from Single Images.IEEE Transactions on Visualization and Computer Graphics(2026). doi:10.1109/tvcg.2026.3652452

    2026 doi

  29. [37]

    Jiawei Li, Zisu Li, Siyu Chen, Ziyan Wang, Yukai Zhang, Mingming Fan, and Liang He. 2026. SafetyBuilder: An AR-based Framework for In-situ AI-assisted Creation of Child Safety Protection.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.10, 1, Article 8 (March 2026), 29 pag...

    2026 doi

  30. [38]

    Ruohao Li, Jiawei Li, Jia Sun, Zhiqing Wu, Zisu Li, Ziyan Wang, Ge Lin, and Mingming Fan. 2025. RemVerse: Supporting Reminiscence Activities for Older Adults through AI-Assisted Virtual Reality.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.9, 3, Article 103 (Sept. 2025)...

    2025 doi

  31. [39]

    Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin

  32. [40]

    InProceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Magic3d: High-resolution text-to-3d content creation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 300–309. doi:10.1109/cvpr52729.2023.00037

    2023 doi

  33. [41]

    Phoebe Lin and Jessica Van Brummelen. 2021. Engaging teachers to co-design integrated AI curriculum for K-12 classrooms. InProceedings of the 2021 CHI conference on human factors in computing systems. 1–12. doi:10.1145/3411764.3445377

    2021 doi

  34. [42]

    Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund Varma T, Zexiang Xu, and Hao Su. 2023. One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization.Advances in Neural Information Processing Systems36 (2023), 22226–22246. doi:10.52202/075280-0976

    2023 doi

  35. [43]

    Ruoshi Liu, Aditya Ramesh, Ce Liu, and Chenfanfu Zhu. 2023. Zero-1-to-3: Zero-shot One Image to 3D Object. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

    2023

  36. [44]

    Danielle Lottridge, Mark Chignell, and Aleksandra Jovicic. 2011. Affective interaction: understanding, evaluating, and designing for human emotion. Reviews of Human Factors and Ergonomics7, 1 (2011), 197–217

    2011

  37. [45]

    Michael A Madaio, Luke Stark, Jennifer Wortman Vaughan, and Hanna Wallach. 2020. Co-designing checklists to understand organizational challenges and opportunities around fairness in AI. InProceedings of the 2020 CHI conference on human factors in computing systems. 1–14. doi:1...

    2020 doi

  38. [46]

    Hidenobu Matsuki, Gwangbin Bae, and Andrew J Davison. 2025. 4dtam: Non-rigid tracking and mapping via dynamic surface gaussians. In Proceedings of the Computer Vision and Pattern Recognition Conference. 26921–26932. doi:10.1109/cvpr52734.2025.02507

    2025 doi

  39. [47]

    Norton, Daniel Mochon, and Dan Ariely

    Michael I. Norton, Daniel Mochon, and Dan Ariely. 2012. The IKEA effect: When labor leads to love.Journal of Consumer Psychology22, 3 (2012), 453–460. doi:10.1016/j.jcps.2011.08.002

    2012 doi

  40. [48]

    Fatema Rahimi, Abolghasem Sadeghi-Niaraki, and Soo-Mi Choi. 2025. Generative AI Meets Virtual Reality: A Comprehensive Survey on Applications, Challenges, and Future Direction.IEEE Access13 (2025), 94893–94909. doi:10.1109/ACCESS.2025.3574779

    2025 doi

  41. [49]

    Amit Raj, Srinivas Kaza, Ben Poole, Michael Niemeyer, Nataniel Ruiz, Ben Mildenhall, Shiran Zada, Kfir Aberman, Michael Rubinstein, Jonathan Barron, et al. 2023. Dreambooth3d: Subject-driven text-to-3d generation. InProceedings of the IEEE/CVF international conference on compu...

    2023 doi

  42. [50]

    Giuseppe Riva, Fabrizia Mantovani, Claret Samantha Capideville, Alessandra Preziosa, Francesca Morganti, Daniela Villani, Andrea Gaggioli, Cristina Botella, and Mariano Alcañiz. 2007. Affective interactions using virtual reality: the link between presence and emotions.Cyberpsy...

    2007

  43. [51]

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

    2022

  44. [52]

    James A. Russell. 1980. A Circumplex Model of Affect.Journal of Personality and Social Psychology39, 6 (1980), 1161–1178

    1980

  45. [53]

    Russell and Albert Mehrabian

    James A. Russell and Albert Mehrabian. 1977. Evidence for a Three-Factor Theory of Emotions.Journal of Research in Personality11, 3 (1977), 273–294

    1977

  46. [54]

    Jalal Safari Bazargani, Abolghasem Sadeghi-Niaraki, and Soo-Mi Choi. 2026. From 2D to XR: enhancing object placement in design simulations through spatial relations.Virtual Reality30, 2 (24 Feb 2026), 76. doi:10.1007/s10055-026-01327-0

    2026 doi

  47. [55]

    Karen B Schloss, Laurent Lessard, Chris Racey, and Anya C Hurlbert. 2018. Modeling color preference using color space metrics.Vision Research151 (2018), 99–116

    2018

  48. [56]

    Benjamin Schöne, Joanna Kisker, Rebecca Sophia Sylvester, Elise Leila Radtke, and Thomas Gruber. 2023. Library for universal virtual reality experiments (luVRe): A standardized immersive 3D/360 picture and video database for VR based research.Current Psychology42, 7 (2023), 5366–5384

    2023

  49. [57]

    Ben Shneiderman. 2007. Creativity Support Tools: Accelerating Discovery and Innovation.Commun. ACM50, 12 (2007), 20–32

    2007

  50. [58]

    Rukshani Somarathna, Tomasz Bednarz, and Gelareh Mohammadi. 2022. Virtual Reality for Emotion Elicitation: A Review.IEEE Transactions on Affective Computing(2022), 1–21. doi:10.1109/TAFFC.2022.3181053

    2022 doi

  51. [59]

    Jiaxiang Tang, Hang Zhou, Xiaokang Chen, Tianshu Hu, Errui Ding, Jingdong Wang, and Gang Zeng. 2023. Delicate textured mesh recovery from nerf via adaptive surface refinement. InProceedings of the IEEE/CVF International Conference on Computer Vision. 17739–17749. doi:10.1109/i...

    2023 doi

  52. [60]

    Christina Tsalicoglou, Fabian Manhardt, Alessio Tonioni, Michael Niemeyer, and Federico Tombari. 2024. Textmesh: Generation of realistic 3d meshes from text prompts. In2024 International Conference on 3D Vision (3DV). IEEE, 1554–1563. doi:10.1109/3dv62453.2024.00154

    2024 doi

  53. [61]

    Cyrus Vachha, Yixiao Kang, Zach Dive, Ashwat Chidambaram, Anik Gupta, Eunice Jun, and Björn Hartmann. 2025. Dreamcrafter: Immersive Editing of 3D Radiance Fields Through Flexible, Generative Inputs and Outputs. InProceedings of the 2025 CHI Conference on Human Factors in Compu...

    2025 doi

  54. [62]

    Nadine Wagener, Arne Kiesewetter, Leon Reicherts, Paweł W Woźniak, Johannes Schöning, Yvonne Rogers, and Jasmin Niess. 2024. MoodShaper: A Virtual Reality Experience to Support Managing Negative Emotions. InProceedings of the 2024 ACM Designing Interactive Systems Conference. ...

    2024

  55. [63]

    Dhanuja Wanniarachchi and Archan Misra. 2025. MIMIC: AI and AR-enhanced Multi-Modal, Immersive, Relative Instruction Comprehension.Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.9, 1, Article 21 (March 2025), 34 pages. doi:10.1145/3712268

    2025 doi

  56. [64]

    Chao Wen, Yinda Zhang, Chenjie Cao, Zhuwen Li, Xiangyang Xue, and Yanwei Fu. 2022. Pixel2Mesh++: 3D mesh generation and refinement from multi-view images.IEEE Transactions on Pattern Analysis and Machine Intelligence45, 2 (2022), 2166–2180. doi:10.1109/tpami.2022.3169735

    2022 doi

  57. [65]

    Ruoyu Wen, Kunal Gupta, Simon Hoermann, Mark Billinghurst, Alaeddin Nassani, and Thammathip Piumsomboon. 2026. From Prompt to Presence: Co-Creating Personalised Emotional Sanctuaries in VR with Generative AI. InProceedings of the 31st International Conference on Intelligent Us...

    2026 doi

  58. [66]

    Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. 2025. Structured 3d latents for scalable and versatile 3d generation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 21469–...

    2025 doi

  59. [67]

    Tianhua Xie, Mingliang Cao, and Zhigeng Pan. 2020. Applying self-assessment manikin (sam) to evaluate the affective arousal effects of vr games. InProceedings of the 2020 3rd International Conference on Image and Graphics Processing. 134–138

    2020

  60. [68]

    Bojun Xiong, Jialun Liu, Jiakui Hu, Chenming Wu, Jinbo Wu, Xing Liu, Chen Zhao, Errui Ding, and Zhouhui Lian. 2025. Texgaussian: Generating high-quality pbr material via octree-based 3d gaussian splatting. InProceedings of the Computer Vision and Pattern Recognition Conference...

    2025 doi

  61. [69]

    Qian Yang, Aaron Steinfeld, Carolyn Rosé, and John Zimmerman. 2020. Re-examining whether, why, and how human-AI interaction is uniquely difficult to design. InProceedings of the 2020 chi conference on human factors in computing systems. 1–13. doi:10.1145/3313831.3376301

    2020 doi

  62. [70]

    Xingrui Yang, Hai Li, Hongjia Zhai, Yuhang Ming, Yuqian Liu, and Guofeng Zhang. 2022. Vox-fusion: Dense tracking and mapping with voxel-based neural implicit representation. In2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, 499–507. doi:10.1109/...

    2022 doi

  63. [71]

    Zhihao Yao, Xiwen Yao, Haowei Xiong, Yuan-Ling Feng, Qirui Sun, Yijie Guo, and Haipeng Mi. 2026. GestuProp: 3D Virtual Reality Prop Generation with Co-Speech Gestures. InProceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26). Association for Com...

    2026 doi

  64. [72]

    Lei Zhang, Jin Pan, Jacob Gettig, Steve Oney, and Anhong Guo. 2024. VRCopilot: Authoring 3D Layouts with Generative AI Models in VR. In Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology(Pittsburgh, PA, USA)(UIST ’24). Association for Comput...

    2024 doi

  65. [73]

    Yiqin Zhao, Mallesham Dasari, and Tian Guo. 2025. CleAR: Robust Context-Guided Generative Lighting Estimation for Mobile Augmented Reality. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.9, 3, Article 154 (Sept. 2025), 26 pages. doi:10.1145/3749535 CoGen3D: An Agentic Hu...

    2025 doi

  66. [74]

    Zibo Zhao, Zeqiang Chen, Qingxu Yang, et al. 2025. Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation. arXiv preprint arXiv:2501.12202(2025)

    2025 arXiv

  67. [75]

    Jiayi Zhou, Renzhong Li, Junxiu Tang, Tan Tang, Haotian Li, Weiwei Cui, and Yingcai Wu. 2024. Understanding nonlinear collaboration between human and AI agents: A co-design framework for creative design. InProceedings of the 2024 CHI conference on human factors in computing sy...

    2024 doi

Pith tools

Reviewed July 12, 2026 · model on record in the stance chip above.