Pith. sign in

REVIEW 3 major objections 4 minor 57 references

Digital Salon: An AI and Physics-Driven Tool for 3D Hair Grooming and Simulation

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Digital Salon turns natural-language prompts into 3D hairstyles that can be simulated, groomed, and AI-rendered on consumer hardware.

desk verdict Integration is real and useful; the user-study evidence for the headline efficiency claim is thinner than the paper implies. read the letter →

arxiv 2507.07387 v1 pith:4CYNJKNY submitted 2025-07-10 cs.GR cs.HC

Chengan He , Jorge Alejandro Amador Herrera , Zhixin Shu , Xin Sun , Yao Feng , Sören Pirk , Dominik L. Michels , Meng Zhang
show 4 more authors
Tuanfeng Y. Wang Julie Dorsey Holly Rushmeier Yi Zhou
This is my paper
classification cs.GRcs.HC
keywords HairModelingSimulationInteractiveSystemText-to-VisualAIAgentAI-generatedContentReal-TimeNaturalLanguageRetrieval
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

Digital Salon aims to make 3D hairstyle prototyping as fast as typing a sentence. The system combines four stages into one workflow: text-guided retrieval from a curated database of 1,320 artist-groomed hairstyles, real-time strand-level simulation, interactive grooming and trimming, and text-conditioned photorealistic image generation. The authors argue that this removes the steep learning curve and hours-long manual effort of professional hair modeling tools, and they support that argument with user studies in which seven participants completed original hairstyles in less than ten minutes each. A reader should care because the system points toward a future where hairstyle ideas can be tested and communicated in 3D before any scissors are involved.

What carries the argument

The load-bearing mechanism is a four-stage pipeline coordinated by a chat-based copilot named 'Tony Sensei.' Retrieval uses CLIP text embeddings computed against captions produced by InternVL 2.0 for each of the 1,320 database hairstyles; simulation uses the Augmented Mass-Spring Model (AMS), a strand-level mass-spring discretization with augmented one-way coupling springs and a hybrid Eulerian/Lagrangian solver that handles hair-hair interactions; refinement uses procedural strand growth and simulation-driven grooming in which user edits apply spring forces and trimming removes particles and their connecting springs; rendering uses ControlNet with Canny edge maps on a Stable Diffusion 1.5 backbone. AMS is what makes strand-level dynamics interactive on consumer hardware, while the CLIP retrieval and ControlNet stages are what let non-experts drive the system with natural language.

What would settle it

A controlled within-subjects user study in which the same participants model the same target hairstyles in Digital Salon and in a conventional strand-modeling tool, with completion time, required assistance, and output quality all recorded, would settle whether the rapid-prototyping claim holds; the current study does not include such a baseline.

Watch

Extended reading notes

Core claim

The paper's central claim is that a single integrated system can cover the full hair-authoring loop—generation, simulation, refinement, and rendering—in real time on a consumer desktop. Text prompts are encoded with CLIP and matched against captions of the database hairstyles, returning three candidates for the user to inspect and refine. The Augmented Mass-Spring Model (AMS) simulates thousands of interacting strands at over 50 frames per second, grooming tools add or remove strands with physically consistent spring-force responses, and ControlNet turns the current 3D view into a photorealistic image conditioned on the hairstyle. The user study is the evidence for the headline efficiency claim: seven in-person participants designed complete hairstyles in 3 to 10 minutes, ratings for intuitiveness and enjoyment were positive, and a professional stylist interviewed remotely saw practical value for salon consultations. The paper frames the system as a step beyond prior work that addresses only one stage or requires expensive training or offline computation.

Load-bearing premise

The efficiency claim rests on comparing study participants' completion times (3 to 10 minutes) with industry-reported hours for professional tools, because the user study had no control condition using those tools; if that cross-study comparison is invalid, the outperformance claim lacks direct evidence.

Editorial extensions

If this is right

  • Hairstyles that typically take professional artists 6 to 56 hours to model can be prototyped in minutes, making iteration cheap enough for everyday creative exploration.
  • People without 3D modeling training can retrieve, adjust, and preview hairstyles using text prompts plus simple brush and trim gestures.
  • Physics-based simulation at over 50 frames per second lets users see how a style moves under wind or head motion before committing to it.
  • AI-rendered previews add clothing, background, and lighting context, so a hairstyle can be judged in a near-final image rather than on a bare head mesh.
  • Salon consultations become plausible as a 3D, physics-aware communication tool, giving clients a more concrete preview than a static magazine photo.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, retrieval precision is the likely first bottleneck in practice, because it inherits whatever CLIP and the captioner miss about hair-specific attributes; styles underrepresented in the 1,320-style database will fall back on manual grooming.
  • Beyond the paper, the 22-second image generation could become interactive with the engineering optimizations the authors name (persistent model loading and asynchronous queuing), which would close the main remaining latency gap.
  • Beyond the paper, replacing the default head template with the user's own face, as study participants requested, would likely change how suitable a style looks, since face shape strongly influences which cuts appear balanced.
  • Beyond the paper, the exposed physics parameters could be calibrated against real measured hair properties, turning the simulator from a visualization aid into a predictive tool for how a given cut will behave on a specific client's hair.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper presents Digital Salon, a 3D hair authoring system that combines text-guided retrieval from a curated 1,320-style database, an AMS-based real-time strand-level simulator, interactive grooming tools (procedural strand growth and simulation-driven trimming), and ControlNet-based image generation. The authors report runtime benchmarks on consumer hardware, a seven-participant in-person user study, and a remote professional stylist interview, and they conclude that the system can outperform traditional hair modeling workflows for rapid prototyping.

Significance. If its claims are taken at face value, the paper's main contribution is an end-to-end integration: a single interactive pipeline from natural-language prompt to simulated, groomable, photorealistic 3D hair, with measured real-time simulation (under 20 ms/frame in Table 1) and a relatively short retrieval latency. The formative study and the attention to diverse hair types are also valuable. The central usability claim, however, is currently supported only by informal time comparisons rather than by a controlled experiment, so the significance of the user-study results is limited until that evidence is strengthened or the claim is softened.

major comments (3)
  1. [Abstract and Section 5.3.1] The abstract's claim that "user studies show that our system can outperform traditional hair modeling workflows for rapid prototyping" is not supported by the reported evaluation. The in-person study (Section 5.2.2) has no baseline condition: participants used only Digital Salon, and the "significant improvement" statement in Section 5.3.1 rests on comparing Table 2 completion times (3 to 10 minutes) with uncited industry hours from Section 1 (6 to 56 hours). Because the participants were novices or semi-experts performing open-ended design tasks, while the cited industry numbers describe expert production grooming in XGen/Ornatrix, the comparison conflates different tasks, skill levels, and output-quality requirements. Please either add a matched baseline task with a conventional tool or remove/soften the outperformance claim.
  2. [Section 1] The industry time statistics (6–10 hours, 16–24 hours, 40–56 hours) are asserted without a citation or a described measurement protocol. Since these numbers are the quantitative anchor for the "outperform" conclusion, they need a source or a clearly explained estimation method; otherwise the comparison is anecdotal and cannot support the claim.
  3. [Section 5.3.1] The statement that the measured times are "comparable to the results reported by Xing et al. [41]" is not substantiated: Xing et al.'s system targets VR hair generation only, and no task times or conditions from that paper are reproduced side-by-side. This comparison should be removed or replaced with a direct quantitative reference, because as written it does not strengthen the external validity of the user study.
minor comments (4)
  1. [Section 5.2.1] There is a typo in "aged between26 and 40" that should read "between 26 and 40."
  2. [Section 3] The text contains grammar errors in "C3 have expertise" and "C4 have significant experience"; these should be "C3 has" and "C4 has."
  3. [Figure 11] Please clarify the figure: it appears to display individual participant ratings (with numeric labels) rather than aggregate statistics; a small table of counts or means would be more readable and would avoid over-interpretation of a seven-participant sample.
  4. [Section 5.1] The comparison to Blender ("over 40 seconds") lacks a concrete test scene, rendering settings, and output resolution; please specify the conditions or remove the comparison, since it is not reproducible as stated.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the system integrates cited components (CLIP, AMS, ControlNet) with independent runtime benchmarks; the user-study comparison to industry times is an external-validity concern, not circularity.

full rationale

No load-bearing circular step was found. Digital Salon is an integration of existing components that are cited rather than derived: CLIP-based text retrieval (Eqs. 1-2), the Augmented Mass-Spring Model [13], and ControlNet [44]. No parameter in the paper is fitted to the reported user-study outcomes, and no quantity that is called a prediction is constructed from the data it claims to predict. The overlapping-author citations ([12], [13]) support components that are separately benchmarked in Section 5.1 and are not invoked to forbid alternative models or to define the measured quantities. The claim that the system 'can outperform traditional hair modeling workflows for rapid prototyping' rests on comparing Table 2 completion times to uncited industry-hour estimates from Section 1 without a control condition in the user study (Section 5.2); that is a missing-baseline and missing-reference concern about external validity, not a circularity inside the paper's derivation chain. Therefore the circularity score is 0.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central contribution is an integration of existing models (CLIP, AMS, ControlNet) plus hand-built growth and grooming tools; no new physical or mathematical entity is introduced. The strongest claims depend on the authors' own simulator and on a small user study.

free parameters (1)
  • Procedural strand growth parameters (pΓ, pγ, pΩ, ph, pfreq) = Default: pΓ=0.2, pΩ=0.3, pfreq=1.0; pγ and ph are swept in Fig 7
    Hand-chosen values that control gravity and curl effects in Eqs (3)-(6); they are exposed in the UI and not fitted to measured data, but the plausible look of generated strands depends on them.
assumptions (4)
  • domain assumption The AMS mass-spring formulation is an adequate physical approximation for real-time hair dynamics across straight and curly hair types.
    Adopted from the authors' prior work [13]; the paper gives no independent validation of simulation accuracy, relying on this model for the physics claims in Section 4.3.
  • domain assumption CLIP embeddings of text captions are a valid measure of semantic similarity between user prompts and hairstyle descriptions.
    The retrieval pipeline (Eqs 1-2) assumes CLIP text embeddings align with hair-style language; precision is only self-reported by seven users, with no quantitative retrieval benchmark.
  • ad hoc to paper The procedural strand growth equations (Eqs 3-6) generate plausible hair shapes.
    This hand-built helical and gravity model is introduced for this system and is not validated against real hair geometry or prior generative models.
  • domain assumption Seven self-selected participants are representative of the target user population.
    The user study (Section 5.2) has 7 in-person participants plus one remote stylist; the paper generalizes to users of varying skill levels without a sampling framework.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Digital Salon: An AI and Physics-Driven Tool for 3D Hair Grooming and Simulation." pith.science (2026). https://pith.science/paper/4CYNJKNY

@misc{pith2026250707387,
  author       = {Pith},
  title        = {Pith review of: Digital Salon: An AI and Physics-Driven Tool for 3D Hair Grooming and Simulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4CYNJKNY}},
  note         = {Machine review of arXiv:2507.07387}
}
read the original abstract

We introduce Digital Salon, a comprehensive hair authoring system that supports real-time 3D hair generation, simulation, and rendering. Unlike existing methods that focus on isolated parts of 3D hair modeling and involve a heavy computation process or network training, Digital Salon offers a holistic and interactive system that lowers the technical barriers of 3D hair modeling through natural language-based interaction. The system guides users through four key stages: text-guided hair retrieval, real-time hair simulation, interactive hair refinement, and hair-conditioned image generation. This cohesive workflow makes advanced hair design accessible to users of varying skill levels and dramatically streamlines the creative process in digital media with an intuitive, versatile, and efficient solution for hair modeling. User studies show that our system can outperform traditional hair modeling workflows for rapid prototyping. Furthermore, we provide insights into the benefits of our system with future potential of deploying our system in real salon environments. More details can be found on our project page: https://digital-salon.github.io/.

Figures

Figures reproduced from arXiv: 2507.07387 by the authors.

Figure 1
Figure 1. The Digital Salon system. Digital Salon enables users to: (a) retrieve 3D hair models from a curated database using [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Authoring workflow of Digital Salon. To create a new hairstyle whether for digital content or real-world styling, [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. User interface of Digital Salon, comprising: (a) the [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Examples of complex hairstyles in our database, [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: Real-time simulation of double tails under a wind [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Parameter space exploration showing the impact [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Procedural facial hair generation using our algo [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: Simulation-driven grooming in our system. Begin [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: Hair-conditioned image generation. From top to [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]
Figure 11
Figure 11. Figure 11: Participant ratings across six evaluation metrics: [PITH_FULL_IMAGE:figures/full_fig_p009_11.png]
Figure 12
Figure 12. Figure 12: Example designs by participants in the user study. (a) P1 designed a long hairstyle with loose waves and short bangs. [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

57 extracted references · 47 canonical work pages

  1. [41]

    Jun Xing, Koki Nagano, Weikai Chen, Haotian Xu, Li-yi Wei, Yajie Zhao, Jingwan Lu, Byungmoon Kim, and Hao Li. 2019. HairBrush for Immersive Data-Driven Hair Modeling. In Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (UIST ’19) . 263–279

    work page 2019

  2. [1]

    Adobe. 2025. Firefly. https://www.adobe.com/products/firefly.html

    work page 2025

  3. [2]

    Miklós Bergou, Max Wardetzky, Stephen Robinson, Basile Audoly, and Eitan Grinspun. 2008. Discrete elastic rods. InACM SIGGRAPH 2008 Papers (SIGGRAPH ’08). Article 63, 12 pages

    work page 2008

  4. [3]

    Florence Bertails, Basile Audoly, Marie-Paule Cani, Bernard Querleux, Frédéric Leroy, and Jean-Luc Lévêque. 2006. Super-helices for predicting the dynamics of natural hair. ACM Transactions on Graphics (TOG) 25, 3 (2006), 1180–1187

    work page 2006

  5. [4]

    Gaurav Bhokare, Eisen Montalvo, Elie Diaz, and Cem Yuksel. 2024. Real-time hair rendering with hair meshes. In ACM SIGGRAPH 2024 Conference Papers . 1–10

    work page 2024

  6. [5]

    John Canny. 1986. A computational approach to edge detection.IEEE Transactions on pattern analysis and machine intelligence 6 (1986), 679–698

    work page 1986

  7. [6]

    Menglei Chai, Tianjia Shao, Hongzhi Wu, Yanlin Weng, and Kun Zhou. 2016. AutoHair: Fully Automatic Hair Modeling from a Single Image.ACM Transactions on Graphics 35, 4, Article 116 (jul 2016), 12 pages

    work page 2016

  8. [7]

    Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. 2024. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 24185–24198

    work page 2024

Show all 57 references
  1. [8]

    Matt Jen-Yuan Chiang, Benedikt Bitterli, Chuck Tappan, and Brent Burley. 2015. A practical and controllable hair and fur model for production path tracing. In ACM SIGGRAPH 2015 Talks (SIGGRAPH ’15) . Article 23, 1 pages

    2015

  2. [9]

    Gilles Daviet. 2023. Interactive Hair Simulation on the GPU using ADMM. In ACM SIGGRAPH 2023 Conference Proceedings . 1–11

    2023

  3. [10]

    Eugene d’Eon, Guillaume Francois, Martin Hill, Joe Letteri, and Jean-Marie Aubry. 2011. An energy-conserving hair reflectance model. In Proceedings of the Twenty-Second Eurographics Conference on Rendering (EGSR ’11) . 1181–1187

    2011

  4. [11]

    Yao Feng, Weiyang Liu, Timo Bolkart, Jinlong Yang, Marc Pollefeys, and Michael J Black. 2023. Learning disentangled avatars with hybrid 3d representations.arXiv preprint arXiv:2309.06441 (2023). Digital Salon: An AI and Physics-Driven Tool for 3D Hair Grooming and Simulation

    2023 arXiv

  5. [12]

    Michels, Tuanfeng Y

    Chengan He, Xin Sun, Zhixin Shu, Fujun Luan, Sören Pirk, Jorge Alejan- dro Amador Herrera, Dominik L. Michels, Tuanfeng Y. Wang, Meng Zhang, Holly Rushmeier, and Yi Zhou. 2025. Perm: A Parametric Representation for Multi-Style 3D Hair Modeling. In International Conference on L...

    2025

  6. [13]

    Jorge Alejandro Amador Herrera, Yi Zhou, Xin Sun, Zhixin Shu, Chengan He, Sören Pirk, and Dominik L Michels. 2024. Augmented Mass-Spring Model for Real-Time Dense Hair Simulation. arXiv preprint arXiv:2412.17144 (2024)

    2024

  7. [14]

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems 33 (2020), 6840–6851

    2020

  8. [15]

    Jerry Hsu, Tongtong Wang, Zherong Pan, Xifeng Gao, Cem Yuksel, and Kui Wu

  9. [16]

    Liwen Hu, Chongyang Ma, Linjie Luo, and Hao Li. 2015. Single-view hair modeling using a hairstyle database. ACM Trans. Graph. 34, 4, Article 125 (July 2015), 9 pages

    2015

  10. [17]

    Tao Huang, Yang Zhou, Daqi Lin, Junqiu Zhu, Ling-Qi Yan, and Kui Wu

  11. [18]

    Moon, and Steve Marschner

    Wenzel Jakob, Jonathan T. Moon, and Steve Marschner. 2009. Capturing hair assemblies fiber by fiber. ACM Trans. Graph. 28, 5 (dec 2009), 1–9

    2009

  12. [19]

    arXiv preprint arXiv:2405.10565 (2024)

    Real-time Level-of-Detail Strand-based Hair Rendering. arXiv preprint arXiv:2405.10565 (2024)

    2024 arXiv

  13. [20]

    James T Kajiya and Timothy L Kay. 1989. Rendering fur with three dimensional textures. ACM Siggraph Computer Graphics 23, 3 (1989), 271–280

    1989

  14. [21]

    Jianwei Jiang, Bin Sheng, Ping Li, Lizhuang Ma, Xin Tong, and Enhua Wu. 2020. Real-time hair simulation with heptadiagonal decomposition on mass spring system. Graphical Models 111 (2020), 101077

    2020

  15. [22]

    Qing Lyu, Menglei Chai, Xiang Chen, and Kun Zhou. 2020. Real-time hair simulation with neural interpolation. IEEE Transactions on Visualization and Computer Graphics 28, 4 (2020), 1894–1905

    2020

  16. [23]

    Haimin Luo, Min Ouyang, Zijun Zhao, Suyi Jiang, Longwen Zhang, Qixuan Zhang, Wei Yang, Lan Xu, and Jingyi Yu. 2024. GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians. arXiv preprint arXiv:2402.10483 (2024)

    2024 arXiv

  17. [24]

    Michels, Vu Thai Luan, and Mayya Tokman

    Dominik L. Michels, Vu Thai Luan, and Mayya Tokman. 2017. A stiffly accurate integrator for elastodynamic problems. ACM Trans. Graph. 36, 4, Article 116 (jul 2017), 14 pages

    2017

  18. [25]

    Marschner, Henrik Wann Jensen, Mike Cammarano, Steve Worley, and Pat Hanrahan

    Stephen R. Marschner, Henrik Wann Jensen, Mike Cammarano, Steve Worley, and Pat Hanrahan. 2003. Light scattering from human hair fibers. ACM Trans. Graph. 22, 3 (July 2003), 780–791

    2003

  19. [26]

    OpenAI. 2024. GPT-4o Technical Report. https://openai.com/index/gpt-4o

    2024

  20. [27]

    Dominik L Michels, J Paul T Mueller, and Gerrit A Sobottka. 2015. A physically based approach to the accurate simulation of stiff fibers and stiff fiber meshes. Computers & Graphics 53 (2015), 136–146

    2015

  21. [28]

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sand- hini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al

  22. [29]

    Briceño, and François X

    Sylvain Paris, Hector M. Briceño, and François X. Sillion. 2004. Capture of hair geometry from multiple images. ACM Trans. Graph. 23, 3 (aug 2004), 712–719

    2004

  23. [30]

    Robert E Rosenblum, Wayne E Carlson, and Edwin Tripp III. 1991. Simulating the structure and dynamics of human hair: modelling, rendering and animation. The Journal of Visualization and Computer Animation 2, 4 (1991), 141–148

    1991

  24. [31]

    Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2023. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 22500–22510

    2023

  25. [32]

    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 . 10684–10695

    2022

  26. [33]

    Shunsuke Saito, Liwen Hu, Chongyang Ma, Hikaru Ibayashi, Linjie Luo, and Hao Li. 2018. 3D Hair Synthesis Using Volumetric Variational Autoencoders. ACM Trans. Graph. 37, 6, Article 208 (dec 2018), 12 pages

    2018

  27. [34]

    Andrew Selle, Michael Lentine, and Ronald Fedkiw. 2008. A mass spring model for hair simulation. ACM Trans. Graph. 27, 3 (2008), 1–11

    2008

  28. [35]

    Iman Sadeghi, Heather Pritchett, Henrik Wann Jensen, and Rasmus Tamstorf

  29. [36]

    Black, and Justus Thies

    Vanessa Sklyarova, Egor Zakharov, Otmar Hilliges, Michael J. Black, and Justus Thies. 2024. Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 4703–4712

    2024

  30. [37]

    Ziyan Wang, Giljoo Nam, Tuur Stuyck, Stephen Lombardi, Michael Zollhöfer, Jessica Hodgins, and Christoph Lassner. 2022. HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture. In Proceed- ings of the IEEE/CVF Conference on Computer Vision ...

    2022

  31. [38]

    Kelly Ward, Florence Bertails, Tae-Yong Kim, Stephen R Marschner, Marie-Paule Cani, and Ming C Lin. 2007. A survey on hair modeling: Styling, simulation, and rendering. IEEE transactions on visualization and computer graphics 13, 2 (2007), 213–234

    2007

  32. [39]

    Yuefan Shen, Shunsuke Saito, Ziyan Wang, Olivier Maury, Chenglei Wu, Jessica Hodgins, Youyi Zheng, and Giljoo Nam. 2023. CT2Hair: High-Fidelity 3D Hair Modeling using Computed Tomography. ACM Transactions on Graphics 42, 4 (2023), 1–13

    2023

  33. [40]

    Mengqi Xia, Bruce Walter, Christophe Hery, Olivier Maury, Eric Michielssen, and Steve Marschner. 2023. A Practical Wave Optics Reflection Model for Hair and Fur. ACM Trans. Graph. 42, 4, Article 39 (2023), 15 pages

    2023

  34. [42]

    Zexiang Xu, Hsiang-Tao Wu, Lvdi Wang, Changxi Zheng, Xin Tong, and Yue Qi

  35. [43]

    Keyu Wu, Yifan Ye, Lingchen Yang, Hongbo Fu, Kun Zhou, and Youyi Zheng

  36. [44]

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. 2023. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF inter- national conference on computer vision . 3836–3847

    2023

  37. [45]

    Meng Zhang, Menglei Chai, Hongzhi Wu, Hao Yang, and Kun Zhou. 2017. A data-driven approach to four-view image-based hair modeling. ACM Trans. Graph. 36, 4 (2017), 156–1

    2017

  38. [46]

    Meng Zhang and Youyi Zheng. 2019. Hair-GAN: Recovering 3D hair structure from a single image using generative adversarial networks. Visual Informatics 3, 2 (2019), 102–112

    2019

  39. [47]

    Yujian Zheng, Zirong Jin, Moran Li, Haibin Huang, Chongyang Ma, Shuguang Cui, and Xiaoguang Han. 2023. HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re...

    2023

  40. [48]

    Yuxiao Zhou, Menglei Chai, Alessandro Pepe, Markus Gross, and Thabo Beeler

  41. [49]

    Egor Zakharov, Vanessa Sklyarova, Michael J Black, Giljoo Nam, Justus Thies, and Otmar Hilliges. 2024. Human Hair Reconstruction with Strand-Aligned 3D Gaussians. In European Conference of Computer Vision (ECCV)

    2024

  42. [50]

    Arno Zinke, Martin Rump, Tomás Lay, Andreas Weber, Anton Andriyenko, and Reinhard Klein. 2009. A practical approach for photometric acquisition of hair color. ACM Trans. Graph. 28, 5 (2009), 1–9

    2009

  43. [56]

    Yi Zhou, Liwen Hu, Jun Xing, Weikai Chen, Han-Wei Kung, Xin Tong, and Hao Li. 2018. Hairnet: Single-view hair reconstruction using convolutional neural networks. In Proceedings of the European Conference on Computer Vision (ECCV) . 235–251

    2018

  44. [2010]

    ACM Trans

    An artist friendly hair shading system. ACM Trans. Graph. 29, 4, Article 56 (2010), 10 pages

    2010

  45. [2014]

    ACM Transactions on Graphics (TOG) 33, 6, Article 224 (nov 2014), 11 pages

    Dynamic hair capture using spacetime optimization. ACM Transactions on Graphics (TOG) 33, 6, Article 224 (nov 2014), 11 pages

    2014

  46. [2021]

    In International conference on machine learning

    Learning transferable visual models from natural language supervision. In International conference on machine learning . 8748–8763

  47. [2022]

    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    NeuralHDHair: Automatic High-fidelity Hair Modeling from a Single Image Using Implicit Neural Representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1526–1535

  48. [2023]

    ACM Trans

    GroomGen: A High-Quality Generative Hair Model Using Hierarchical La- tent Representations. ACM Trans. Graph. 42, 6, Article 270 (Dec. 2023), 16 pages

    2023

  49. [2024]

    ACM Trans

    Real-time Physically Guided Hair Interpolation. ACM Trans. Graph. 43, 4, Article 95 (July 2024), 11 pages

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.