Lighting hair from the inside

Herrera, Tomas Lay; Zinke, Arno; Weber, Andreas

doi:10.1145/2366145.2366165

Cited by 42 publications

(3 citation statements)

References 17 publications

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“…Existing methods typically rely on dense vector fields extracted from images combined with photometric and geometric constraints, often followed by a bottom up hair growing strategy [PBS04; PCK*08; LLP*12; LLR13; WOQS05; NWKS19]. To enhance reconstruction fidelity, researchers leveraged various sensor modalities including depth‐of‐focus devices [JMM09], thermal imaging [LWZ12], RGB‐D cameras [HML*14] and Computed Tomography [SSW*23]. Nam et al .…”

Section: Related Workmentioning

confidence: 99%

3D Reconstruction and Semantic Modeling of Eyelashes

Kerbiriou,

Avril,

Marchal

2024

Computer Graphics Forum

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High‐fidelity digital human modeling has become crucial in various applications, including gaming, visual effects and virtual reality. Despite the significant impact of eyelashes on facial aesthetics, their reconstruction and modeling have been largely unexplored. In this paper, we introduce the first data‐driven generative model of eyelashes based on semantic features. This model is derived from real data by introducing a new 3D eyelash reconstruction method based on multi‐view images. The reconstructed data is made available which constitutes the first dataset of 3D eyelashes ever published. Through an innovative extraction process, we determine the features of any set of eyelashes, and present detailed descriptive statistics of human eyelashes shapes. The proposed eyelashes model, which exclusively relies on semantic parameters, effectively captures the appearance of a set of eyelashes. Results show that the proposed model enables interactive, intuitive and realistic eyelashes modeling for non‐experts, enriching avatar creation and synthetic data generation pipelines.

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Section: Related Workmentioning

confidence: 99%

3D Reconstruction and Semantic Modeling of Eyelashes

Kerbiriou,

Avril,

Marchal

2024

Computer Graphics Forum

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“…Building upon their research, subsequent work has steadily improved the quality of hair reconstruction [WOQS05, PCK*08, LLP*12, LZZR13,LLR13,HMLL14]. In addition, some research uses a special capture approach or setup for hair reconstruction such as focal stack photography [JMM09], thermal camera [HZW12], and RGB‐D camera [ZWW*18]. These unique setups can acquire additional physical cues to help reconstruct hair.…”

Section: Related Workmentioning

confidence: 99%

Refinement of Hair Geometry by Strand Integration

Maeda,

Takayama,

Taketomi

2023

Computer Graphics Forum

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Reconstructing 3D hair is challenging due to its complex micro‐scale geometry, and is of essential importance for the efficient creation of high‐fidelity virtual humans. Existing hair capture methods based on multi‐view stereo tend to generate results that are noisy and inaccurate. In this study, we propose a refinement method for hair geometry by incorporating the gradient of strands into the computation of their position. We formulate a gradient integration strategy for hair strands. We evaluate the performance of our method using a synthetic multi‐view dataset containing four hairstyles, and show that our refinement produces more accurate hair geometry. Furthermore, we tested our method with a real image input. Our method produces a plausible result. Our source code is publicly available at https://github.com/elerac/strand_integration.

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“…Reference 13 assembled hair fiber by fiber by recovering hair depth from defocused images. A thermal camera was used in Reference 14 to segment hair between skin and background. References 10,15‐17 leveraged specialized capture setups with multiple synchronized cameras to get high‐quality input images.…”

Section: Related Workmentioning

confidence: 99%

Struct2Hair: A hair shape descriptor for hairstyle modeling

Zhang

Nie

Guo

et al. 2022

Computer Animation & Virtual

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In recent years, it becomes possible to extract hair information for hair reconstruction from multiple cameras or monocular camera. Using a single image as the input avoids the high cost setups and complex calibration compared to multiviewed reconstruction. Taking advantage of an extendible hairstyle database, this paper introduced Struct2Hair, a novel single‐viewed hair modelling approach by extracting hair shape descriptor (HSD). The HSD is defined as the fundamental structure‐aware feature, which is a combination of critical shapes in a hairstyle. A complete dataset of critical hair shapes is constructed from a known database of three‐dimensional (3D) hair models. We first analyze the input two‐dimensional (2D) image to extract the orientation information and 2D hair sketch automatically. The extracted information is then used to retrieve the corresponding critical shapes with optimization to build the robust HSD. Finally, the HSD constructs a weighted 3D hair orientation field to guide full‐head hair model generation. Our method can preserve local geometric features of hair and retain the whole shape of the hairstyle globally owing to the HSD, which will benefit further hair editing and stylization.

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Lighting hair from the inside

Cited by 42 publications

References 17 publications

3D Reconstruction and Semantic Modeling of Eyelashes

3D Reconstruction and Semantic Modeling of Eyelashes

Refinement of Hair Geometry by Strand Integration

Struct2Hair: A hair shape descriptor for hairstyle modeling

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