Who doesn’t like gaming? The more natural and fashioned the characters in the sport, the more we enjoy it. Is it possible to have graphics that look exactly like natural hair?

Other than 3D hair authoring tools, the manual creation by artists is each time-consuming and difficult to scale and can be biased by the restrictions of current 3D authoring tools. Making a large dataset that accurately represents a wide selection of real-world hair variations like curly, silky, straight, and wavy is an enormous challenge. Researchers at State Key Labs and Meta Reality Labs succeeded in reconstructing various hairstyle graphics from real-world hair wigs as input. 

Researchers created density volumes of hair regions, which allows them to see through the hair, unlike the image-based approaches of visible surfaces. The tactic implemented to create density volumes was computed tomography (CT). They employed CT using X-rays for top resolution and enormous scan volumes. CT X-rays are often used to reconstruct human tissues or general objects. As a result of the skinny structure of the hair strand, recovering an entire human hair strand from CT is a non-trivial task. This may inherit noise in CT imaging and reduce the resolution. To handle this issue, they follow a coarse-to-fine approach.

They first estimate a 3D orientation field from a loud density volume ( an actual hair wig ) and extract useful guide strands using the estimated orientation field. They then populate the scalp with strands using a neural interpolation method and at last refine it with optimization such that they accurately conform to the input density volume. The optimization step involves higher aligning the reconstructed hair strands with the input volume. Their work doesn’t include hand-crafted priors for particular hair types in order that they will get well diverse hairstyles in a single framework.

Researchers compared their methods with the opposite three image-based methods, that are single-view-based, sparse-view-based and dense-view-based. They found that single-view-based and sparse-view-based methods produced reasonable results for relatively easy hairstyles but failed hugely in curly hair as a consequence of a scarcity of coaching datasets. The dense-view-based process surpassed those two methods but failed in inferring interior geometry and, because of this, produced incomplete geometry. In contrast, the researcher’s model showed good geometry and contained more details, which made them look realistic. 

Nonetheless, extending this ideology to capture real human heads stays difficult. Industry CT scanners use large exposure of X-rays that exceeds the security limit for living organisms, so modeling the face’s geometry using this shouldn’t be feasible. Researchers say that even a subtle motion throughout the capture will result in substantial blurriness within the density volume. 

By implementing machine learning approaches, future work may generate a big corpus of high-quality 3D hair data, enabling them to infer 3D hair models even from low-resolution density volumes using medical CT scanners. 

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