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A Study on the Perceived Realism Of Strand-based Hair Simulated By Style : Evaluating Real-time Hair-simulation in Unreal Engine 4

Authors: Larsson-Ståhl, Jennifer;

A Study on the Perceived Realism Of Strand-based Hair Simulated By Style : Evaluating Real-time Hair-simulation in Unreal Engine 4

Abstract

Background. In order to increase the visual fidelity of characters in video games, strand-based hair rendering within Unreal Engine 4 is explored in this thesis. Because of the complex nature of hair, explicit hair models can be very costly to render and simulate. It is theorized that utilizing customized hair and simulation settings, tailored to specific types of hairstyles could alleviate this issue while preserving visual fidelity. Objectives. The aim of this thesis is to provide an insight into what can be done to increase visual appeal and computer performance of physical simulation for different types of hairstyles and determine if customized settings may cause a significant impact on the perceived level of realism. The objectives of this thesis are to acquire a set of different strand-based hairstyles, determine a set of customized hair and simulation settings which can be applied to them, create a test scene inside of Unreal Engine 4 and render out a set of images and videos to be used in a user experiment, measure the performance of each customized setting and finally synthesize the acquired data. Methods. In order to achieve the aims and objectives of this thesis, a user experiment that utilizes the 2AFC method to let participants compare image- and video-pairs is performed as well as a performance experiment using the built-in profiling tools in Unreal Engine 4. In addition, a pilot experiment was performed in order to ascertain that the experiments would be feasible on the available hardware. Results. The results show that there was a significant difference in the perceived level of realism when different simulation settings was applied to the hairstyles, with customized settings being preferred to the default setting. The voting results on the image-pairs showed a preference for fine hair strands while the strand count did not have as much of an impact. It was shown that participants could easier distinguish between the different simulation- and hair-settings in long hair compared to short hair. The performance experiment showed that the amount of hair strands had the biggest impact on computer performance. Conclusions. Customizing hair and simulation settings to different types of hairstyles could provide a heightened perceived level of realism and a limited performance boost, from what could be derived from these experiments. Lowering the hair strand count was determined to be the most effective method of increasing performance. The performance of strand-based hair is currently not quite reaching a consistent 60 frames per second on the tested hardware, but with further optimizations it is believed that this could be acquired, especially on more powerful graphics cards. Future work should keep focus on increasing the stability of real-time strand-based hair simulation.

Country
Sweden
Related Organizations
Keywords

unreal engine 4, computer graphics, photo-realism, physical simulation, real-time hair rendering, Other Computer and Information Science, Annan data- och informationsvetenskap

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
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