• Title/Summary/Keyword: pixel-unit make up

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Interactive Pixel-unit AR Lip Makeup System Using RGB Camera

  • Nam, Hyeongil;Lee, Jeongeun;Park, Jong-Il
    • Journal of Broadcast Engineering
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    • v.25 no.7
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    • pp.1042-1051
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    • 2020
  • In this paper, we propose an AR (Augmented Reality) lip makeup using bare hands interactively using an RGB camera. Unlike previous interactive makeup studies, this interactive lip makeup system is based on an RGB camera. Also, the system controls the makeup area in pixels, not in polygon-units. For pixel-unit controlling, the system also proposed a 'Rendering Map' that can store the relative position of the touched hand relative to the lip landmarks. With the map, the part to be changed in color can be specified in the current frame. And the lip color of the corresponding area is adjusted, even if the movement of the face changes in the next frame. Through user experiments, we compare quantitatively and qualitatively our makeup method with the conventional polygon-unit method. Experimental results demonstrate that the proposed method enhances the quality of makeup with a little sacrifice of computational complexity. It is confirmed that natural makeup similar to the actual lip makeup is possible by dividing the lip area into more detailed areas. Furthermore, the method can be applied to make the face makeup of other areas more realistic.

Hologram Generation Acceleration Method Using GPGPU (GPGPU를 이용한 홀로그램 생성 가속화 방법)

  • Lee, Yoon-Hyuk;Kim, Dong-Wook;Seo, Young-Ho
    • Journal of Broadcast Engineering
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    • v.22 no.6
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    • pp.800-807
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    • 2017
  • A large amount of computation is required to generate a hologram using a computer. In order to accelerate the computation, many methods of acceleration by parallel programming using GPGPU(General Purpose computing on Graphic Process Unit) have been researched. In this paper, we propose a method of reducing the bottleneck caused by hologram pixel based parallel processing and using the shareable variables. We also propose how to optimize using Visual Profiler supported by nVidia's CUDA to make threads work optimally. The experimental results show that the proposed method reduces the calculation time by up to 40% compared with the existing research.