Test of Kamar rendering human head skin

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So I am wondering if I can find a fast and good sss rendering techniques in Karma.
Previous experience in offline rendering with CPU educate us that if you enable SSS feature, your render time will go sky rocket..

I downloaded a free head texture and model set and made a very simple shader/renderer setting comparison


This one is using the principle shader and the random walk SSS using CPU renderer
I think it uses about 2 min
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This one is using the skin shader core and CPU renderer
This cost 3.7 min.
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This one is using the materialX sss and XPU render
This cost only 20 seconds.

I feel that in a CPU rendering pipeline, maybe using random walk SSS is more cost efficient than using a skin shader core. It takes less time but produce a good looking result.
What I cannot judge is the XPU render using materialX sss. The parameter tweaking of is troublesome, I sense a strong non-linearity
when tweaking the sss parameters, a little value more will completely make the character looks like a big jade or has just born. Luckily XPU rendering is fast so we can see the result quickly and iterate on it.

My current suggestion is that principle shader is best for doing characters, or if you are very confident at tweaking materials, then you should try materialX SSS and XPU render.

But I am still wondering if there might be better options. I know that unreal engine uses something called subsurface profile and can achieve a good result without using textures. Unreal also uses something called burley subsurface model, and then it is combined with textures it will produce good result in real-time.

What's your thought on this field?


PS: I've just used basecolor, normal and spec texture and I haven't done any other combinations yet. I've also made a huge downgrade to the textures (from 8K to 2K)


The project file
Image Not Found
Edited by goose7 - Jan. 19, 2023 09:02:46

Attachments:
principle_randomwwalk_cpu.jpg (582.3 KB)
skin_Shader_core_cpu.jpg (309.5 KB)
xpu_SSS.jpg (575.4 KB)
KarmaHead.zip (14.2 MB)

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