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using System;
namespace UnityEngine.Experimental.Rendering.HDPipeline
{
[GenerateHLSL]
public class SssConstants
{
public const int SSS_N_PROFILES = 8; // Max. number of profiles, including the slot taken by the neutral profile
public const int SSS_NEUTRAL_PROFILE_ID = SSS_N_PROFILES - 1; // Does not result in blurring
public const int SSS_N_SAMPLES_NEAR_FIELD = 55; // Used for extreme close ups; must be a Fibonacci number
public const int SSS_N_SAMPLES_FAR_FIELD = 21; // Used at a regular distance; must be a Fibonacci number
public const int SSS_LOD_THRESHOLD = 4; // The LoD threshold of the near-field kernel (in pixels)
public const int SSS_TRSM_MODE_NONE = 0;
public const int SSS_TRSM_MODE_THIN = 1;
// Old SSS Model >>>
public const int SSS_BASIC_N_SAMPLES = 11; // Must be an odd number
public const int SSS_BASIC_DISTANCE_SCALE = 3; // SSS distance units per centimeter
// <<< Old SSS Model
}
[Serializable]
public class SubsurfaceScatteringProfile : ScriptableObject
{
public enum TexturingMode : uint { PreAndPostScatter = 0, PostScatter = 1 };
public enum TransmissionMode : uint { None = SssConstants.SSS_TRSM_MODE_NONE, ThinObject = SssConstants.SSS_TRSM_MODE_THIN, Regular };
[ColorUsage(false, true, 0f, 8f, 0.125f, 3f)]
public Color scatteringDistance; // Per color channel (no meaningful units)
[ColorUsage(false)]
public Color transmissionTint; // Color, 0 to 1
public TexturingMode texturingMode;
public TransmissionMode transmissionMode;
public Vector2 thicknessRemap; // X = min, Y = max (in millimeters)
public float worldScale; // Size of the world unit in meters
[HideInInspector]
public int settingsIndex; // SubsurfaceScatteringSettings.profiles[i]
[SerializeField]
Vector3 m_ShapeParam; // RGB = shape parameter: S = 1 / D
[SerializeField]
float m_MaxRadius; // In millimeters
[SerializeField]
Vector2[] m_FilterKernelNearField; // X = radius, Y = reciprocal of the PDF
[SerializeField]
Vector2[] m_FilterKernelFarField; // X = radius, Y = reciprocal of the PDF
// Old SSS Model >>>
[ColorUsage(false, true, 0f, 8f, 0.125f, 3f)]
public Color scatterDistance1;
[ColorUsage(false, true, 0f, 8f, 0.125f, 3f)]
public Color scatterDistance2;
[Range(0f, 1f)]
public float lerpWeight;
[SerializeField]
Vector4 m_HalfRcpWeightedVariances;
[SerializeField]
Vector4[] m_FilterKernelBasic;
// <<< Old SSS Model
// --- Public Methods ---
public SubsurfaceScatteringProfile()
{
scatteringDistance = Color.grey;
transmissionTint = Color.white;
texturingMode = TexturingMode.PreAndPostScatter;
transmissionMode = TransmissionMode.None;
thicknessRemap = new Vector2(0.0f, 5.0f);
worldScale = 1.0f;
settingsIndex = SssConstants.SSS_NEUTRAL_PROFILE_ID; // Updated by SubsurfaceScatteringSettings.OnValidate() once assigned
// Old SSS Model >>>
scatterDistance1 = new Color(0.3f, 0.3f, 0.3f, 0.0f);
scatterDistance2 = new Color(0.5f, 0.5f, 0.5f, 0.0f);
lerpWeight = 1.0f;
// <<< Old SSS Model
BuildKernel();
}
public void Validate()
{
thicknessRemap.y = Mathf.Max(thicknessRemap.y, 0f);
thicknessRemap.x = Mathf.Clamp(thicknessRemap.x, 0f, thicknessRemap.y);
worldScale = Mathf.Max(worldScale, 0.001f);
// Old SSS Model >>>
var c = new Color();
c.r = Mathf.Max(0.05f, scatterDistance1.r);
c.g = Mathf.Max(0.05f, scatterDistance1.g);
c.b = Mathf.Max(0.05f, scatterDistance1.b);
c.a = 0.0f;
scatterDistance1 = c;
c.r = Mathf.Max(0.05f, scatterDistance2.r);
c.g = Mathf.Max(0.05f, scatterDistance2.g);
c.b = Mathf.Max(0.05f, scatterDistance2.b);
c.a = 0.0f;
scatterDistance2 = c;
// <<< Old SSS Model
BuildKernel();
}
// Ref: Approximate Reflectance Profiles for Efficient Subsurface Scattering by Pixar.
public void BuildKernel()
{
if (m_FilterKernelNearField == null || m_FilterKernelNearField.Length != SssConstants.SSS_N_SAMPLES_NEAR_FIELD)
{
m_FilterKernelNearField = new Vector2[SssConstants.SSS_N_SAMPLES_NEAR_FIELD];
}
if (m_FilterKernelFarField == null || m_FilterKernelFarField.Length != SssConstants.SSS_N_SAMPLES_FAR_FIELD)
{
m_FilterKernelFarField = new Vector2[SssConstants.SSS_N_SAMPLES_FAR_FIELD];
}
// Clamp to avoid artifacts.
m_ShapeParam.x = 1.0f / Mathf.Max(0.001f, scatteringDistance.r);
m_ShapeParam.y = 1.0f / Mathf.Max(0.001f, scatteringDistance.g);
m_ShapeParam.z = 1.0f / Mathf.Max(0.001f, scatteringDistance.b);
// We importance sample the color channel with the widest scattering distance.
float s = Mathf.Min(m_ShapeParam.x, m_ShapeParam.y, m_ShapeParam.z);
// Importance sample the normalized diffusion profile for the computed value of 's'.
// ------------------------------------------------------------------------------------
// R(r, s) = s * (Exp[-r * s] + Exp[-r * s / 3]) / (8 * Pi * r)
// PDF(r, s) = s * (Exp[-r * s] + Exp[-r * s / 3]) / 4
// CDF(r, s) = 1 - 1/4 * Exp[-r * s] - 3/4 * Exp[-r * s / 3]
// ------------------------------------------------------------------------------------
// Importance sample the near field kernel.
for (int i = 0, n = SssConstants.SSS_N_SAMPLES_NEAR_FIELD; i < n; i++)
{
float p = (i + 0.5f) * (1.0f / n);
float r = KernelCdfInverse(p, s);
// N.b.: computation of normalized weights, and multiplication by the surface albedo
// of the actual geometry is performed at runtime (in the shader).
m_FilterKernelNearField[i].x = r;
m_FilterKernelNearField[i].y = 1.0f / KernelPdf(r, s);
}
// Importance sample the far field kernel.
for (int i = 0, n = SssConstants.SSS_N_SAMPLES_FAR_FIELD; i < n; i++)
{
float p = (i + 0.5f) * (1.0f / n);
float r = KernelCdfInverse(p, s);
// N.b.: computation of normalized weights, and multiplication by the surface albedo
// of the actual geometry is performed at runtime (in the shader).
m_FilterKernelFarField[i].x = r;
m_FilterKernelFarField[i].y = 1.0f / KernelPdf(r, s);
}
m_MaxRadius = m_FilterKernelFarField[SssConstants.SSS_N_SAMPLES_FAR_FIELD - 1].x;
// Old SSS Model >>>
UpdateKernelAndVarianceData();
// <<< Old SSS Model
}
// Old SSS Model >>>
public void UpdateKernelAndVarianceData()
{
const int numSamples = SssConstants.SSS_BASIC_N_SAMPLES;
const int distanceScale = SssConstants.SSS_BASIC_DISTANCE_SCALE;
if (m_FilterKernelBasic == null || m_FilterKernelBasic.Length != numSamples)
{
m_FilterKernelBasic = new Vector4[numSamples];
}
// Apply the three-sigma rule, and rescale.
Color stdDev1 = ((1.0f / 3.0f) * distanceScale) * scatterDistance1;
Color stdDev2 = ((1.0f / 3.0f) * distanceScale) * scatterDistance2;
// Our goal is to blur the image using a filter which is represented
// as a product of a linear combination of two normalized 1D Gaussians
// as suggested by Jimenez et al. in "Separable Subsurface Scattering".
// A normalized (i.e. energy-preserving) 1D Gaussian with the mean of 0
// is defined as follows: G1(x, v) = exp(-x * x / (2 * v)) / sqrt(2 * Pi * v),
// where 'v' is variance and 'x' is the radial distance from the origin.
// Using the weight 'w', our 1D and the resulting 2D filters are given as:
// A1(v1, v2, w, x) = G1(x, v1) * (1 - w) + G1(r, v2) * w,
// A2(v1, v2, w, x, y) = A1(v1, v2, w, x) * A1(v1, v2, w, y).
// The resulting filter function is a non-Gaussian PDF.
// It is separable by design, but generally not radially symmetric.
// N.b.: our scattering distance is rather limited. Therefore, in order to allow
// for a greater range of standard deviation values for flatter profiles,
// we rescale the world using 'distanceScale', effectively reducing the SSS
// distance units from centimeters to (1 / distanceScale).
// Find the widest Gaussian across 3 color channels.
float maxStdDev1 = Mathf.Max(stdDev1.r, stdDev1.g, stdDev1.b);
float maxStdDev2 = Mathf.Max(stdDev2.r, stdDev2.g, stdDev2.b);
Vector3 weightSum = new Vector3(0, 0, 0);
float step = 1.0f / (numSamples - 1);
// Importance sample the linear combination of two Gaussians.
for (int i = 0; i < numSamples; i++)
{
// Generate 'u' on (0, 0.5] and (0.5, 1).
float u = (i <= numSamples / 2) ? 0.5f - i * step // The center and to the left
: i * step; // From the center to the right
u = Mathf.Clamp(u, 0.001f, 0.999f);
float pos = GaussianCombinationCdfInverse(u, maxStdDev1, maxStdDev2, lerpWeight);
float pdf = GaussianCombination(pos, maxStdDev1, maxStdDev2, lerpWeight);
Vector3 val;
val.x = GaussianCombination(pos, stdDev1.r, stdDev2.r, lerpWeight);
val.y = GaussianCombination(pos, stdDev1.g, stdDev2.g, lerpWeight);
val.z = GaussianCombination(pos, stdDev1.b, stdDev2.b, lerpWeight);
// We do not divide by 'numSamples' since we will renormalize, anyway.
m_FilterKernelBasic[i].x = val.x * (1 / pdf);
m_FilterKernelBasic[i].y = val.y * (1 / pdf);
m_FilterKernelBasic[i].z = val.z * (1 / pdf);
m_FilterKernelBasic[i].w = pos;
weightSum.x += m_FilterKernelBasic[i].x;
weightSum.y += m_FilterKernelBasic[i].y;
weightSum.z += m_FilterKernelBasic[i].z;
}
// Renormalize the weights to conserve energy.
for (int i = 0; i < numSamples; i++)
{
m_FilterKernelBasic[i].x *= 1 / weightSum.x;
m_FilterKernelBasic[i].y *= 1 / weightSum.y;
m_FilterKernelBasic[i].z *= 1 / weightSum.z;
}
Vector4 weightedStdDev;
weightedStdDev.x = Mathf.Lerp(stdDev1.r, stdDev2.r, lerpWeight);
weightedStdDev.y = Mathf.Lerp(stdDev1.g, stdDev2.g, lerpWeight);
weightedStdDev.z = Mathf.Lerp(stdDev1.b, stdDev2.b, lerpWeight);
weightedStdDev.w = Mathf.Lerp(maxStdDev1, maxStdDev2, lerpWeight);
// Store (1 / (2 * WeightedVariance)) per color channel.
m_HalfRcpWeightedVariances.x = 0.5f / (weightedStdDev.x * weightedStdDev.x);
m_HalfRcpWeightedVariances.y = 0.5f / (weightedStdDev.y * weightedStdDev.y);
m_HalfRcpWeightedVariances.z = 0.5f / (weightedStdDev.z * weightedStdDev.z);
m_HalfRcpWeightedVariances.w = 0.5f / (weightedStdDev.w * weightedStdDev.w);
}
// <<< Old SSS Model
public Vector3 shapeParameter
{
// Set in BuildKernel().
get { return m_ShapeParam; }
}
public float maxRadius
{
// Set in BuildKernel().
get { return m_MaxRadius; }
}
public Vector2[] filterKernelNearField
{
// Set in BuildKernel().
get { return m_FilterKernelNearField; }
}
public Vector2[] filterKernelFarField
{
// Set in BuildKernel().
get { return m_FilterKernelFarField; }
}
// Old SSS Model >>>
public Vector4[] filterKernelBasic
{
// Set via UpdateKernelAndVarianceData().
get { return m_FilterKernelBasic; }
}
public Vector4 halfRcpWeightedVariances
{
// Set via UpdateKernelAndVarianceData().
get { return m_HalfRcpWeightedVariances; }
}
// <<< Old SSS Model
// --- Private Methods ---
static float KernelVal(float r, float s)
{
return s * (Mathf.Exp(-r * s) + Mathf.Exp(-r * s * (1.0f / 3.0f))) / (8.0f * Mathf.PI * r);
}
// Computes the value of the integrand over a disk: (2 * PI * r) * KernelVal().
static float KernelValCircle(float r, float s)
{
return 0.25f * s * (Mathf.Exp(-r * s) + Mathf.Exp(-r * s * (1.0f / 3.0f)));
}
static float KernelPdf(float r, float s)
{
return KernelValCircle(r, s);
}
static float KernelCdf(float r, float s)
{
return 1.0f - 0.25f * Mathf.Exp(-r * s) - 0.75f * Mathf.Exp(-r * s * (1.0f / 3.0f));
}
static float KernelCdfDerivative1(float r, float s)
{
return 0.25f * s * Mathf.Exp(-r * s) * (1.0f + Mathf.Exp(r * s * (2.0f / 3.0f)));
}
static float KernelCdfDerivative2(float r, float s)
{
return (-1.0f / 12.0f) * s * s * Mathf.Exp(-r * s) * (3.0f + Mathf.Exp(r * s * (2.0f / 3.0f)));
}
// The CDF is not analytically invertible, so we use Halley's Method of root finding.
// { f(r, s, p) = CDF(r, s) - p = 0 } with the initial guess { r = (10^p - 1) / s }.
static float KernelCdfInverse(float p, float s)
{
// Supply the initial guess.
float r = (Mathf.Pow(10.0f, p) - 1.0f) / s;
float t = float.MaxValue;
while (true)
{
float f0 = KernelCdf(r, s) - p;
float f1 = KernelCdfDerivative1(r, s);
float f2 = KernelCdfDerivative2(r, s);
float dr = f0 / (f1 * (1.0f - f0 * f2 / (2.0f * f1 * f1)));
if (Mathf.Abs(dr) < t)
{
r = r - dr;
t = Mathf.Abs(dr);
}
else
{
// Converged to the best result.
break;
}
}
return r;
}
// Old SSS Model >>>
static float Gaussian(float x, float stdDev)
{
float variance = stdDev * stdDev;
return Mathf.Exp(-x * x / (2 * variance)) / Mathf.Sqrt(2 * Mathf.PI * variance);
}
static float GaussianCombination(float x, float stdDev1, float stdDev2, float lerpWeight)
{
return Mathf.Lerp(Gaussian(x, stdDev1), Gaussian(x, stdDev2), lerpWeight);
}
static float RationalApproximation(float t)
{
// Abramowitz and Stegun formula 26.2.23.
// The absolute value of the error should be less than 4.5 e-4.
float[] c = {2.515517f, 0.802853f, 0.010328f};
float[] d = {1.432788f, 0.189269f, 0.001308f};
return t - ((c[2] * t + c[1]) * t + c[0]) / (((d[2] * t + d[1]) * t + d[0]) * t + 1.0f);
}
// Ref: https://www.johndcook.com/blog/csharp_phi_inverse/
static float NormalCdfInverse(float p, float stdDev)
{
float x;
if (p < 0.5)
{
// F^-1(p) = - G^-1(p)
x = -RationalApproximation(Mathf.Sqrt(-2.0f * Mathf.Log(p)));
}
else
{
// F^-1(p) = G^-1(1-p)
x = RationalApproximation(Mathf.Sqrt(-2.0f * Mathf.Log(1.0f - p)));
}
return x * stdDev;
}
static float GaussianCombinationCdfInverse(float p, float stdDev1, float stdDev2, float lerpWeight)
{
return Mathf.Lerp(NormalCdfInverse(p, stdDev1), NormalCdfInverse(p, stdDev2), lerpWeight);
}
// <<< Old SSS Model
}
[Serializable]
public class SubsurfaceScatteringSettings : ISerializationCallbackReceiver
{
public int numProfiles; // Excluding the neutral profile
public SubsurfaceScatteringProfile[] profiles;
// Below are the cached values. TODO: uncomment when SSS profile asset serialization is fixed.
/*[NonSerialized]*/ public int texturingModeFlags; // 1 bit/profile; 0 = PreAndPostScatter, 1 = PostScatter
/*[NonSerialized]*/ public int transmissionFlags; // 2 bit/profile; 0 = inf. thick, 1 = thin, 2 = regular
/*[NonSerialized]*/ public Vector4[] thicknessRemaps; // Remap: 0 = start, 1 = end - start
/*[NonSerialized]*/ public Vector4[] worldScales; // Size of the world unit in meters (only the X component is used)
/*[NonSerialized]*/ public Vector4[] shapeParams; // RGB = S = 1 / D, A = filter radius
/*[NonSerialized]*/ public Vector4[] transmissionTints; // RGB = color, A = unused
/*[NonSerialized]*/ public Vector4[] filterKernels; // XY = near field, ZW = far field; 0 = radius, 1 = reciprocal of the PDF
// Old SSS Model >>>
public bool useDisneySSS;
/*[NonSerialized]*/ public Vector4[] halfRcpWeightedVariances;
/*[NonSerialized]*/ public Vector4[] halfRcpVariancesAndWeights;
/*[NonSerialized]*/ public Vector4[] filterKernelsBasic;
// <<< Old SSS Model
// --- Public Methods ---
public SubsurfaceScatteringSettings()
{
numProfiles = 1;
profiles = new SubsurfaceScatteringProfile[numProfiles];
profiles[0] = null;
texturingModeFlags = 0;
transmissionFlags = 0;
thicknessRemaps = null;
worldScales = null;
shapeParams = null;
transmissionTints = null;
filterKernels = null;
// Old SSS Model >>>
useDisneySSS = true;
halfRcpWeightedVariances = null;
halfRcpVariancesAndWeights = null;
filterKernelsBasic = null;
// <<< Old SSS Model
UpdateCache();
}
public void OnValidate()
{
// Reserve one slot for the neutral profile.
numProfiles = Math.Min(profiles.Length, SssConstants.SSS_N_PROFILES - 1);
if (profiles.Length != numProfiles)
{
Array.Resize(ref profiles, numProfiles);
}
for (int i = 0; i < numProfiles; i++)
{
if (profiles[i] != null)
{
// Assign the profile IDs.
profiles[i].settingsIndex = i;
}
}
foreach (var profile in profiles)
{
if (profile != null)
profile.Validate();
}
UpdateCache();
}
public void UpdateCache()
{
texturingModeFlags = transmissionFlags = 0;
if (thicknessRemaps == null || thicknessRemaps.Length != SssConstants.SSS_N_PROFILES)
{
thicknessRemaps = new Vector4[SssConstants.SSS_N_PROFILES];
}
if (worldScales == null || worldScales.Length != SssConstants.SSS_N_PROFILES)
{
worldScales = new Vector4[SssConstants.SSS_N_PROFILES];
}
if (shapeParams == null || shapeParams.Length != SssConstants.SSS_N_PROFILES)
{
shapeParams = new Vector4[SssConstants.SSS_N_PROFILES];
}
if (transmissionTints == null || transmissionTints.Length != SssConstants.SSS_N_PROFILES)
{
transmissionTints = new Vector4[SssConstants.SSS_N_PROFILES];
}
const int filterKernelsNearFieldLen = SssConstants.SSS_N_PROFILES * SssConstants.SSS_N_SAMPLES_NEAR_FIELD;
if (filterKernels == null || filterKernels.Length != filterKernelsNearFieldLen)
{
filterKernels = new Vector4[filterKernelsNearFieldLen];
}
// Old SSS Model >>>
if (halfRcpWeightedVariances == null || halfRcpWeightedVariances.Length != SssConstants.SSS_N_PROFILES)
{
halfRcpWeightedVariances = new Vector4[SssConstants.SSS_N_PROFILES];
}
if (halfRcpVariancesAndWeights == null || halfRcpVariancesAndWeights.Length != 2 * SssConstants.SSS_N_PROFILES)
{
halfRcpVariancesAndWeights = new Vector4[2 * SssConstants.SSS_N_PROFILES];
}
const int filterKernelsLen = SssConstants.SSS_N_PROFILES * SssConstants.SSS_BASIC_N_SAMPLES;
if (filterKernelsBasic == null || filterKernelsBasic.Length != filterKernelsLen)
{
filterKernelsBasic = new Vector4[filterKernelsLen];
}
// <<< Old SSS Model
for (int i = 0; i < SssConstants.SSS_N_PROFILES - 1; i++)
{
// If a profile is null, it means that it was never set in the HDRenderPipeline Asset or that the profile asset has been deleted.
// In this case we want the users to be warned if a material uses one of those. This is why we fill the profile with pink transmission values.
if (i >= numProfiles || profiles[i] == null)
{
// Pink transmission
transmissionFlags |= 1 << i * 2;
transmissionTints[i] = new Vector4(100.0f, 0.0f, 100.0f, 1.0f);
// Default neutral values for the rest
worldScales[i] = Vector4.one;
shapeParams[i] = Vector4.zero;
for (int j = 0, n = SssConstants.SSS_N_SAMPLES_NEAR_FIELD; j < n; j++)
{
filterKernels[n * i + j].x = 0.0f;
filterKernels[n * i + j].y = 1.0f;
filterKernels[n * i + j].z = 0.0f;
filterKernels[n * i + j].w = 1.0f;
}
// Old SSS Model >>>
halfRcpWeightedVariances[i] = Vector4.one;
halfRcpVariancesAndWeights[2 * i + 0] = Vector4.one;
halfRcpVariancesAndWeights[2 * i + 1] = Vector4.one;
for (int j = 0, n = SssConstants.SSS_BASIC_N_SAMPLES; j < n; j++)
{
filterKernelsBasic[n * i + j] = Vector4.one;
filterKernelsBasic[n * i + j].w = 0.0f;
}
continue;
}
Debug.Assert(numProfiles < 16, "Transmission flags (32-bit integer) cannot support more than 16 profiles.");
texturingModeFlags |= (int)profiles[i].texturingMode << i;
transmissionFlags |= (int)profiles[i].transmissionMode << i * 2;
thicknessRemaps[i] = new Vector4(profiles[i].thicknessRemap.x, profiles[i].thicknessRemap.y - profiles[i].thicknessRemap.x, 0.0f, 0.0f);
worldScales[i] = new Vector4(profiles[i].worldScale, 0, 0, 0);
shapeParams[i] = profiles[i].shapeParameter;
shapeParams[i].w = profiles[i].maxRadius;
transmissionTints[i] = profiles[i].transmissionTint * 0.25f; // Premultiplied
for (int j = 0, n = SssConstants.SSS_N_SAMPLES_NEAR_FIELD; j < n; j++)
{
filterKernels[n * i + j].x = profiles[i].filterKernelNearField[j].x;
filterKernels[n * i + j].y = profiles[i].filterKernelNearField[j].y;
if (j < SssConstants.SSS_N_SAMPLES_FAR_FIELD)
{
filterKernels[n * i + j].z = profiles[i].filterKernelFarField[j].x;
filterKernels[n * i + j].w = profiles[i].filterKernelFarField[j].y;
}
}
// Old SSS Model >>>
halfRcpWeightedVariances[i] = profiles[i].halfRcpWeightedVariances;
Vector4 stdDev1 = ((1.0f / 3.0f) * SssConstants.SSS_BASIC_DISTANCE_SCALE) * profiles[i].scatterDistance1;
Vector4 stdDev2 = ((1.0f / 3.0f) * SssConstants.SSS_BASIC_DISTANCE_SCALE) * profiles[i].scatterDistance2;
// Multiply by 0.1 to convert from millimeters to centimeters. Apply the distance scale.
// Rescale by 4 to counter rescaling of transmission tints.
float a = 0.1f * SssConstants.SSS_BASIC_DISTANCE_SCALE;
halfRcpVariancesAndWeights[2 * i + 0] = new Vector4(a * a * 0.5f / (stdDev1.x * stdDev1.x), a * a * 0.5f / (stdDev1.y * stdDev1.y), a * a * 0.5f / (stdDev1.z * stdDev1.z), 4 * (1.0f - profiles[i].lerpWeight));
halfRcpVariancesAndWeights[2 * i + 1] = new Vector4(a * a * 0.5f / (stdDev2.x * stdDev2.x), a * a * 0.5f / (stdDev2.y * stdDev2.y), a * a * 0.5f / (stdDev2.z * stdDev2.z), 4 * profiles[i].lerpWeight);
for (int j = 0, n = SssConstants.SSS_BASIC_N_SAMPLES; j < n; j++)
{
filterKernelsBasic[n * i + j] = profiles[i].filterKernelBasic[j];
}
// <<< Old SSS Model
}
// Fill the neutral profile.
{
int i = SssConstants.SSS_NEUTRAL_PROFILE_ID;
worldScales[i] = Vector4.one;
shapeParams[i] = Vector4.zero;
for (int j = 0, n = SssConstants.SSS_N_SAMPLES_NEAR_FIELD; j < n; j++)
{
filterKernels[n * i + j].x = 0.0f;
filterKernels[n * i + j].y = 1.0f;
filterKernels[n * i + j].z = 0.0f;
filterKernels[n * i + j].w = 1.0f;
}
// Old SSS Model >>>
halfRcpWeightedVariances[i] = Vector4.one;
for (int j = 0, n = SssConstants.SSS_BASIC_N_SAMPLES; j < n; j++)
{
filterKernelsBasic[n * i + j] = Vector4.one;
filterKernelsBasic[n * i + j].w = 0.0f;
}
// <<< Old SSS Model
}
}
public void OnBeforeSerialize()
{
// No special action required.
}
public void OnAfterDeserialize()
{
// TODO: uncomment when SSS profile asset serialization is fixed.
// UpdateCache();
}
}
}