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516 行
24 KiB
516 行
24 KiB
using System;
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namespace UnityEngine.Experimental.Rendering.HDPipeline
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{
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[GenerateHLSL]
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public class DiffusionProfileConstants
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{
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public const int DIFFUSION_PROFILE_COUNT = 16; // Max. number of profiles, including the slot taken by the neutral profile
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public const int DIFFUSION_PROFILE_NEUTRAL_ID = 0; // Does not result in blurring
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public const int SSS_N_SAMPLES_NEAR_FIELD = 55; // Used for extreme close ups; must be a Fibonacci number
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public const int SSS_N_SAMPLES_FAR_FIELD = 21; // Used at a regular distance; must be a Fibonacci number
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public const int SSS_LOD_THRESHOLD = 4; // The LoD threshold of the near-field kernel (in pixels)
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// Old SSS Model >>>
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public const int SSS_BASIC_N_SAMPLES = 11; // Must be an odd number
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public const int SSS_BASIC_DISTANCE_SCALE = 3; // SSS distance units per centimeter
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// <<< Old SSS Model
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}
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[Serializable]
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public sealed class DiffusionProfile
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{
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public enum TexturingMode : uint
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{
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PreAndPostScatter = 0,
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PostScatter = 1
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}
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public enum TransmissionMode : uint
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{
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Regular = 0,
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ThinObject = 1
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}
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public string name;
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[ColorUsage(false, true)]
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public Color scatteringDistance; // Per color channel (no meaningful units)
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[ColorUsage(false, true)]
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public Color transmissionTint; // HDR color
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public TexturingMode texturingMode;
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public TransmissionMode transmissionMode;
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public Vector2 thicknessRemap; // X = min, Y = max (in millimeters)
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public float worldScale; // Size of the world unit in meters
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public float ior; // 1.4 for skin (mean ~0.028)
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// Old SSS Model >>>
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[ColorUsage(false, true)]
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public Color scatterDistance1;
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[ColorUsage(false, true)]
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public Color scatterDistance2;
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[Range(0f, 1f)]
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public float lerpWeight;
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// <<< Old SSS Model
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public Vector3 shapeParam { get; private set; } // RGB = shape parameter: S = 1 / D
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public float maxRadius { get; private set; } // In millimeters
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public Vector2[] filterKernelNearField { get; private set; } // X = radius, Y = reciprocal of the PDF
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public Vector2[] filterKernelFarField { get; private set; } // X = radius, Y = reciprocal of the PDF
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public Vector4 halfRcpWeightedVariances { get; private set; }
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public Vector4[] filterKernelBasic { get; private set; }
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public DiffusionProfile(string name)
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{
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this.name = name;
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scatteringDistance = Color.grey;
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transmissionTint = Color.white;
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texturingMode = TexturingMode.PreAndPostScatter;
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transmissionMode = TransmissionMode.ThinObject;
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thicknessRemap = new Vector2(0f, 5f);
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worldScale = 1f;
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ior = 1.4f; // TYpical value for skin specular reflectance
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// Old SSS Model >>>
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scatterDistance1 = new Color(0.3f, 0.3f, 0.3f, 0f);
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scatterDistance2 = new Color(0.5f, 0.5f, 0.5f, 0f);
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lerpWeight = 1f;
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// <<< Old SSS Model
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}
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public void Validate()
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{
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thicknessRemap.y = Mathf.Max(thicknessRemap.y, 0f);
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thicknessRemap.x = Mathf.Clamp(thicknessRemap.x, 0f, thicknessRemap.y);
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worldScale = Mathf.Max(worldScale, 0.001f);
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ior = Mathf.Clamp(ior, 1.0f, 2.0f);
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// Old SSS Model >>>
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scatterDistance1 = new Color
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{
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r = Mathf.Max(0.05f, scatterDistance1.r),
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g = Mathf.Max(0.05f, scatterDistance1.g),
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b = Mathf.Max(0.05f, scatterDistance1.b),
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a = 0.0f
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};
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scatterDistance2 = new Color
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{
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r = Mathf.Max(0.05f, scatterDistance2.r),
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g = Mathf.Max(0.05f, scatterDistance2.g),
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b = Mathf.Max(0.05f, scatterDistance2.b),
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a = 0f
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};
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// <<< Old SSS Model
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UpdateKernel();
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}
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// Ref: Approximate Reflectance Profiles for Efficient Subsurface Scattering by Pixar.
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public void UpdateKernel()
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{
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if (filterKernelNearField == null || filterKernelNearField.Length != DiffusionProfileConstants.SSS_N_SAMPLES_NEAR_FIELD)
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filterKernelNearField = new Vector2[DiffusionProfileConstants.SSS_N_SAMPLES_NEAR_FIELD];
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if (filterKernelFarField == null || filterKernelFarField.Length != DiffusionProfileConstants.SSS_N_SAMPLES_FAR_FIELD)
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filterKernelFarField = new Vector2[DiffusionProfileConstants.SSS_N_SAMPLES_FAR_FIELD];
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// Clamp to avoid artifacts.
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shapeParam = new Vector3(
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1f / Mathf.Max(0.001f, scatteringDistance.r),
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1f / Mathf.Max(0.001f, scatteringDistance.g),
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1f / Mathf.Max(0.001f, scatteringDistance.b)
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);
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// We importance sample the color channel with the widest scattering distance.
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float s = Mathf.Min(shapeParam.x, shapeParam.y, shapeParam.z);
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// Importance sample the normalized diffuse reflectance profile for the computed value of 's'.
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// ------------------------------------------------------------------------------------
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// R[r, phi, s] = s * (Exp[-r * s] + Exp[-r * s / 3]) / (8 * Pi * r)
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// PDF[r, phi, s] = r * R[r, phi, s]
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// CDF[r, s] = 1 - 1/4 * Exp[-r * s] - 3/4 * Exp[-r * s / 3]
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// ------------------------------------------------------------------------------------
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// Importance sample the near field kernel.
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for (int i = 0, n = DiffusionProfileConstants.SSS_N_SAMPLES_NEAR_FIELD; i < n; i++)
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{
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float p = (i + 0.5f) * (1f / n);
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float r = DisneyProfileCdfInverse(p, s);
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// N.b.: computation of normalized weights, and multiplication by the surface albedo
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// of the actual geometry is performed at runtime (in the shader).
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filterKernelNearField[i].x = r;
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filterKernelNearField[i].y = 1f / DisneyProfilePdf(r, s);
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}
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// Importance sample the far field kernel.
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for (int i = 0, n = DiffusionProfileConstants.SSS_N_SAMPLES_FAR_FIELD; i < n; i++)
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{
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float p = (i + 0.5f) * (1f / n);
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float r = DisneyProfileCdfInverse(p, s);
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// N.b.: computation of normalized weights, and multiplication by the surface albedo
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// of the actual geometry is performed at runtime (in the shader).
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filterKernelFarField[i].x = r;
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filterKernelFarField[i].y = 1f / DisneyProfilePdf(r, s);
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}
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maxRadius = filterKernelFarField[DiffusionProfileConstants.SSS_N_SAMPLES_FAR_FIELD - 1].x;
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// Old SSS Model >>>
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UpdateKernelAndVarianceData();
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// <<< Old SSS Model
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}
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// Old SSS Model >>>
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public void UpdateKernelAndVarianceData()
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{
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const int kNumSamples = DiffusionProfileConstants.SSS_BASIC_N_SAMPLES;
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const int kDistanceScale = DiffusionProfileConstants.SSS_BASIC_DISTANCE_SCALE;
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if (filterKernelBasic == null || filterKernelBasic.Length != kNumSamples)
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filterKernelBasic = new Vector4[kNumSamples];
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// Apply the three-sigma rule, and rescale.
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var stdDev1 = ((1f / 3f) * kDistanceScale) * scatterDistance1;
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var stdDev2 = ((1f / 3f) * kDistanceScale) * scatterDistance2;
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// Our goal is to blur the image using a filter which is represented
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// as a product of a linear combination of two normalized 1D Gaussians
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// as suggested by Jimenez et al. in "Separable Subsurface Scattering".
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// A normalized (i.e. energy-preserving) 1D Gaussian with the mean of 0
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// is defined as follows: G1(x, v) = exp(-x * x / (2 * v)) / sqrt(2 * Pi * v),
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// where 'v' is variance and 'x' is the radial distance from the origin.
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// Using the weight 'w', our 1D and the resulting 2D filters are given as:
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// A1(v1, v2, w, x) = G1(x, v1) * (1 - w) + G1(r, v2) * w,
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// A2(v1, v2, w, x, y) = A1(v1, v2, w, x) * A1(v1, v2, w, y).
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// The resulting filter function is a non-Gaussian PDF.
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// It is separable by design, but generally not radially symmetric.
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// N.b.: our scattering distance is rather limited. Therefore, in order to allow
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// for a greater range of standard deviation values for flatter profiles,
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// we rescale the world using 'distanceScale', effectively reducing the SSS
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// distance units from centimeters to (1 / distanceScale).
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// Find the widest Gaussian across 3 color channels.
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float maxStdDev1 = Mathf.Max(stdDev1.r, stdDev1.g, stdDev1.b);
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float maxStdDev2 = Mathf.Max(stdDev2.r, stdDev2.g, stdDev2.b);
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var weightSum = Vector3.zero;
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float step = 1f / (kNumSamples - 1);
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// Importance sample the linear combination of two Gaussians.
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for (int i = 0; i < kNumSamples; i++)
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{
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// Generate 'u' on (0, 0.5] and (0.5, 1).
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float u = (i <= kNumSamples / 2) ? 0.5f - i * step // The center and to the left
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: i * step; // From the center to the right
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u = Mathf.Clamp(u, 0.001f, 0.999f);
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float pos = GaussianCombinationCdfInverse(u, maxStdDev1, maxStdDev2, lerpWeight);
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float pdf = GaussianCombination(pos, maxStdDev1, maxStdDev2, lerpWeight);
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Vector3 val;
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val.x = GaussianCombination(pos, stdDev1.r, stdDev2.r, lerpWeight);
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val.y = GaussianCombination(pos, stdDev1.g, stdDev2.g, lerpWeight);
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val.z = GaussianCombination(pos, stdDev1.b, stdDev2.b, lerpWeight);
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// We do not divide by 'numSamples' since we will renormalize, anyway.
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filterKernelBasic[i].x = val.x * (1 / pdf);
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filterKernelBasic[i].y = val.y * (1 / pdf);
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filterKernelBasic[i].z = val.z * (1 / pdf);
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filterKernelBasic[i].w = pos;
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weightSum.x += filterKernelBasic[i].x;
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weightSum.y += filterKernelBasic[i].y;
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weightSum.z += filterKernelBasic[i].z;
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}
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// Renormalize the weights to conserve energy.
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for (int i = 0; i < kNumSamples; i++)
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{
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filterKernelBasic[i].x *= 1 / weightSum.x;
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filterKernelBasic[i].y *= 1 / weightSum.y;
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filterKernelBasic[i].z *= 1 / weightSum.z;
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}
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Vector4 weightedStdDev;
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weightedStdDev.x = Mathf.Lerp(stdDev1.r, stdDev2.r, lerpWeight);
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weightedStdDev.y = Mathf.Lerp(stdDev1.g, stdDev2.g, lerpWeight);
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weightedStdDev.z = Mathf.Lerp(stdDev1.b, stdDev2.b, lerpWeight);
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weightedStdDev.w = Mathf.Lerp(maxStdDev1, maxStdDev2, lerpWeight);
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// Store (1 / (2 * WeightedVariance)) per color channel.
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// Warning: do not use halfRcpWeightedVariances.Set(). It will not work.
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halfRcpWeightedVariances = new Vector4(0.5f / (weightedStdDev.x * weightedStdDev.x),
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0.5f / (weightedStdDev.y * weightedStdDev.y),
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0.5f / (weightedStdDev.z * weightedStdDev.z),
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0.5f / (weightedStdDev.w * weightedStdDev.w));
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}
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// <<< Old SSS Model
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static float DisneyProfile(float r, float s)
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{
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return s * (Mathf.Exp(-r * s) + Mathf.Exp(-r * s * (1.0f / 3.0f))) / (8.0f * Mathf.PI * r);
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}
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static float DisneyProfilePdf(float r, float s)
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{
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return r * DisneyProfile(r, s);
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}
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static float DisneyProfileCdf(float r, float s)
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{
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return 1.0f - 0.25f * Mathf.Exp(-r * s) - 0.75f * Mathf.Exp(-r * s * (1.0f / 3.0f));
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}
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static float DisneyProfileCdfDerivative1(float r, float s)
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{
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return 0.25f * s * Mathf.Exp(-r * s) * (1.0f + Mathf.Exp(r * s * (2.0f / 3.0f)));
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}
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static float DisneyProfileCdfDerivative2(float r, float s)
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{
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return (-1.0f / 12.0f) * s * s * Mathf.Exp(-r * s) * (3.0f + Mathf.Exp(r * s * (2.0f / 3.0f)));
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}
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// The CDF is not analytically invertible, so we use Halley's Method of root finding.
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// { f(r, s, p) = CDF(r, s) - p = 0 } with the initial guess { r = (10^p - 1) / s }.
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static float DisneyProfileCdfInverse(float p, float s)
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{
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// Supply the initial guess.
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float r = (Mathf.Pow(10f, p) - 1f) / s;
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float t = float.MaxValue;
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while (true)
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{
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float f0 = DisneyProfileCdf(r, s) - p;
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float f1 = DisneyProfileCdfDerivative1(r, s);
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float f2 = DisneyProfileCdfDerivative2(r, s);
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float dr = f0 / (f1 * (1f - f0 * f2 / (2f * f1 * f1)));
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if (Mathf.Abs(dr) < t)
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{
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r = r - dr;
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t = Mathf.Abs(dr);
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}
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else
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{
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// Converged to the best result.
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break;
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}
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}
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return r;
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}
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// Old SSS Model >>>
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static float Gaussian(float x, float stdDev)
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{
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float variance = stdDev * stdDev;
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return Mathf.Exp(-x * x / (2 * variance)) / Mathf.Sqrt(2 * Mathf.PI * variance);
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}
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static float GaussianCombination(float x, float stdDev1, float stdDev2, float lerpWeight)
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{
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return Mathf.Lerp(Gaussian(x, stdDev1), Gaussian(x, stdDev2), lerpWeight);
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}
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static float RationalApproximation(float t)
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{
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// Abramowitz and Stegun formula 26.2.23.
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// The absolute value of the error should be less than 4.5 e-4.
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float[] c = { 2.515517f, 0.802853f, 0.010328f };
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float[] d = { 1.432788f, 0.189269f, 0.001308f };
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return t - ((c[2] * t + c[1]) * t + c[0]) / (((d[2] * t + d[1]) * t + d[0]) * t + 1.0f);
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}
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// Ref: https://www.johndcook.com/blog/csharp_phi_inverse/
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static float NormalCdfInverse(float p, float stdDev)
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{
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float x;
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if (p < 0.5)
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{
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// F^-1(p) = - G^-1(p)
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x = -RationalApproximation(Mathf.Sqrt(-2f * Mathf.Log(p)));
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}
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else
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{
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// F^-1(p) = G^-1(1-p)
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x = RationalApproximation(Mathf.Sqrt(-2f * Mathf.Log(1f - p)));
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}
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return x * stdDev;
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}
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static float GaussianCombinationCdfInverse(float p, float stdDev1, float stdDev2, float lerpWeight)
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{
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return Mathf.Lerp(NormalCdfInverse(p, stdDev1), NormalCdfInverse(p, stdDev2), lerpWeight);
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}
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// <<< Old SSS Model
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}
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public sealed class DiffusionProfileSettings : ScriptableObject
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{
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public DiffusionProfile[] profiles;
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[NonSerialized] public uint texturingModeFlags; // 1 bit/profile: 0 = PreAndPostScatter, 1 = PostScatter
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[NonSerialized] public uint transmissionFlags; // 1 bit/profile: 0 = regular, 1 = thin
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[NonSerialized] public Vector4[] thicknessRemaps; // Remap: 0 = start, 1 = end - start
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[NonSerialized] public Vector4[] worldScales; // X = meters per world unit; Y = world units per meter
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[NonSerialized] public Vector4[] shapeParams; // RGB = S = 1 / D, A = filter radius
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[NonSerialized] public Vector4[] transmissionTintsAndFresnel0; // RGB = color, A = fresnel0
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[NonSerialized] public Vector4[] disabledTransmissionTintsAndFresnel0; // RGB = black, A = fresnel0 - For debug to remove the transmission
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[NonSerialized] public Vector4[] filterKernels; // XY = near field, ZW = far field; 0 = radius, 1 = reciprocal of the PDF
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// Old SSS Model >>>
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[NonSerialized] public Vector4[] halfRcpWeightedVariances;
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[NonSerialized] public Vector4[] halfRcpVariancesAndWeights;
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[NonSerialized] public Vector4[] filterKernelsBasic;
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// <<< Old SSS Model
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public DiffusionProfile this[int index]
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{
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get
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{
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if (index >= DiffusionProfileConstants.DIFFUSION_PROFILE_COUNT - 1)
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throw new IndexOutOfRangeException("index");
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return profiles[index];
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}
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}
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void OnEnable()
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{
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// The neutral profile is not a part of the array.
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int profileArraySize = DiffusionProfileConstants.DIFFUSION_PROFILE_COUNT - 1;
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if (profiles != null && profiles.Length != profileArraySize)
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Array.Resize(ref profiles, profileArraySize);
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if (profiles == null)
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profiles = new DiffusionProfile[profileArraySize];
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for (int i = 0; i < profileArraySize; i++)
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{
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if (profiles[i] == null)
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profiles[i] = new DiffusionProfile("Profile " + (i + 1));
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profiles[i].Validate();
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}
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ValidateArray(ref thicknessRemaps, DiffusionProfileConstants.DIFFUSION_PROFILE_COUNT);
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ValidateArray(ref worldScales, DiffusionProfileConstants.DIFFUSION_PROFILE_COUNT);
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ValidateArray(ref shapeParams, DiffusionProfileConstants.DIFFUSION_PROFILE_COUNT);
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ValidateArray(ref transmissionTintsAndFresnel0, DiffusionProfileConstants.DIFFUSION_PROFILE_COUNT);
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ValidateArray(ref disabledTransmissionTintsAndFresnel0, DiffusionProfileConstants.DIFFUSION_PROFILE_COUNT);
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ValidateArray(ref filterKernels, DiffusionProfileConstants.DIFFUSION_PROFILE_COUNT * DiffusionProfileConstants.SSS_N_SAMPLES_NEAR_FIELD);
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// Old SSS Model >>>
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ValidateArray(ref halfRcpWeightedVariances, DiffusionProfileConstants.DIFFUSION_PROFILE_COUNT);
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ValidateArray(ref halfRcpVariancesAndWeights, DiffusionProfileConstants.DIFFUSION_PROFILE_COUNT * 2);
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ValidateArray(ref filterKernelsBasic, DiffusionProfileConstants.DIFFUSION_PROFILE_COUNT * DiffusionProfileConstants.SSS_BASIC_N_SAMPLES);
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Debug.Assert(DiffusionProfileConstants.DIFFUSION_PROFILE_NEUTRAL_ID <= 32, "Transmission and Texture flags (32-bit integer) cannot support more than 32 profiles.");
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UpdateCache();
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}
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static void ValidateArray<T>(ref T[] array, int len)
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{
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if (array == null || array.Length != len)
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array = new T[len];
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}
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public void UpdateCache()
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{
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for (int i = 0; i < DiffusionProfileConstants.DIFFUSION_PROFILE_COUNT - 1; i++)
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{
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UpdateCache(i);
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}
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// Fill the neutral profile.
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int neutralId = DiffusionProfileConstants.DIFFUSION_PROFILE_NEUTRAL_ID;
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worldScales[neutralId] = Vector4.one;
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shapeParams[neutralId] = Vector4.zero;
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for (int j = 0, n = DiffusionProfileConstants.SSS_N_SAMPLES_NEAR_FIELD; j < n; j++)
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{
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filterKernels[n * neutralId + j].x = 0f;
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filterKernels[n * neutralId + j].y = 1f;
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filterKernels[n * neutralId + j].z = 0f;
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filterKernels[n * neutralId + j].w = 1f;
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}
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|
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// Old SSS Model >>>
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halfRcpWeightedVariances[neutralId] = Vector4.one;
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|
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for (int j = 0, n = DiffusionProfileConstants.SSS_BASIC_N_SAMPLES; j < n; j++)
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{
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filterKernelsBasic[n * neutralId + j] = Vector4.one;
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filterKernelsBasic[n * neutralId + j].w = 0f;
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}
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// <<< Old SSS Model
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|
}
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|
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public void UpdateCache(int p)
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{
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// 'p' is the profile array index. 'i' is the index in the shader (accounting for the neutral profile).
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int i = p + 1;
|
|
|
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// Erase previous value (This need to be done here individually as in the SSS editor we edit individual component)
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uint mask = 1u << i;
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texturingModeFlags &= ~mask;
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mask = 1u << i;
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transmissionFlags &= ~mask;
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|
|
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texturingModeFlags |= (uint)profiles[p].texturingMode << i;
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transmissionFlags |= (uint)profiles[p].transmissionMode << i;
|
|
|
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thicknessRemaps[i] = new Vector4(profiles[p].thicknessRemap.x, profiles[p].thicknessRemap.y - profiles[p].thicknessRemap.x, 0f, 0f);
|
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worldScales[i] = new Vector4(profiles[p].worldScale, 1.0f / profiles[p].worldScale, 0f, 0f);
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shapeParams[i] = profiles[p].shapeParam;
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shapeParams[i].w = profiles[p].maxRadius;
|
|
// Convert ior to fresnel0
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|
float fresnel0 = (profiles[p].ior - 1.0f) / (profiles[p].ior + 1.0f);
|
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fresnel0 *= fresnel0; // square
|
|
transmissionTintsAndFresnel0[i] = new Vector4(profiles[p].transmissionTint.r * 0.25f, profiles[p].transmissionTint.g * 0.25f, profiles[p].transmissionTint.b * 0.25f, fresnel0); // Premultiplied
|
|
disabledTransmissionTintsAndFresnel0[i] = new Vector4(0.0f, 0.0f, 0.0f, fresnel0);
|
|
|
|
for (int j = 0, n = DiffusionProfileConstants.SSS_N_SAMPLES_NEAR_FIELD; j < n; j++)
|
|
{
|
|
filterKernels[n * i + j].x = profiles[p].filterKernelNearField[j].x;
|
|
filterKernels[n * i + j].y = profiles[p].filterKernelNearField[j].y;
|
|
|
|
if (j < DiffusionProfileConstants.SSS_N_SAMPLES_FAR_FIELD)
|
|
{
|
|
filterKernels[n * i + j].z = profiles[p].filterKernelFarField[j].x;
|
|
filterKernels[n * i + j].w = profiles[p].filterKernelFarField[j].y;
|
|
}
|
|
}
|
|
|
|
// Old SSS Model >>>
|
|
halfRcpWeightedVariances[i] = profiles[p].halfRcpWeightedVariances;
|
|
|
|
var stdDev1 = ((1f / 3f) * DiffusionProfileConstants.SSS_BASIC_DISTANCE_SCALE) * (Vector4)profiles[p].scatterDistance1;
|
|
var stdDev2 = ((1f / 3f) * DiffusionProfileConstants.SSS_BASIC_DISTANCE_SCALE) * (Vector4)profiles[p].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 * DiffusionProfileConstants.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), 4f * (1f - profiles[p].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), 4f * profiles[p].lerpWeight);
|
|
|
|
for (int j = 0, n = DiffusionProfileConstants.SSS_BASIC_N_SAMPLES; j < n; j++)
|
|
{
|
|
filterKernelsBasic[n * i + j] = profiles[p].filterKernelBasic[j];
|
|
}
|
|
// <<< Old SSS Model
|
|
}
|
|
}
|
|
}
|