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Crea flujos de trabajo de renderizado neuronal en tiempo real con Metal
Descubre cómo integrar el aprendizaje automático en tu flujo de trabajo de renderizado en tiempo real con Metal 4. Analizaremos patrones de implementación prácticos y mejores prácticas para lograr resultados con calidad de producción mediante la eliminación de ruido neuronal de MetalFX, con información real proporcionada por Redshift Live de Maxon. Obtén información sobre cómo entrenar e implementar un mapeador de tonos neuronal utilizando el codificador de comandos de ML en línea con tu trabajo gráfico. Por último, explora la nueva API tensor para crear y evaluar redes neuronales pequeñas y especializadas directamente dentro de tus sombreadores.
Capítulos
- 0:00 - Introduction
- 2:16 - MetalFX Denoising
- 9:57 - Deploy custom ML networks with Metal 4
- 13:40 - Inline neural networks with tensorOps
- 20:55 - Next steps
Recursos
- Training a neural network to render irradiance in real time
- Metal sample code library
- Download the Metal Performance Primitives (MPP) Programming Guide
- Understanding the Metal 4 core API
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8:46 - Compute camera-only motion vectors
#include <metal_stdlib> using namespace metal; // Compute camera-only motion vectors float4 clipCurrent = viewProjCurrent * float4(worldPos, 1.0); float2 ndcCurrent = clipCurrent.xy / clipCurrent.w; float4 clipPrevious = viewProjPrevious * float4(worldPos, 1.0); float2 ndcPrevious = clipPrevious.xy / clipPrevious.w; float2 motion = ndcPrevious - ndcCurrent; // Get subpixel offset for current and previous frames float2 jitterCurrent = getJitter(frameIndex); float2 jitterPrevious = getJitter(frameIndexPrevious); motion -= jitterPrevious - jitterCurrent;
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- 0:00 - Introduction
An overview of how machine learning is transforming real-time rendering pipelines on Apple platforms, and a preview of three levels of ML integration: MetalFX Denoising, deploying custom networks with Metal 4, and building tiny networks inline in shaders with tensorOps.
- 2:16 - MetalFX Denoising
How to integrate MetalFX Denoising into a path tracer running at one sample per pixel. Covers auxiliary inputs (albedo, depth, motion vectors), best practices for clean inputs, transparency overlays, the denoiser strength mask, and primary surface replacement for mirrors and glass — illustrated with Redshift Live from Maxon.
- 9:57 - Deploy custom ML networks with Metal 4
How to train a neural tone mapper offline (e.g., HDRNet), export it to Metal Performance Shaders Graph, and execute it inside a Metal 4 command buffer alongside your existing rendering passes to replace complex post-processing pipelines with a single network.
- 13:40 - Inline neural networks with tensorOps
How to build and run small multilayer perceptrons directly inside Metal shaders using the TensorOps API and cooperative tensors. Demonstrates an online-trained sky visibility probe that adapts to dynamic scenes each frame — enabling ML inference and training that runs alongside your existing compute and render work.
- 20:55 - Next steps
A recap of the three levels of ML integration in rendering pipelines, and guidance on where to start: download Xcode, explore Metal 4 sample code, and adopt MetalFX denoising for real-time applications first.