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Metalによるリアルタイムのニューラルレンダリングパイプラインの構築
Metal 4を使用して機械学習をリアルタイムレンダリングパイプラインに統合する方法を紹介します。MetalFXニューラルデノイジングによってプロダクション品質の成果を達成するための実用的な導入パターンとベストプラクティス、MaxonのRedshift Liveからの実践的なインサイトを共有します。MLコマンドエンコーダーをインラインで使用して、グラフィックス作品のニューラルトーンマッパーをトレーニングしてデプロイする方法や、テンソルに関する新しいAPIを使って、シェーダ内でコンパクトな特化型ニューラルネットワークを直接構築し評価する方法について解説します。
関連する章
- 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
リソース
- 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
関連ビデオ
WWDC25
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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.