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Metal로 실시간 뉴럴 렌더링 파이프라인 구축하기
Metal 4를 사용하여 실시간 렌더링 파이프라인에 머신 러닝을 통합하는 방법을 알아보세요. MetalFX 뉴럴 노이즈 제거로 프로덕션 품질의 결과를 얻는 실용적인 도입 패턴과 모범 사례를 살펴보며, Maxon의 Redshift Live에서 얻은 실제 인사이트를 소개합니다. 그래픽 작업에 맞게 ML 커맨드 인코더를 사용하여 뉴럴 톤 매퍼를 학습시키고 배포하는 방법을 알아보세요. 마지막으로, 새로운 텐서 API를 자세히 알아보고 셰이더 내에서 직접 작고 특수한 신경망을 구축하고 평가해 보세요.
챕터
- 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
리소스
- 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
관련 비디오
WWDC25
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비디오 검색…
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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.