---
title: "Build real-time neural rendering pipelines with Metal"
description: "Integrate ML into Metal 4 renderers with MetalFX denoising, ML command encoder deployment, and inline shader networks using TensorOps."
shoutout: "Unofficial, made with love by Superwall - the best way to monetize your apps."
category: "Graphics & Games"
---

# Build real-time neural rendering pipelines with Metal


[Watch on Apple Developer](https://developer.apple.com/videos/play/wwdc2026/359/)

Integrate ML into Metal 4 renderers with MetalFX denoising, ML command encoder deployment, and inline shader networks using TensorOps.

## TL;DR

- Use MetalFX Denoising for low-sample real-time path tracing; quality depends heavily on clean auxiliary inputs, primary surface replacement, and dejittered motion vectors.
- Deploy offline-trained neural render stages, such as a neural tone mapper, by exporting to MTLPackage and running them with the Metal 4 ML command encoder inside the same command buffer as rendering work.
- Build tiny specialized neural networks directly in Metal shaders with TensorOps and cooperative tensors, including online-trained models that adapt over frames.
- The session frames ML adoption as three levels: platform-integrated MetalFX, custom pre-trained models with the ML command encoder, and inline neural evaluation/training in shaders.

## Three levels of ML integration in Metal renderers

Metal 4 supports a progression of machine-learning integration points for real-time rendering. At the highest level, MetalFX provides platform-integrated neural denoising and upscaling. For custom trained models, the Metal 4 ML command encoder runs model inference directly in a command buffer. For maximum flexibility, TensorOps lets shaders evaluate small neural networks inline, using hardware acceleration on supported Apple silicon GPUs.

The session focuses on practical rendering use cases: denoising a one-sample-per-pixel path tracer, replacing a multi-stage post-processing chain with a neural tone mapper, and evaluating/training tiny scene-specific networks such as a sky illumination probe.

- MetalFX: ready-to-use neural denoising and upscaling for interactive renderers.
- ML command encoder: run pre-trained models in the same frame and command buffer as graphics, compute, and render passes.
- TensorOps API: implement small networks directly in shaders, including inference and back propagation routines.
- Relevant workloads include real-time path tracing viewports, games, learned tone mapping, image-based lighting, and other learnable rendering signals.

## MetalFX Denoising for low-sample path tracing

A real-time path tracer often has a frame budget of only one or a few samples per pixel. MetalFX Denoising is designed for low-latency live viewports and combines neural denoising with upscaling to turn noisy input into a temporally stable output.

The Redshift Live example from Maxon demonstrates a one-sample-per-pixel path-traced viewport using hardware-accelerated ray tracing plus MetalFX neural denoising to produce cleaner interactive lighting, shadows, and global illumination.

- Provide high-quality auxiliary inputs such as diffuse albedo, depth, and motion vectors; denoiser output quality tracks input quality.
- Treat diffuse albedo as a strong denoising signal and make it as close as possible to a noise-free version of the desired final result.
- Build debug views for every MetalFX input and inspect textures frame-by-frame in a GPU capture.
- Use the transparency overlay for noise-free layers such as particles, fog, volumetrics, or sky that should be upscaled and composited rather than denoised.
- Use the denoiser strength mask to reduce or disable denoising per pixel, from 0 for no denoising to 1 for maximum strength.

## Inputs, reflections, glass, and motion vectors

MetalFX quality depends on feeding the model the signal it expects. For mirror-like and transmissive materials, the auxiliary buffers should represent what the viewer sees rather than only the primary hit surface. The session describes this as primary surface replacement.

For mirrors, store reflected geometry properties such as albedo, normal, and roughness in the mirror-like object's auxiliary data. For glass, blend reflected and refracted geometry properties using the Fresnel term to reduce noisy inputs and preserve sharp reflection and refraction results.

Motion vectors are also critical for temporal stability. MetalFX expects dejittered per-pixel screen-space displacements from the current frame to the previous frame. If subpixel jitter is left in the motion vectors, edges can shimmer because vectors may be nearly a pixel wrong.

- For static objects, compute camera-only motion vectors by projecting the current world position with current and previous view-projection matrices, then subtracting the jitter delta.
- For moving or deforming geometry, store previous-frame world positions or skin twice to compute actual motion.
- For unreliable fast motion, such as alpha-blended particles, use the reactive mask rather than trusting bad motion vectors.

### Compute camera-only motion vectors

Projects the same world position into current and previous clip space, computes screen-space displacement, then removes the jitter delta to produce dejittered motion vectors for MetalFX.

```text
#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;
```

## Deploy a custom neural tone mapper with the ML command encoder

Metal 4 can run a custom trained neural network inline with the rest of a renderer's command-buffer work. The example use case is replacing a complex HDR post-processing pipeline - tone mapping, color grading, film emulation, and related stages - with a single learned color transformation.

The session uses HDRNet as an example architecture. HDRNet operates on a downsampled image, performs global and local analysis, creates localized color transforms for 16×16 tiles, and applies them with edge-aware techniques to produce the final tone-mapped image.

- Train the network offline in a framework such as PyTorch, using manually tone-mapped projects or generated tone-mapped images from the renderer as training data.
- Export the trained model to an MTLPackage.
- During setup, load the MTLPackage, specify the network function with a function descriptor, and create a machine learning pipeline descriptor.
- During execution, create an ML command encoder, bind inputs and outputs through an argument table, and dispatch the work in the command buffer.
- A representative frame pipeline becomes: path tracing samples, MetalFX denoising, neural tone mapping, then display/output work.

## Inline neural networks with TensorOps

TensorOps supports small neural networks directly inside Metal shaders, alongside ALU, texture sampling, compute, and render work. This is aimed at tiny, specialized models with a few thousand parameters or fewer, often trained on scene-specific data rather than large general-purpose datasets.

The sky illumination example models average sky lighting from visible directions. Instead of relying on an offline-precomputed signal that can become stale during a dynamic day-night cycle, the renderer can run training iterations over frames: sample a direction, run inference, compute an analytical target, calculate error, and run back propagation to improve the model.

The example network is a fully connected multilayer perceptron with a 3-4-4-3 shape: three floats encode an input direction, two hidden layers have four neurons each, and three output floats represent color.

- Batch inputs as 2D tensors, such as a matrix of input directions; outputs can likewise be a 2D matrix of colors.
- Evaluate a forward pass by multiplying the input tensor by layer weights with a 2D tensor matmul, then applying activation functions between layers.
- Use thread execution scope when one thread should own the operation, such as divergent work or stages without full threadgroup control.
- Use SIMD group execution scope in compute when participating threads can cooperate on the same matrix multiplication.
- Use cooperative tensors to keep intermediate results distributed in fast thread storage memory instead of round-tripping through main memory.

## Adoption guidance and next steps

The recommended entry point depends on how much control the renderer needs. MetalFX is the fastest path for real-time apps that need denoising and upscaling. The ML command encoder is appropriate when a renderer has an offline-trained model to deploy as a standalone stage. TensorOps is for highly specialized inline networks where shader-level integration and possibly online training are needed.

- Start with MetalFX Denoising and Upscaling for real-time viewports and games, especially low-sample path tracing.
- Use the Metal 4 sample code and Xcode to explore the core API patterns.
- Try training a neural tone mapper against an existing post-processing pipeline before replacing it in production.
- Use the Metal Performance Primitives Programming Guide for TensorOps and back propagation implementation details.

## Resources

- [Training a neural network to render irradiance in real time](https://developer.apple.com/documentation/Metal/training-a-neural-network-to-render-irradiance-in-real-time)
- [Metal sample code library](https://developer.apple.com/documentation/Metal/metal-sample-code-library)
- [Download the Metal Performance Primitives (MPP) Programming Guide](https://developer.apple.com/download/files/Metal-Performance-Primitives-Programming-Guide.pdf)
- [Understanding the Metal 4 core API](https://developer.apple.com/documentation/Metal/understanding-the-metal-4-core-api)

## Related Sessions

- [Combine Metal 4 machine learning and graphics](https://developer.apple.com/videos/play/wwdc2025/262)
- [Go further with Metal 4 games](https://developer.apple.com/videos/play/wwdc2025/211)

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