Neural History Validation for Ghosting-Resistant Temporal Anti-Aliasing

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Temporal Anti-aliasing (TAA) techniques in graphics processing units suffer from ghosting artifacts due to mismatched warped sample history and motion vector errors, leading to lower quality anti-aliasing when rendering at native resolution.

Innovation Solution

Implement a mixed low precision convolutional neural network for temporally amortized supersampling to enhance TAA, allowing rendering at lower spatial resolution while maintaining high-quality image output.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Stability of the object's composition

If traditional TAA is used with motion vector warping, then temporal stability is improved, but ghosting artifacts occur due to mismatched sample history

Engineering Contradiction:

Improvetemporal stabilityVSAvoidghosting artifacts

Core Design Contradiction:

Stability of the object's compositionVSObject-generated harmful factors

Solution Approach 1:

The system uses a neural network to validate historical samples by comparing them against current frame data, creating a feedback mechanism that identifies and corrects mismatched samples before they contribute to the final anti-aliased output, thereby eliminating ghosting while preserving temporal stability

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces the traditional motion vector-based warping mechanism with a neural network-based validation system that uses learned patterns to identify valid historical samples, substituting mechanical interpolation with intelligent validation to avoid ghosting artifacts

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If rendering at native resolution is performed, then image quality is improved, but rendering time increases significantly

Engineering Contradiction:

Improveimage qualityVSAvoidrendering time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system transitions from spatial-only anti-aliasing to temporal anti-aliasing by incorporating the time dimension, accumulating and validating samples across multiple frames to achieve high-quality anti-aliased output at lower spatial resolutions without sacrificing image quality

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Solution Approach 2:

The neural network is pre-trained to recognize valid sample patterns and is ready to validate historical samples in real-time, performing preliminary validation work that enables fast rendering at native resolution while maintaining high image quality

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12374006B2Augmenting temporal anti-aliasing with a neural network for history validation

Publication Date: 2025.07.29 INTEL CORP

AI summary

An apparatus to facilitate augmenting temporal anti-aliasing with a neural network for history validation is disclosed. The apparatus includes a set of processing resources configured to perform augmented temporal anti-aliasing (TAA), the set of processing resources including circuitry configured to: receive, at a history validation neural network, inputs for a current pixel of a current frame and a reprojected pixel corresponding to the current pixel, the reprojected pixel originating from history data of the current frame; generate, using an output of the history validation neural network, a validated color for the current pixel based on current color data corresponding to the current pixel and history color data corresponding to the reprojected pixel; render an output frame using the validated color; and add the output frame to the history data.