Neural History Validation for Temporal Anti-Aliasing Ghosting

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Solution Overview

Problem

Temporal Anti-aliasing (TAA) techniques in graphics processing units suffer from ghosting artifacts due to frame-to-frame visibility changes 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 and improving image quality while reducing render time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If TAA is used to generate temporally stable anti-aliased images, then image quality is improved, but ghosting artifacts appear around moving object boundaries due to frame-to-frame visibility changes and motion vector errors

Engineering Contradiction:

Improveanti-aliasing qualityVSAvoidghosting artifacts

Core Design Contradiction:

Manufacturing precisionVSObject-affected harmful factors

Solution Approach 1:

A neural network validation stage is introduced as an intermediary between the TAA accumulation stage and the final output. This validation network examines accumulated samples and identifies mismatched history samples caused by visibility changes or motion vector errors, allowing selective discarding of invalid samples to reduce ghosting artifacts while preserving the temporal anti-aliasing benefits

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback by using the neural network to analyze the quality of accumulated samples and provide validation information back to the rendering pipeline. This feedback mechanism allows the system to adjust which historical samples are used for accumulation, improving accuracy by eliminating samples that would cause ghosting artifacts

Inventive Principle:

Principle #23Feedback

2Manufacturing precision

If rendering is performed at native resolution with TAA, then image quality is improved, but render time increases

Engineering Contradiction:

Improveimage qualityVSAvoidrender time

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The neural network validation stage applies partial action by selectively validating only the necessary historical samples rather than processing all possible samples. This approach maintains high image quality by validating critical samples while reducing overall processing time by avoiding exhaustive validation of every sample in the accumulation buffer

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260024249A1Augmenting temporal Anti-aliasing with a neural network for history validation

Publication Date: 2026.01.22 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.