IEEE P3079.3/D02, Dec 2022
IEEE Draft Standard for a Framework for Evaluating the Quality of Digital Humans
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- Language: English
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About This Item
IEEE P3079.3/D02, Dec 2022 is a draft IEEE standard for a framework for evaluating the quality of digital humans in computing and processing contexts. It provides a technical basis for assessing how convincingly a digital human performs, responds, and appears across intended use cases, which matters for development, testing, and comparison. By focusing on evaluation rather than creation, IEEE P3079.3/D02, Dec 2022 helps define a more consistent way to review quality and readiness.
About IEEE P3079.3/D02, Dec 2022
This draft standard is aimed at establishing a structured framework for quality evaluation of digital humans, a subject that sits at the intersection of computing, processing, and interactive media systems. IEEE P3079.3/D02, Dec 2022 is relevant where teams need common criteria to examine realism, behavior, and functional performance in digital human implementations. As a draft document, it is best viewed as guidance for technical discussion, review, and early-stage alignment on measurable quality factors.
Where is IEEE P3079.3/D02, Dec 2022 used?
IEEE P3079.3/D02, Dec 2022 may be used in software and system development work involving digital human avatars, virtual assistants, immersive interfaces, simulation platforms, and other computer-generated human representations. It is useful in workflows where teams need to evaluate output quality, compare versions, or validate whether a digital human behaves as intended. The standard may also support procurement and internal review when organizations need a clearer technical basis for assessing digital human systems.
Importance in practice
In practice, a framework for evaluating digital human quality helps reduce subjectivity in design and testing. IEEE P3079.3/D02, Dec 2022 can support more consistent assessment across teams by giving reviewers a shared reference for performance, realism, and functional suitability. That consistency is valuable when selecting tools, comparing implementations, or tracking quality over time. For organizations developing or integrating digital humans, it can also help lower risk by making evaluation criteria more transparent and repeatable.
- Draft framework for quality evaluation
- Digital human performance and realism
- Testing and review consistency
- Computing and processing applications
- Support for technical comparison and alignment
- Publication Date: 2023
- Standard Status: Inactive
- Publisher: IEEE
- Subject: Computing and Processing
- Official IEEE: Doi link
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