[x3d-public] [x3d] annotating X3D models to indicate production with AI tools

[John Carlson]

Interesting points Don.  What do we for joint mappings, where all the
joints are provided  by humans, but the AI rearranges the data so there are
joint correspondences?  Do you know any human who wants to do this for 50+
joints?

John

On Sat, Aug 30, 2025 at 11:47 AM Don Brutzman <don.brutzman at gmail.com>
wrote:

> Web3D Consortium members have suggested creation of a new working group in
> an important, high-impact area of endeavor.
>
>    - Web3D Consortium: AI with X3D
>    - This preliminary charter serves as a fodiscussion document for the
>    Special Interest Group on AI with X3D, outlining its purpose, objectives,
>    and operational framework. The group is committed to advancing the
>    intersection of AI and 3D graphics, fostering innovation, and promoting
>    collaboration among its members and the broader community.  If X3D models
>    include metadata and common best practices that support AI training and 3D
>    model generation, powerful new capabilities might arise.
>    - https://www.web3d.org/working-groups/ai-x3d
>
> Here are two interesting new references that may significantly help our
> preparations to work towards use of AI in concert with X3D and HAnim.  They
> look to be similarly compatible and authoritative with respect to existing
> X3D (and HTML5) practices for applying document metadata standards such as
> Dublin Core.
>
> 1.  Internet Engineering Task Force (IETF) which governs Internet
> standards has proposed an interesting Request for Comments (RFC).
>
>    - AI Content Disclosure Header
>    - Abstract. This document proposes a machine-readable Hypertext
>    Transfer Protocol (HTTP) response header field, AI-Disclosure, to disclose
>    the presence and degree of Artificial Intelligence (AI) generated or
>    AI-assisted content in web responses. The header is designed for
>    compatibility with HTTP structured field syntax and provides metadata for
>    user agents, bots, and archiving systems. It supports layered disclosure
>    strategies alongside human-readable and structured metadata formats.
>       - Workgroup:Independent Submission
>       - Internet-Draft:draft-abaris-aicdh-00
>       - Published:30 April 2025
>       - Intended Status:Informational
>       - Expires:1 November 2025
>       - Author: D. Abaris, Individual Contributor
>    - https://www.ietf.org/archive/id/draft-abaris-aicdh-00.html
>
> 2. Coalition for Content Provenance and Authenticity (C2PA)
>
>    - Advancing digital content transparency and authenticity.  Explore a
>    global content provenance and authenticity standard called Content
>    Credentials.
>    - Content Credentials function like a nutrition label for digital
>    content, giving a peek at the content’s history available for anyone to
>    access, at any time.
>    - The Coalition for Content Provenance and Authenticity, or C2PA,
>    provides an open technical standard for publishers, creators and consumers
>    to establish the origin and edits of digital content. It’s called Content
>    Credentials, and it ensures content complies with standards as the digital
>    ecosystem evolves.
>    - https://c2pa.org
>
> Excerpt from specification prose:
>
>    - Content Credentials : C2PA Technical Specification
>    - 18.16. Asset Type, 18.16.1. Description
>    - The asset type assertion provides a way to more completely describe
>    an asset, specifically additional context on how to parse or otherwise
>    process it. Although both claims and ingredients are required to include a
>    valid (IANA Media Type) in the dc:format field, there are many file formats
>    for assets that cannot be completely described by a single Media Type value.
>    - NOTE. As C2PA is adopted to provide provenance for AI/ML (i.e.,
>    artificial intelligence/machine learning) assets in the future, the C2PA
>    Manifest can be embedded in the model and dataset assets, and the asset
>    type assertion used to specify the type of these model and dataset assets.
>       - Table 11. Asset type values
>       - C2PA Type; Description of C2PA Type of the Asset
>       - *c2pa.types.dataset;* AI/ML dataset which can be processed by
>       multiple AI/ML frameworks or is not described by any other value
>       - etc.
>    -
>    https://spec.c2pa.org/specifications/specifications/2.1/specs/C2PA_Specification.html#_asset_type
>
> These approaches look like an excellent way to distinguish X3D models that
> are prepared for AI LLM training, or produced by AI LLM tools.
>
> I recommend that these considerations be added to the draft Web3D AI with
> X3D group charter.
>
> I will work further to try integrating these concepts further in our X3D
> metadata documentation.  They are likely also relevant in future
> specification efforts for the X3D Architecture, draft version 4.1.
>
>    - X3D Scene Authoring Hints: meta Statements
>    -
>    https://www.web3d.org/x3d/content/examples/X3dSceneAuthoringHints.html#meta
>
> all the best, Don
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