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

[Don Brutzman]

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 discussion 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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