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1. Technical Specification 2. Reference Software 3. Conformance Testing 4. Performance Assessment

1. Technical Specifications

Technical Specification: Portable Avatar Format (MPAI-PAF) V1.6, jointly with other MPAI Technical Specifications, provides technologies for the digital representation of 3D Model Data that enable the Avatar-Based Videoconference Use Case, a form of videoconference held in a Virtual Environment populated by speaking Avatars. The Use Case is implemented using four Modules, each an AI Module (AIM) executed in an AI Framework specified by Technical Specification: AI Framework (MPAI-AIF) V3.0.

Table 1 lists the four AIMs implementing the Modules of the Use Case. Links lead to the specifications of the AIMs. They include the AIM Functions, Reference Model, and I/O Data and, for each AIM it is composed of, Functions, I/O Data, and links to the AIMs and JSON metadata.

Table 1 – AIMs implementing the Modules of the Avatar-Based Videoconference

Acronym Names and Specifications of AIMs JSON
PAF-VCT Videoconference Client Transmitter X
PAF-VMS Virtual Meeting Secretary X
PAF-AVS Avatar Videoconference Server X
PAF-VCR Videoconference Client Receiver X

Figure 1 depicts the Avatar-Based Videoconference system composed of four Modules.

Figure 1 – Avatar-Based Videoconference end-to-end diagram

The components of the PAF-ABV system:

  1. Participant: a human joining an ABV either individually or as a member of a group of humans in the same physical space.
  2. Audio-Visual Scene: a Virtual Audio-Visual Environment populated by Visual Objects such as a Table, chairs etc., and Audio Objects. The Environment is described by Audio-Visual Scene Descriptors.
  3. Portable Avatar: an MPAI-PAF-specified Data Type including data representing a human participant.
  4. Videoconference Client Transmitter (VCT):
    • At the beginning of the conference, VCT:
      • Receives from Participants and sends to the Server Portable Avatars containing Avatar Model(s) and Language Selectors.
      • Sends to the Server Speech Object and Face Object for Authentication.
    • Continuously updates the Server with Portable Avatars containing Avatar Descriptors and Speech.
  5. The Avatar Videoconference Server (AVS)
    • At the beginning of the conference, AVS:
      • Selects specific Audio-Visual Descriptors, representing the meeting room.
      • Equips the room with objects, e.g., table and chairs.
      • Places Avatar Models around the Table with a specific Spatial Attitude.
      • Distributes Portable Avatars containing Audio-Visual Scene Descriptors, Avatar Models, their Speech Objects and Spatial Attitudes to all Videoconference Client Receivers.
      •  Assigns IDs to Avatars after authenticating Speech and Face Objects.
      • Sets the common conference language.
    • Continuously:
      • Translates and sends to Participants Speech according to their Language Selectors.
      • Sends Portable Avatars containing Avatar Descriptors, Speech, and Spatial Attitude of Participants and Virtual Meeting Secretary to all Receiving Clients and Virtual Meeting Secretary.
  6. Virtual Meeting Secretary is represented as an Avatar but does not correspond to any Participant and continuously:
    • Uses the common meeting language.
    • Understands Text Objects and Speech Objects of all Avatars and extracts their Personal Statuses.
    • Drafts a Summary of its understanding of Avatars’ Text Objects, Speech Objects, and Personal Statuses.
    • Sends the Summary outside of the Virtual Environment for participants to read and edit directly, and/or displays the Summary ihe Visual Space for Avatars to comment, e.g., via Text Objects.
    • Refines the Summary based on comments received.
    • Sends its Portable Avatar containing its Avatar Descriptors to the Server.
  7. Videoconference Client Receiver (VCR):
    • At the beginning of the conference, the VCR:
      • Receives Audio-Visual Scene Descriptors and Portable Avatars containing Avatar Models with their Spatial Attitudes.
    • Continuously:
      • Receives Portable Avatars with Avatar Descriptors and Speech.
      • Produces Visual Scene Descriptors and Audio Scene Descriptors.
      • Renders the Audio-Visual Scene by spatially adding the Avatars’ Speech Objects to the Spatial Attitude of the respective Avatars’ Mouths.
        Rendering may be done from a Point of View, that is possibly different from the Position assigned to their Avatars in the Visual Scene. This is selected by participant who use a device of their choice (Head Mounted Display or 2D display/earpad) to experience the Audio-Visual Scene.

Each component of the Avatar-Based Videoconference Use Case is a Module: an AIM, composed of other AIMs, executed in an AI Framework. The specification of each such AIM includes the following elements:

1 Functions of the AIM The functions performed by the AIM implementing a component of the Use Case.
2 Reference Model of the AIM The Topology of the AIMs composing the AIM.
3 Input and Output Data of the AIM Input and Output Data of the AIM.
4 Functions of the composing AIMs Functions performed by the composing AIMs.
5 Input and Output Data of the composing AIMs Input and Output Data of the composing AIMs.
6 AIMs and JSON Metadata Links to summary specification on the web of the AIMs and corresponding JSON Metadata [2].

#2. Reference Software

As a rule, MPAI provides Reference Software implementing the Technical Specification released with the BSD-3-Clause licence and the following disclaimers

  1. The purpose of the Reference Software is to demonstrate a working Implementation of an AIM, not to provide a ready-to-use product.
  2. MPAI disclaims the suitability of the Software for any other purposes than those of the MPAI-PAF Standard, and does not guarantee that it offers the best performance and that it is secure.
  3. Users shall verify that they have the right to use any third-party software required by this Reference Software, e.g., by accepting the licences from third-party repositories.

Note that at this stage only part of the AIMs required to operate the Modules of the Use Case have a Reference Software Implementation.

3. Conformance Testing

An implementation of an AIM conforms with MPAI-PAF if it accepts as input and produces as output Data and/or Data Objects (Data of a Data Type and its Qualifier) conforming with those specified by MPAI-PAF.

The Conformance of an instance of a Data is to be expressed by a sentence like “Data validates against the Data Type Schema”. This means that:

  • Any Data Sub-Type is as indicated in the Qualifier.
  • The Data Format is indicated by the Qualifier.
  • Any File and/or Stream have the Formats indicated by the Qualifier.
  • Any Attribute of the Data is of the type or validates against the Schema specified in the Qualifier.

The method to Test the Conformance of a Data or Data Object instance is specified in the Data Types chapter.

4. Performance Assessment

Performance is a multidimensional entity because it can have various connotations. Therefore, the Performance Assessment Specification should provide methods to measure how well an AIM performs its function, using a metric that depends on the nature of the function, such as:

  1. Quality: the Performance of a Videoconference Client Transmitter AIM can measure how well the AIM represents the human Participant.
  2. Bias: Performance of a Videoconference Client Receiver AIM can reproduce the avatar videoconference.
  3. Legal compliance: the Performance of an AIM can measure the compliance of the AIM to a regulation, e.g., the European AI Act.
  4. Ethical compliance: the Performance Assessment of an AIM can measure the compliance of an AIM to a target ethical standard.

Note that at this stage MPAI-PAF AIMs do not have a Performance Assessment Specification.

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