[x3d-public] Fwd: [x3d] MSF Plenary #18 Slide Set

[GPU Group]

John,
> 1. Who is going to pay for AI training?
- great question.
There are little computers Nvidia Jetson Orin Nano Developer (check Amazon)
for 400 to 1500 $
- they are loaded with Nvidia's AI stack for peripheral AI, linux OS, and
various 3rd party have 'foundation models' for various things
- One option for funding: there could be a little club, where those
who want to provide the tool to their users, would chip in (note 1)
- the data sets - web3d.org has lots of data sets / x3d example scenes, and
the images for them can be generated with an x3d-browser (note 2)
> 2. Is there a foundation model we can use?
- great question.
- presumably the Nvidia stack comes with some basics, and 3rd parties might
have some refined models
- AlphaFold by DeepMind: AI used to solve protein folding, used the 2 step
confirmation method, where the AI would give a best guess, then a chemical
bond checking algorithm would confirm or reject its logic and feed that
back for refining the AI training, (I read somewhere, so it must be
possible to do that.)

Note 1: there could also be a shared 'resource pull tool' for club members,
and public downloaders would see a screen with
tool-or-resource-name [Install / Uninstall / Update / Discontinued ] button
for each tool or resource. So someone downloading freewrl, would get the
pull tool that allows pulling sunrise, x3dom server, castle model viewer,
x3d-edit, ....., and the Update button would be lit when there's a new
version.
Note 2: complex shapes like HAnimHumanoid would be easier to train AI for
if instead of training against a mesh, it was training against simplified
scalar parameters, like MakeHuman / MPFB extension for Blender. There could
be an abstract x3d type HanimGenerator, and a concrete type
HAnimHumanoidGenerated that derives from it and HAnimHumanoid, and on
rendering, it generates the skin Shape and textures, and coveroids /
clothing based on parameters. Since AI trains on the scenefile --not the
runtime scenegraph-- it would only need to learn a few dozen slidebar
parameters and control them to generate a humanoid that looks like the
training image. This would allow x3d authoring tools to tinker with the
humanoid looks with slidebars, and allow x3d-browser events to change the
look of the Humanoid on-the-fly / at runtime.
-Doug

On Thu, Nov 13, 2025 at 3:43 PM John Carlson <yottzumm at gmail.com> wrote:

> That sounds good, Doug.  Questions
>
> 1. Who is going to pay for AI training?
> 2. Is there a foundation model we can use?
>
> On Thu, Nov 13, 2025 at 1:47 PM GPU Group via x3d-public <
> x3d-public at web3d.org> wrote:
>
>> "AI assisted content creation"
>> Suggestion for web3d: train AI to generate x3d content:
>> a) from photos
>> b) from textual description
>> Since chatGPT can generate images from textual description, its image
>> output (b) could be combined with x3d-specific (a) photo to scenefile
>> generation:
>> textual scene description > chatGPT > images > select image > feed image
>> to (a)
>> To train for x3d-specific (a) a 2-step process is used:
>> 1. pairs of (input,output) = (x3d-browser-rendered image, x3d scenefile)
>> are fed to the AI training system to train it
>> 2. then AI used with input image to generate a scenefile, and the
>> resulting scenefile is checked using an automated tool to confirm or reject
>> on xml and web3d spec conformance, and the result used to reinforce /
>> correct train the AI further
>> -Doug
>>
>>
>> On Thu, Nov 13, 2025 at 11:36 AM Joe D Williams <joedwil at earthlink.net>
>> wrote:
>>
>>> > MSF plenary, can you point everyone to slides?
>>>
>>> Here is the slide set for the MSF Plenary November 12 2025
>>>
>>>
>>>
>>> https://docs.google.com/presentation/d/1-zOzU38yRLasbth0JSd_9PqtJEjEiumTtLOj1q_tw7g
>>>
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>
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