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So, after many hours, over many months I finally got the Sample plugin to compile. First let me state that I am new to C++ programming. I started programming back in the mid to late 70's on an Apple I (Integer/Applesoft BASIC) in high school and a SWTP 6800 with an IQ terminal (Hex op code) at home. A few more languages (Cobol, RPG, Assembler, other Basic flavors) and moved on to other endeavors. Tried learning C, or maybe it was C++ (Borland), but I never got into it. I started again, quite a few months ago, with the assistance of a few others who had in recent years started their journey, to learn C++. One reason was to dabble in and experiment with the creation of plugins for AM. I now have a list of cleanup tasks on this project, then compare it to the C++ Sample Plugin project file, available to download and the existing info/tutorials on it to perhaps bring it UpToDate with the available Microsoft Visual Studio (2026). Which is what I used to compile it. I also have to get it working for 32bit systems. Hopefully that will be as simple (😁) as changing the configuration of the project in VS, but we will see.

Okay, what's going on here? In trying to model some curved splines and patches I had GPT create a treasure chest (not particularly successful). A treasure chest needs gold coins though right? So, had GPT made stacks of gold coins. Successful but time consuming to tweak and rerun with variations. So, what to do? Answer: Have GPT create a python program to create stacks of gold coins with easily adjusted settings. Many of the variables were informed by my failures to create good looking stacks of gold coins. For instance, if too close on top of each other the stacks look too much like long tall objects. Even though the coins have random shades of orange and yellow. So, need some distance between coins vertically. Perfect stacks horizontally don't look good either. So how about a 1 in 10 chance the coin will go in the same direction as the last coin placed? Etc., Etc. I started with GPT creating clusers of stacks with 1000 coins total. Not the best starting demo and we want the user to set all those numbers as well (min and max for stacking etc. too) Here's Take 1 results out of the python program that replicated the basic process GPT was using. Not bad. Save out a file with the settings for that (in case we want to recreate the same or similar set of coins (seed value allows us to get same results with random numbers) Try 1000 coins (the programs current max count) as these models are being generated immediately and... As each coin/object has it's own group we can grab any coin we want and adjust. Don't want to stack coins? Point to a different model, such as a sheet of paper. These processes are pretty good at plussing up the Duplication WIzard. Which reminds me. I didn't add an option for rotating each object as it is placed.

After having GPT extend the buildings, storefronts and street with cars another 10x wide I set up a few cameras and ran through the scene. The 'helicopter shot': storefront_helicoptershot.mp4

I'm wondering why a children's book needs to look Noir. If i had to get a dozen images for a children's book... I'd call Rodney! Rodney is very clever doodler. He makes great-looking characters. If it needed to be hi-contrast I'm sure he could do that too. (I hope it's OK that I showed these )

I'm certainly cheating a bit by generating models with flat planar surfaces but... again... method to madness. Had GPT add books to the bookcase and while it did it the first time the books looked more like boxes because of how wide they were. Second attempt: This is the point where I've introduced GPT to the concept of Group Folders. Before we were getting a lot of benefit out of Named Groups but organizing Groups into Folders will be even more useful. Specifically, the set of groups that is used just for generative modeling won't be very useful to the users so those groups can be placed in their own Folder. It is there where temporary data can be stored as well. Consider for instance where the group might store instructions and once a task is successfully completed that task/group is moved from one Group Folder to another. Groups specifically used for targeting to apply bones, decals, materials, etc. Some rules likely need to be devised in order to determine what groups can be removed without effecting the look of the model and which are essential.

Something I think of importance: Note how GPT has named all of the various surfaces of the model as Groups. This is useful on many levels but primarily for identification. If we were to tell GPT to connect SeatTop to the SeatFrontFace it might not actually do it correctly but at least it has named those parts so it knows what they are. Thus giving it a fighting chance of success. From a users perspective it saves a lot of time performing manual identification.

Short answer, every hair is like three or more CPs whose position needs to be recalculated on every frame, with motion and collisions and stiffness and weight, etc taken into account. A head of hair will easily be more points to track than most character models typically have.

GPT is struggling with the understanding of Hooks (although making some progress). It would be good to feed some information about Hooks from the A:M SDK. Of note: Animation:Master does a very good job of repairing models where it can. Where I am currently with Hook testing is getting GPT to generate the model, A:M identifying the error upon loading, and A:M fixing the error to best of its ability. In some cases this requires manual fixing to take the model to its intended state. (although not in my latest test as A:M fully repaired the problem) I then feed back the repaired model to GPT (along with any error messages) so it can compare the good model with the bad and make appropriate adjustments. Here's an example of a generated mesh that A:M repaired upon opening

This is an important step. Without providing any specific guidance on how to extrude horizontally GPT correctly extruded 10x to the right. Such a minor thing right? But I love those 1 shot successes.

software is great and there is good helpers

Still a long... long way to go but getting there little by little.

Here's an interesting fail: