These sorts of data sets can be useful for graphics research, particular as a data set to test ray tracing algorithms on.
See for example, the Moana Island data set. [1]
I definitely foresee papers on BVH construction using this scene.
For graphics research in academia, there's a dearth of real-world data sets like this, so the ones that do get released are gold. And for graphics research in industry, one may have access to good internal data sets for development and testing, but getting permission to publish anything with them tends to be a giant hassle. It's often easier to just use publicly available data sets. Plus, that makes it easier to compare results across papers.
Since they provide player movement data, you can train a transformer to predict which player will win the BR given movement patterns. Or maybe create "player embeddings" to see if player behaviors can be clustered. That could be a fun project...but definitely not useful.
Extracting and converting the player data from the .usd files would not be fun, though.
> Since they provide player movement data, you can train a transformer to predict which player will win the BR given movement patterns.
You didn't consider the main factor for CoD - cheating. Which clearly seems to be an inside thing.
Not sure if anything meaningful can be obtained by analyzing anything that has player data on it considering every video game out there is prone to this.
They are implying player teleporting, which is a common hack in BRs.
Player movement data that is too fast for normal players could be seen as cheating. An AI isn't strictly needed for that, just check displacement over time.
Is it really a common hack? I would have guessed teleportation is the easiest to detect server-side, or impossible from the start as the server is authoritative (clients sends inputs, the server computes the positions and any important change, sends them back to clients, clients cannot hack their movement).
Given all the other variables that introduce a bunch of noise to the player movement data, I doubt you could ever determine any useful predictive pattern.
If anything though, I could see how player behavior of match winners could be used to both identify varying level of cheaters and players that use various methods for providing an advantage (i.e., keyboard mouse, joystick extensions, etc) and automatically sequester or even handicap their accounts.
It appears to me that so much effort is placed on trying to identify and hamper cheaters in real time, when that both seems extremely resource intensive and unnecessary, considering you have all the digital evidence proof of cheating you need after the fact, you just have to understand what you are looking at.
> so much effort is placed on trying to identify and hamper cheaters in real time, when that both seems extremely resource intensive and unnecessary, considering you have all the digital evidence proof of cheating you need after the fact, you just have to understand what you are looking at
It's not resource intensive at all compared to the alternative of ahaving humans doing post match reviews. It's all "AI" and automated reviews because it's cheaper. Half of the "anti-cheat" tactic is anyway using your computer resources to run some anti cheat tool.
These games are optimized for revenue so every action is dictated by that. Including catching/banning cheaters. If it costs too much to do it properly, or (and this is actually plausible) cheaters are a significant enough portion of the already small chunk of players who create recurring revenue, then there's no incentive to take real action.
This data is probably useful for actual academic rather than practical purposes today. They're building the knowledge they might want to use in a few years.
>Given all the other variables that introduce a bunch of noise to the player movement data, I doubt you could ever determine any useful predictive pattern.
Predicting a winner will be difficult but I would not be surprised if you could loosely predict rank (does Warzone track player rank?) off of movement alone. You may be able to predict more accurately by looking at the associations between two players and their movement. From my prior experience in FPS games, positioning, awareness, and aim are the core pillars of success. Unfortunately as far as I can tell from the data set, only player position is tracked.
See for example, the Moana Island data set. [1]
I definitely foresee papers on BVH construction using this scene.
For graphics research in academia, there's a dearth of real-world data sets like this, so the ones that do get released are gold. And for graphics research in industry, one may have access to good internal data sets for development and testing, but getting permission to publish anything with them tends to be a giant hassle. It's often easier to just use publicly available data sets. Plus, that makes it easier to compare results across papers.
[1] https://www.disneyanimation.com/resources/moana-island-scene...