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Last month, Google The GameNGen AI model is Generalized image diffusion technique Can be used Generates a playable version without any issues DoomNow, researchers are using similar techniques in a model called MarioVGG to see if AI can generate lifelike videos. Super Mario Bros. Depending on user input.
Results Mario VGG Models-Available As a preprint paper Published by a crypto-related AI company Virtual Protocol—It still displays many obvious glitches and is too slow to approach real-time gameplay. But the results show that even with a limited model, it’s possible to infer impressive physics and gameplay dynamics by simply studying a bit of video and input data.
The researchers hope that this is a first step towards “fabricating and demonstrating a reliable and controllable video game generator” or even in the future “using video generative models to completely replace game development and game engines.”
Watch 737,000 frames of Mario
MarioVGG researchers (GitHub users) used Ernie Choo and Brian Lim (listed as contributors) Public Datasets of Super Mario Bros. Gameplay with 280 “levels” worth of input and image data tuned for machine learning (level 1-1 was removed from the training data so its images could be used for evaluation). Over 737,000 individual frames from this dataset were “pre-processed” into chunks of 35 frames to allow the model to learn what the immediate results of different inputs typically look like.
To “simplify the gameplay landscape,” the researchers decided to focus on only two potential inputs in the dataset: “run right” and “run right and jump.” But even this limited set of moves posed some challenges for the machine learning system, as the preprocessor had to look back several frames before the jump to determine when the “run” began. Jumps that included mid-air adjustments (i.e., the “left” button) also had to be excluded because they “introduced noise to the training dataset,” the researchers wrote.
After preprocessing (and about 48 hours of training on a single RTX 4090 graphics card), the researchers ran a standard Convolution and Noise Reduction The process of generating new video frames from a static game-start image and text input (in this limited case, “run” or “jump”). The generated sequences only last for a few frames, but the last frame of one sequence can be used as the first frame of a new sequence, allowing the researchers to create gameplay videos that show “consistent, coherent gameplay” regardless of their length.
Super Mario 0.5
Even with all these settings, MarioVGG still doesn’t produce smooth video that’s indistinguishable from the actual NES game. For efficiency, the researchers scaled the output frames down from the NES’s 256×240 resolution to a much blurrier 64×48, and condensed 35 frames’ worth of video time into the seven generated frames, distributed at “uniform intervals,” creating a “gameplay” video that’s much grainier than the actual game output.
Despite these limitations, the MarioVGG model currently struggles to even come close to real-time video generation: On the single RTX 4090 the researchers used, it took a full six seconds to generate a six-frame video sequence (just over half a second of video) even at the very limited frame rate. The researchers acknowledge that this is “impractical and difficult to use for interactive video games,” but they hope that future optimizations of weight quantization (and perhaps the use of more computing resources) will improve this rate.
But with these limitations in mind, MarioVGG is able to create a fairly believable video of Mario running and jumping from a static starting image. Genie Game Maker by GoogleThe model was even able to “learn the physics of the game solely from the video frames in the training data, without any explicitly hard-coded rules,” the researchers wrote. This included inferring behaviors like Mario falling off the edge of a cliff (with realistic gravity) and (usually) halting forward movement when he was adjacent to an obstacle, the researchers wrote.
While MarioVGG was focused on simulating Mario’s movements, the researchers found that the system could effectively hallucinate new obstacles for Mario as the video scrolled through the imagined level. These obstacles are “consistent with the game’s graphical language,” the researchers wrote, but currently cannot be influenced by user prompts (such as placing a hole in front of Mario and having him jump through it).
Just make it up
But like other probabilistic AI models, MarioVGG has a frustrating tendency to sometimes produce completely useless results, meaning it ignores user input prompts (“it has been observed that input action text is not always followed,” the researchers wrote). Hallucinations Obvious visual glitches: Mario will land into obstacles, walk through obstacles and enemies, flash different colors, shrink/expand every frame, or disappear completely for multiple frames before reappearing.
One particularly ridiculous video shared by the researchers shows Mario falling off a bridge, turning into Chee-Che, then jumping back over the bridge and transforming back into Mario — something you’d see from Wonder Flower, not the original AI video. Super Mario Bros.
The researchers speculate that training for a longer period with “more diverse gameplay data” would address these significant issues and allow the model to simulate more than just running right and jumping. Still, MarioVGG serves as a fun proof of concept that even with limited training data and algorithms, you can create a decent starting model of a basic game.
This story originally Ars Technical.
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