Currently, there are a lot of weapon skins in Valorant to choose from. A Valorant player was able to use neural networking to change the weapon skin in a video.
Valorant Weapon Skin deep fake
Riot Games have been maintaining a constant drip-feed of new weapon skins with unique looks and animations in Valorant. There are two primary ways to get a weapon skin. The in-game store lets players purchase either individually or as a part of a bundle collection. A recent addition, Night market, offers some skins at a discount.
The second way is via the battle pass. Every new act introduces a premium battle pass, which includes three sets of weapon skin collections as a reward for completing it.
Recently Redditor u/swegmesterflex posted his experience of replacing the vanilla weapon skin with a Singularity version in a small captured clip. He did it with the CycleGEN model. It is similar to replacing faces or part of a video using deep fakes.
The gun looks close to indistinguishable. However, there is a small shimmering shape around the gun model. This process does not include unique animations, as they are part of the weapon skins themselves.
This idea sparked a discussion of whether it is possible to replace the gun model in real-time, in-game. Redditor u/Darkfyre42 says that even though training a neural network in real-time is impossible, it might not be far-fetched to use an already trained neural network to replace a gun model. However, there would be a limitation of predetermined skin to be used.
Author u/swegmesterflex also adds that without quantizing, it might not be possible to train in real-time. However, it is possible to inference with neural networking in real-time, similar to Ray-tracing, Deep Learning Super Sampling, and RTX Voice. He further adds:
u/swegmesterflex is currently looking for more gun models to have a larger dataset to draw from. He plans to develop an app, which anyone can download and change the gun model in their clips.
What u/swegmesterflex is developing might not be useful in-game. However, this experiment certainly pushes the boundary of machine-learning, neural networking, and technological development overall.
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