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You can also go down the rabbit hole of using ML in animation. It's really a gamechanger. [0] https://montreal.ubisoft.com/en/introducing-learned-motion-matchi

You can also go down the rabbit hole of using ML in animation. It's really a gamechanger. [0] https://montreal.ubisoft.com/en/introducing-learned-motion-matching/ https://montreal.ubisoft.com/en/introducing-learned-motion-m... [1] https://cascadeur.com/help/autoposing https://cascadeur.com/help/autoposing

lasagnaphil 6y ago
Motion matching isn't really ML by the way, it just does simple pose searching from a motion database every frame with a heuristically designed cost function. And the problem is that it requires a good amount of raw motion capture data beforehand to exceed the quality of traditional hand-made state machines, and as a result is mostly viable for huge game companies with dedicated motion capture studios and professional actors. (For most indies, even just renting out a motion capture studio for their game is out of the picture.) In order to really get the benefits of it, you'll have to plan out something called a "dance card" for your actors (https://www.youtube.com/watch?v=_Bd2T7uP9VA https://www.youtube.com/watch?v=_Bd2T7uP9VA), which are a sequence of actions designed to sufficiently capture enough range of the motions that the character can take. And you also need some manual labeling/tagging of those motions afterhand in order to really get good transitions. The resulting animations can be incredibly good (such as the one we see in games like For Honor and The Last of Us 2), but for most indies and small gamedev studios this isn't really economically viable, and a state machine will probably get you far enough. And learned motion matching is when you simply approximate a motion matching controller using a neural network to save memory and computation time (if you've read the paper). But it requires you to already have a well-made motion matching controller beforehand, so I don't know if it's a technology that can be applied to the majority of gamedevs.
gfxgirl 6y ago
> If you have an animation of walking and one for drinking, Motion Matching will not be able to produce a scene where the character is walking while drinking. Motion Matching will only play back the data you provide it. ML in animation doesn't actually do anything useful according to friends in production at Ubisoft. As the paper says itself > Naturally, searching a larger database means poorer runtime performance too. This is why we say Motion Matching scales poorly in terms of data. It's a neat experiment but ATM AFAIK has not been used in a production title because of its limits.

lasagnaphil 6y ago

Motion matching isn't really ML by the way, it just does simple pose searching from a motion database every frame with a heuristically designed cost function. And the problem is that it requires a good amount of raw motion capture data beforehand to exceed the quality of traditional hand-made state machines, and as a result is mostly viable for huge game companies with dedicated motion capture studios and professional actors. (For most indies, even just renting out a motion capture studio for their game is out of the picture.) In order to really get the benefits of it, you'll have to plan out something called a "dance card" for your actors (https://www.youtube.com/watch?v=_Bd2T7uP9VA https://www.youtube.com/watch?v=_Bd2T7uP9VA), which are a sequence of actions designed to sufficiently capture enough range of the motions that the character can take. And you also need some manual labeling/tagging of those motions afterhand in order to really get good transitions. The resulting animations can be incredibly good (such as the one we see in games like For Honor and The Last of Us 2), but for most indies and small gamedev studios this isn't really economically viable, and a state machine will probably get you far enough. And learned motion matching is when you simply approximate a motion matching controller using a neural network to save memory and computation time (if you've read the paper). But it requires you to already have a well-made motion matching controller beforehand, so I don't know if it's a technology that can be applied to the majority of gamedevs.

gfxgirl 6y ago

> If you have an animation of walking and one for drinking, Motion Matching will not be able to produce a scene where the character is walking while drinking. Motion Matching will only play back the data you provide it. ML in animation doesn't actually do anything useful according to friends in production at Ubisoft. As the paper says itself > Naturally, searching a larger database means poorer runtime performance too. This is why we say Motion Matching scales poorly in terms of data. It's a neat experiment but ATM AFAIK has not been used in a production title because of its limits.