The first link exposes the trick employed by your model.
>animation info and simulation data
but did your model learn any of that?
>explicit goal of the video you posted is to combat runtime constraints
The trick to motion mapping is feeding a lot of data with accompanying inputs to build an atlas you can reference during playback.
>first large-scale GAN successfully trained on GTA V
Its really cool. The problem I had is in the presentation. I immediately felt insincerity bordering on scamming the audience, because I assume someone working in this field would know how the sausage is made. From the YT clip: "the shadow and reflection works", "modeling of physics works". Do they? or did your model build an atlas of video frames it can play back according to the fed input? Im guessing weather/time of day was locked when recording training data - perfect shadow and constant sun position for a nice reflection. Searching for 1:1 matches of generated output in the training set would be interesting and pretty revealing.
>animation info and simulation data
but did your model learn any of that?
>explicit goal of the video you posted is to combat runtime constraints
The trick to motion mapping is feeding a lot of data with accompanying inputs to build an atlas you can reference during playback.
>first large-scale GAN successfully trained on GTA V
Its really cool. The problem I had is in the presentation. I immediately felt insincerity bordering on scamming the audience, because I assume someone working in this field would know how the sausage is made. From the YT clip: "the shadow and reflection works", "modeling of physics works". Do they? or did your model build an atlas of video frames it can play back according to the fed input? Im guessing weather/time of day was locked when recording training data - perfect shadow and constant sun position for a nice reflection. Searching for 1:1 matches of generated output in the training set would be interesting and pretty revealing.