Using Deep Learning to improve FIFA 18 graphics
Game Studios spend millions of dollars and thousands of development hours designing game graphics in trying to make them look as close to reality as possible. While the graphics have looked amazingly realistic in the last few years, it is still easy to distinguish them from the real world. However, with the massive advancements made in the field of image processing using Deep Neural Networks, is it time we can leverage that to improve the graphics while simultaneously also reducing the efforts required to create them?
Let us try to answer that using the game FIFA 18…
Football (i.e. soccer) being my favorite sport, FIFA becomes the natural game of choice for all of my deep learning experiments. To find out whether the recent developments in deep learning can help me answer my question, I tried to focus on improving the player faces in FIFA using the (in?)famous deepfakes algorithm. It is a Deep Neural Network that can be trained to learn and generate extremely realistic human faces. My focus in this project lies on recreating the player faces from within the game and improving them to make them look exactly like the actual players.
Note: Here is a great explanation of how the deepfakes algorithm works. Tl;dr version: it can swap the face of anyone in a video with anybody else’s face using Autoencoders and Convolutional Neural Networks.
Gathering training data
Let us start by looking at one of the best designed faces in FIFA 18, that of Cristiano Ronaldo, and see if we can improve it. To gather the data required for the deepfakes algorithm, I simply recorded the player’s face from the instant replay option in the game. Now, we want to replace this face with the actual face of Ronaldo. For this, I downloaded a bunch of images from Google such that the images clearly show his face from different angles. That’s all that is needed to get us started with the training process of our model.
Model architecture & Training
The deepfakes algorithm involves training of deep neural networks called autoencoders. These networks are used for unsupervised learning and have an encoder that can encode an input to a compact representation called the “encoding”, and a decoder that can use this encoding to reconstruct the original input. This architecture forces the network to learn the underlying distribution of the input rather than simply parroting back the input. For images as our input, we use a convolutional net as our encoder and a deconvolutional net as our decoder. This architecture is trained to minimize the reconstruction error for unsupervised learning.
For our case, we train two autoencoder networks simultaneously. One network learns to recreate face of Ronaldo from FIFA 18 graphics. The other network learns to recreate the face from actual pictures of Ronaldo. In deepfakes, both networks share the same encoder but are trained with different decoders. Thus, we now have two networks that have learnt how Ronaldo looks like in the game and in real life.
When training using a pre-trained model on other faces, the total loss goes down from around 0.06 to 0.02 within 4 hours on a GTX 1070. In my case, I continued training on top of the original CageNet model that has been trained to generate Nicolas Cage’s face.
Using the trained models to swap faces
Now comes the fun part. The algorithm is able to swap faces by adopting a clever trick. The second autoencoder network is actually fed with the input of the first one. This way, the shared encoder is able to get the encoding from FIFA face, but the decoder reconstructs the real face using this encoding. Voila, this setup just converted the face from FIFA to the actual face of Ronaldo.
Results
The GIF below shows a quick preview of results from running this algorithm on faces of other players. I think the improvement is astonishing, but maybe I am biased, so you be the judge.
More such results in video format can be found on my YouTube channel, with the video embedded below. Please subscribe to my channel if you like the video.
Can we use this algorithm to put ourselves in the game?
What if you could play “The Journey” mode of the game as yourself instead of playing as Alex Hunter? All you got to do is upload a minute long video of yourself and download the trained model in a few hours. There you go, you may now play the entire Journey mode as yourself. Now that’d be some next level of immersive gaming!
Where it excels and where it needs more work
The biggest advantage I feel we get with this approach is the amazing life-like faces and graphics that are hard to distinguish from the real world. All of this can be achieved with only a few hours of training, compared to years taken by game designers with the current approach. This means game publishers can come out with new titles much faster rather than spending decades in development. This also means that the studios can save millions of dollars that could now be put into hiring decent story-writers.
The glaring limitation so far is that these faces have been generated post facto, like CGI in movies, while games requires them to be generated in real time. However, one big difference is that this approach does not require any human intervention for generating results once a model has been trained, and the only thing holding it back is the computation time required in generating the output image. I believe it is not going to be very long before we have light weight, not-too-deep generative models that can run very fast without compromising output quality, just like we now have YOLO and SSD MobileNets for real-time object detection, something that wasn’t possible with previous models like RCNNs.
Conclusion
If someone like me, who has no experience in graphics designing, can come up with improved faces within just a few hours, I truly believe that if game developers were to invest heavily in this direction it could change the face of gaming industry (yes, intended) in the not-too-distant future. Now if only anyone from EA sports was reading this…
Note: This is a repost of the article originally published with towardsdatascience in 2018.
