Phase-Functioned Neural Networks for Character Control

we present a real-time character control system using a novel neural network structure called a face function neural network character control is the problem of mapping from a set of control variables such as a desired trajectory to character motion which is both natural and it's used to give an input data driven methods such as ours approached his problem using a large database of motion data and attempting to learn such a mapping with machine learning current data-driven methods have real-time character controls such standard neural networks and recurrent neural networks may end up mixing data from different phases resulting motion that looks stiff and unnatural or producing floating artifacts when the phase is ignored completely other models such as Auto regressive Gaussian processes have a habit of overfitting producing motion which is noisy and unstable we present a new neural network structure that can achieve character control in a way that avoids these issues while still producing expressive natural motion and a number of complex situations including traversing rough terrain crouching and jumping the phase function neural network is a neural network where the weights are not learned directly but instead produced via a separate function which takes as input the phase of the data this creates a regression function which involves smoothly over time and is different for each phase separating the phase out of the global variable avoid explicitly mixing data from different phases while allowing the face to change all the network weights at once gives it a much larger influence in their regression resulting in a system which is fast compact stable and can react well to complex control tasks such as navigating rough terrain to train our system we first capture several long sequences of raw locomotion data at a variety of speeds facing directions and turning angles we also capture motion of stepping climbing and running over obstacles placed in the capture studio since simultaneously capturing motion and geometry is difficult we use a simple procedure to fit to range from a separate database of height maps to the previously captured motion data once the data is acquired we extract a number of parameters relation to the character control and train the phase function neural network to produce the corresponding output we now show some of our results here we show our character performing locomotion in the planar environment the future trajectory direction and speed can be adjusted by the user using a gamepad by changing the facing direction to match the camera direction we can also produce sidestepping and walking backwards by giving as imp at the height of the terrain under the trajectory our character can adapt to rough terrain climbing balancing and jumping we're required you this process works even when the character is jogging you by adjusting an input variable indicating the height of the ceiling either using the gamepad or the environment we can get the character to crouch or move under obstacles sometimes the game designer wishes for the character to traverse the environment in a special way by labeling parts of the environment with an additional variable the character can be told to act differently for example jumping over obstacles instead of stepping over them since I method is data-driven the character doesn't simply play back a jump animation it adjusts its movements continuously based on the height of the obstacle by colliding the future trajectory with walls and other objects in the environment the character will avoid things in its path and slow down to prevent collisions you it can additionally be forced into certain environments such that the desired motion is performed as shown walking over this beam you in summary we present a new method to control using a faes function neural network which can produce high-quality motion complex controlled tasks such as walking over rough terrain it's fast compact stable and can learn from a large amount of data

Channel: Yoshiboy2

Views: 1,015,683

Keywords: Neural Networks, Deep Learning, Character Animation, Phase Functioned Neural Networks, Machine Learning, Animation, Character Control

Id: Ul0Gilv5wvY

Length: 4min 50sec (290 seconds)

Published: Sun Apr 30 2017

Reddit Comments

[u/Merdo86]

This was from 2 yr ago, I remember it.I am sure its integrated in anvilnext 2.0

edit: I also remember this tech was bought by Ubisoft. I am %100 sure some part of this neural AI are working in Ac Odyssey and Origins

[u/TucoBenedictoPacif]

For the record For Honor already implements an early form of this tech and improvements are being made on a steady pace across the industry.

Try to check the youtube channel “Two minutes papers” about advancements in AI, machine learning and neural networks.

[u/DaBi5cu1t]

I don't play them but this should make football games much more realistic, or all sports games for that matter

[u/B-Knight]

It's nice looking but it also resembles the animation from Red Dead 2. Incredibly realistic, incredibly good looking but man is it sluggish. It actually feels like your character weighs a ton.

I await the day they properly fix this issue.

[u/Ketonax]

Wow, looks fantastic. I just wonder what are the CPU utilization costs of running this in games, is it very demanding? This could save tons of scripted movement paths etc.

[u/Solemn2000]

Is assassins creed using this? Movement in that game does not look hand made, but this demo is definitely superior to what I’ve seen in origins and odyssey.

[u/[deleted]]

I understood all the things he said.

[u/CrispyDuchess]

I'll be really impressed when I see your character or A.I. model trip at random times just to inconvenience you.

[u/Blubberibolshivek]

dosent star citizen have something similar to this?