4.0 – ML for Procedural Animation

3D animation poses many challenges for a machine learning system. In games, it is often not possible nor feasible to pre-animate every possible action for a character and how the character should transition between the actions. This is an area where a lot of research has been done. Following the same idea, animation can be a slow process, and systems that can increase an animators productivity can be equally as useful.

4.1 – Animation Blending

I was looking for, and hoping to find some machine learning applications for animation blending (i.e. connecting animations and poses in a natural, fluid way). However, it seems that this type of work is easier with, and better suited for, procedural-type algorithms. A good algorithm / system that is well implemented provides a good solution for blending animation and doesn’t generally require any of the benefits that might be gained by using a learning system (improvement over time, improvement from user feedback, etc.).

4.2 – Style-Based IK  System

Grochow, Martin, Hertzmann and Popović created a system which could very well make animators a lot more efficient in a production environment. Their style-based system works by learning segments of animation as a ‘style’ (e.g. a baseball pitch). The learning system uses the animation data from the pitch as input and maps the probability of various poses based on the poses within the captured data. An animator can then animate variances of this style using a very small number of IK handles and the system can interpolate natural poses and animation from the style space (Grochow, Martin, Hertzmann and Popović, 2004). Therefore, instead of moving tens or hundreds of individual controls on a character rig, animators can collect a library of  styles for this system and use them to produce realistic animations very quickly.

This is a supervised learning system, as it requires training data to create style spaces and starts with knowledge of what the data representsdw.  The algorithm is called a Gaussian Process model, and it essentially maps input x to a y and then uses what’s called it’s kernal function to map the similarities between them. This Kernal Matrix along with the GP mapping is used by the learning algorithm to actually map the 2d space which is used to extrapolate/interpolate other natural poses for variant animations. From what I gather the learning algorithm optimizes the 2d space to fit the given data in a way that allows easy and real-time creation of new poses (Grochow, Martin, Hertzmann and Popović, 2004).

4.3 – Motion Capture Pose Segmentation

Another interesting use for machine learning with respect to animation is in motion capture. I though this research was interesting to mention because of the section on the style-based IK system. Using some pattern recognition and machine learning processes, we can create a system which automatically looks for and divides motion capture data into distinct actions and poses (Barbič, Jernej, et al., 2004). This type of system integrated with the style-based IK system would create an extremely fast and efficient animation pipeline in a production environment.

References

Keith Grochow, Steven L. Martin, Aaron Hertzmann, Zoran Popović. Style-based Inverse Kinematics. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2004), 2004.

Barbič, Jernej, et al. “Segmenting motion capture data into distinct behaviors.”Proceedings of the 2004 Graphics Interface Conference. Canadian Human-Computer Communications Society, 2004.