I was interpreting the footskate cleanup &c. as extra goodies tacked on to the continuous representation of motion space. So their claim that they don’t do any preprocessing is only true in that they don’t need it to construct a path through the vector field, and that you can get a lot of constraints satisfied by weighting the Markov model rather than an explicit annotation and cleanup step.

I’m assuming that once you’ve made the motion then you can do all the post processing you want, and in fact it might be easier since you don’t have to resolve the search problem at every step.

I do have a few questions.

1. Can we nail down a definition for the word “Registration”. I’ve got a feeling of what it means from Kovar’s Thesis but it was used again in this paper and I want to make sure I know what it means in this context.

2. We’ve talked about how computer animation can be broken up into categories, one of which is example based (i.e. throw a bunch of data at the problem). It seems that the papers we are looking at are almost exclusively in this category. It seems that in each paper we read, there is a completely different method being tried and that makes it difficult to know what the standard practices are. Is there any way to categorise these papers into general methods? It would be nice to have some intuition going into a paper as to how their method is going to work at a higher level.

The only concept I was confused about on Monday was the definition of control bins, but looking back now, they are pretty simple. Control bins serve to make the continuous control space discrete, so any input that is nearby in continuous space will jump to them. This enables the construction of the control policy table in the reinforcement learning process; it is extremely simple and requires very little mathematical skills to formulate. Oh and it’s extra cool.

Hello Aaron,

Out of curiosity for the first word “Brutal” and Prof. Mike’s description in the class, I went through the paper and I found it extremely simple and requires very little mathematical skills to formulate the problem. Things are so similar to Least square formulation and expectation minimization techniques that we learn in our undergraduate classes and to understand this paper, just don’t with the fancier words and stories in the paper ( more than 30% of the paper is just a story).

This paper is EXTRA cool.

csv

The main idea of this paper seems pretty simple, they start out with a library of short motion fragments that are length restricted. They model player behavior and use it to build a controller that looks like a lookup table. One axis represents incoming signals, the other axis represents possible motions, intersection of the row and column is the output motion fragment. The main contribution is that this is an online system suitable for interactive games and optimized for immediate responsiveness over pure quality of motion. The concept that “all segments [must be able to] transform into other segments” is almost exactly like the concept of a hub in Snap Together Motion, but the difference is that they weight the trade-offs between a possible bad transition or two and better responsiveness, which is more important for gaming. I do not understand their arguments for why pruning the state space prior to planning may yield worse results. The other thing is that since 2007, I’ve actually noticed that responsive transitions between motions in games I’ve played have actually greatly improved; it looks like if you send a signal mid-motion now, the rest of the motion is accelerated to completion and the next is begun… so this paper may not be altogether relevant … but that’s just based on personal observation and not real knowledge of how this is handled nowadays.

I did not really understand a lot of the underlying optimization techniques, but am hopeful later passes over the mathematics after a bit may help to clarify them.

This technique focuses heavily on using the low-resolution properties of the animation in parameter space in order to synthesize ‘realistic’ human locomotion. This is interesting as the human cognitive system is very good at interpreting the low-resolution details of a motion, even in the periphery. While the authors present a crowd- based study of their methods, it would be interesting to extend this further into environment synthesis and I would also like to understand the discrepancies between the perception of these motions both in primary and secondary characters.

Brief information :
***************
Category : Engineering (Describes a technique to improve existing methods).
Citations Since 2006 : Unknown
Breakthrough Idea: Still searching ( mild way to say, probably Nothing).
Unfamiliar terms: Tabular-Policy based Controller.
Kolmogorov Minimum Description:
Reactive characters are important in animation to augment reality. This paper describes an improvement in “Reinforcement Learning Method” to precalculate good fragments.
Reproducibility: Probably not too hard.

First of all, the Video clip provided by the authors is totally unimpressive.

It is really hard for to evaluate this paper except that I know what authors wants and what they are doing, but compare to previous works such as motion graphs, parametrized motions etc, how superior is their approach and why existing methods can not be modified to incorporate reactive automation, is beyond my experience in these systems. To me all the “Related Work” described in the paper are more elegant and fortunately none of them use AI, which we all know that the efforts for the last thirty years can be best described in two words “Successfully failed”.

One of the disadvantages of motion graphs is that you can “move” only along the transitions (edges) and so, in interactive editing, there tends to be delay if the character isn’t at a node in the graph, which happens if the editor makes a sudden change.

Motion fields brings in the notion of continuity by representing state using three vectors:
– the post comprising the root position, orientation and the joint orientations
– the velocity, which is the difference of two poses (and hence successive frames)
– the task, comprising of task parameters for a user-specified task

They define a similarity metric (very similar to kovar et al, with the addition of the velocity vector).
The beauty of the paper lies in formulating the problem of choosing the best (or was it called near-optimal) action as a Markov model that uses rewards based on the task vector and then reducing the exponential search space using a value function that can be computed recursively and can also be compressed.
Values at states not in the db are calculated via interpolation.
So, motion fields is another data-dependent technique that provides a different representation of motion data allowing real-time user control to make the motion “flow” through the character poses.
The authors mention that “no preprocessing of data or determining where to connect clips of captured data” is needed.
How on earth is this true?
i) The footskate solution in motion fields (i.e., storing whether each foot is in contact per motion state).
This required some pre-processing didn’t it? The user had to annotate frames with foot contact in the mocap data.. isn’t automatic inference useful here?
I don’t think Kovar et al needed the user to annotate the mocap data for footskate clean up.
Also, there isn’t the notion of a post-processing state in this paper. It seems like everything is done on the go.
ii) how are value functions calculated “w/o any preprocessing”? The ability to compress them comes via calculating them earlier, didn’t it?

other points worth discussing:
i) non-parametric vs parametric models in motion and their consequences
ii) ideas to reduce the space of searching for similarity (whatever be the metric)
iii) what are other ways (than markov modelling) to achieve near-optimal decisions?
iv) blending in motion fields v/s motion graphs; no time-warping in motion fields?

This was very dense mathematically and I didn’t understand most of it.

What I did gather, was that they attempted to perform statistical analysis an a motion database to reduce motion down to the form x = M(a,g) where a specifies space and g specifies time. By warping these two values, different motions can be Synthesised. I don’t know what MAP nor do I understand the statistical methods in question.

At first I didn’t think this was all that interesting of a paper, but the pictures at the end, with the sketch interface looked really cool and if animation can be made that intuitive, this paper probably deserves a closer read later.

McCann and Pollard present a method that offers the capabilities of motion graphs but as well as the responsiveness needed for real-time applications. The method uses machine learning to build a predictive model for control input/signals which correspond to short motion fragments. This probability model is determined from training data that is collected from the input streams of normal gameplay. In addition to the probability model, the controller uses a quality heuristic, which selects a motion fragment that provides the best motion quality (jerky or smooth blend) and control quality (incorrect or correct action) evaluated over n time steps in the future. A continuous stream of motion fragments (0.1 seconds in length in the authors scenario) are played and selected fast enough to make seemingly instantaneous response possible.

This paper directly addresses my concerns with the Kovar’s motion graphs; the responsiveness seemed too limited to make them feasible for use in game. I think the underlying ideas of motion graphs are definitively the next step in improving the interactivity of video game characters, so it was really exciting to find that this technology is in the works.

I am confused about the definition of control bins. The authors say that they are regions of space in the high-dimensional space of control signals, which are closest to hand selected points. What are these points and what are these regions? Some examples would really help…