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The use of motion capture data for computer animation will depend on
solving problems in two key areas: producing a convincing single
motion and producing a convincing transition between motions. In terms
of motion capture these two tasks seem to be somewhat separate.

The first task is primarily concerned with the raw motion capture data
and cleaning it for use. Artifacts from the data need to be eliminated
and the motion smoothed so it looks fluid and natural. It also deals
with the concept of synthesis, where many similar motions can be used
to produce a single blend that meets some set of requirements – be
they emotional content or mechanics of limb placement.

The second task is important as a single motion is rarely sufficient
for any purpose. A character’s actions consist of many motions, some
sequential and others concurrent. While motion capture sessions could
be planed to demonstrate all the motions the character needs, rarely
can every action be planned to such detail ahead of time. Techniques
are required to merge multiple motion capture clips together and to
splice them sequentially, all while maintaining realism. This requires
maintaining constraints and physical laws such that any transition,
while not preformed explicitly by the actor, will have the appearance
of being possible to do so.

In many ways, these two task overlap. It is difficult to get good
blending if the starting motions are poorly conditioned for the
scene. Thus well constructed, and with prior knowledge of needed
transitions, motions need to be made for the entire action to come off
successfully. However, depending on the final purposes, the ‘weight’
of these two tasks may be different. For interactive character
controls, like in a game, very good sequences and transitions are
important as the player will be often switching between motions while
playing. If the transitions are poor, they will be obvious to a viewer
who sees them done repeatedly. On the other hand, other areas may be
less concerned with the transition quality, but more focused on the
quality of the individual motions. Medical analysis and physical
therapy probably cares more about the action of rotating a single
damaged joint rather than blending the motion with another.

In the constraints of this area, I am fairly curious about the work
Perlin did with the noise based animation. On one hand, the demos look
strangely appealing – probably due to the natural seeming randomness
of the actions. No motion looks quite like any other, which is
characteristic of a biological system moving. On the other hand, I
wonder if the technique could be improved by combining it with motion
capture data. As with many other applications of noise, perhaps it
would reach its peak if used as a surface decoration rather than the
meat of the animation. This may be a viable alternative to using large
databases of motion to construct motion blends. A limited set of
simple motions could be enhanced with noise to create the effect an
animator is looking for.

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Also, I read this paper last semester when I implemented a Cyclical Coordinate Descent method of IK, and I don’t think its in the IK list. I don’t know if you have seen it, but I thought it was a useful overview of techniques. Its a bit old, but I don’t think that matters too much.

Welman, C. “Inverse kinematics and geometric constraints for articulated figure manipulation.” Simon Fraser University, 1993.
http://lib-ir.lib.sfu.ca/bitstream/1892/7119/1/b15233406.pdf

I am very curious about how specifically to handle the scalability issues presented by the data. My initial sense from reading these papers is that the scalability issue is hard and we don’t currently have a ‘killer’ way of tackling the issue. Not only that, but finding the key trick for rapidly generating natural motion from both mocap databases and algorithmic procedures could provide insight into doing rapid calculation and approximation (i.e. ‘faking’ what is natural) for other computational issues as well.

I felt that the challenges discussed in 2000 paper generally echoes the Eurographics paper with the exception of the solutions for providing more control over motion capture data by motion graphs and similar techniques. This is surprising out of context given how much work has gone into understanding how to effectively animate the human form; however, like most of the elements cited in the Catmull paper as being essential to the development of animation as a field, it still is an open question as it is so difficult to model and humans are so used to seeing real human motion in everyday life that it is perceptually really hard to fake.

2. I am very curious about the specifics involved in parameterization, defined in the second paper as the method of converting intuitive control parameters into the animation parameters defined by whichever animation technique is currently being used. It seems to me to be a very hard problem, yet very important, to be able to present the actual animator with controls that are both intuitive and as orthogonal as possible.

3. I read the draft chapter of Animation from Observation, and it seemed to me to be a very good introduction to the large fundamental issues at hand, all of which seem to still be quite relevant. One interesting note you made was on how there is quite a tension between traditional animators and motion capture technicians and users, stemming from unrealistic expectations of what motion capture can achieve and animators having a difficult time working with the data produced by motion capture. I’m curious as to what extent this tension still exists today?

I was wondering how motion capture data is actually used in an animated film. What about the face of a character? Is that motion captured as well or animated – how to decide that?

In film, a big problem is how transfer the motion of the actors to the characters that will ultimately be displayed. If the characters do not have the same proportions as the actor, how do we deal with that? Even if that is the case, there is no way that we can hope to capture all of the subtle details that the human body makes, so will missing these be a problem?

In games the motions can’t be planned ahead of time, and therefore they need to be generated on the fly. If we restrict what the character can do, perhaps we can construct (both manually and automatically) sets of motions that will be enough for the purposes of the game. However, this tend to make things look repetitive, or even unnatural. As stated in one of the readings, it is not practical to just include huge libraries of examples either, so how do we deal with that?

In medical analysis, the fidelity of the data is extremely important. However, every type of sensing carries with it a certain degree of uncertainty. How can we make sure that this uncertainty will not lead to wrong medical conclusions?

With all of these problems however, I think that what most interests me is something in between the game and film example. How can we use synthesis by example to make it possible for a novice (aka me) to create professional grade animations. Is that goal even possible?

I am curious to learn more about the different ways that the motion data is combined and how several different sources are sometimes used to make a smooth transition. Another interesting issue seems to be the splicing together of different upper and lower motions at the same time

2) At this point, I’m most interested in what I mentioned above, real-time generation/blending of motions that reflect a certain style. If we are to have believable, realistic characters in games (my primary interest), then we need to have their motions communicate a dynamic state of mind.

3) In the intro to your Mocap book, you say that video processing technology is a long way off from being able to determine the movement of someone in clothing. The authors of the paper “Video-based Reconstruction of Animatable Human Characters” present methods that make this appear feasible. Although, if I remember correctly, I think it took six hours to process one video sequence…Thus this is still a problem, but perhaps its solution is not as far off as once thought.

The motion system in Spore is what seems most promising to me: it tackles the issues by doing everything procedurally (well, not really, but that’s the claim). Monsters with 12 legs will move reasonably well, ditto with 8 legged creatures or creatures with caterpillar legs. Of course it is very easy to play around with this system and break it, and all of the motions generated are exaggerated and unnatural (somewhat intentionally so). But I think the fact that it’s possible to set up an a posteriori real time motion scheme is promising.

The Gleicher paper I think makes a convincing argument that advances will need to be revolutionary, not evolutionary. We’re pretty close to surmounting the uncanny valley when it comes to human-looking characters, but we’re still a long ways off from human-moving characters, except in limited contexts. We’ve gotten a little bit better at making “cartoony” human motion: n.b. the hand-tweaked mo-cap canned motions in games like Resident Evil 5: in its intended context the motion doesn’t look “realistic,” but it is compelling and fits the setting (“realistic” in a Walt Disney way, maybe). But much like Spore, it’s easy to break the system by choosing odd angles and other parameters to begin the canned motions. By erring on the side of realism and control, they prevented spontaneity, the flipside to Spore’s design decisions.

I wonder how much of these decisions about where to fall in this vast continuum of motion choices are made. We have an idea of how game designers skimp on things like graphics or storytelling or voice acting, but how do designers skimp on motion? How can people do it well when the skill set needed to implement a motion system can drastically change based on what compromises are made? How much compartmentalization and movement occurs between the keyframe+mo-cap animators working on cutscenes and animated films, and the programmers in the trenches making motion for gameplay?

For things like film the actions are known beforehand, but generally the quality of the animation needs to be far superior than what is generally considered acceptable in a game. In this case the primary problem is recording examples that accurately depict what the director would like the motion to be, a matter of control. Perfecting it is a time consuming process reliant on the actors ability to carry out the directors instructions. Automating this process without sacrificing the realism/naturalness of the motion would be desirable.

2. I’m curious how well motion capture data has been used to generate a model based approach to animation, would it generalize well to new actions while retaining the style directed in the capture of the examples?