3/27 Modeling People from Photos

I had to come a half hour late to this lecture, so unfortunately I didn't get too much out of it since I missed all the explanation in the beginning. I did find it interesting that for interpolating between photos, instead of interpolating directly (which can lead to unwanted states in the middle) they interpolate to neutral in between the beginning and ending states. I really liked that they did that - it's such a simple idea, and it makes a huge difference. I think that really shows the answer isn't always a complicated one.

3/22 Practical Character Physics

This research involved taking animated sequences and adjusting them to be more physically plausible. I think it's a great idea, and in some cases significantly improved the believability of a motion. I also really liked that the artist can decide whether or not to make the adjustments - if a character has a super power or something, he might not always move in physically accurate ways. I think it's really important to give the artist these stylistic choices. Also, I think this was a really great example of how important a character's interactions with its environment are. In the video, it would show an animated sequence, and then show the same sequence with the adjustments made. However, the character was usually just in empty space, and most of us barely noticed a difference. Even though the clip showed the different paths, the motions, at least to my eyes, barely looked different at all. I think a lot of what makes an animation look physically possible or not possible has to do with how objects interact with each other, so it was disappointing for me to see the video and the characters not interact with anything!

3/20 Simulating Balance Recovery

We saw a few videos where characters were walking and had to react and keep their balance after being hit. Some were hit from behind, some from the front; some were merely pushed by an invisible force, and one was even pelted with dodge balls. Some characters did look like they were reacting to being hit, whereas I felt like others looked as if they were trying to avoid being hit, dodging the oncoming forces rather than reacting to them. These motions are pretty similar physically, so what made them look different to me? I don't know. But I find it really interesting how people can differentiate between two extremely similar motions by their intention. When we see a character move, most of the time we are pretty good at guessing what they were trying to do with that motion (so long as the motion is convincing). I don't know what could have been changed in the videos we saw for me to see the correct motions of the characters - reacting vs dodging - but I think it has to do with the fact that, in a simulation, there's really no way to code for a character's intention. Not that I can think of, anyway. You code the simulation to carry out a certain motion, but what if people do that same motion for different reasons? Which reason will be the one audiences see?

3/6 Catching Fly Balls

I found today's topic to be particularly interesting, probably because I'm a huge baseball fan (go Dodgers!). As I mentioned in class, I think the reasoning behind this research is extremely applicable and important, even if actually catching fly balls isn't animated as often as, say, walking. But I think it's really important to realize that searching for the optimal motion is almost always going to be the wrong motion, at least when you're talking about humans. What people do, and what looks natural, is hardly ever optimal. So for this example, people don't immediately move to where the ball is going to land - they adjust. This actually reminded me of a computer game I used to play back in elementary school called Backyard Baseball, so I looked it up to see how the characters caught the ball. Of course, they were pretty clumsy because all of the characters are supposed to be kids, and I think when you're playing the outfield you just click on the character you want to try to catch it. So I thought this game might have a good example of this type of research, but it's so basic (from the YouTube videos at least) that it doesn't seem to have much in common besides baseball. Here's a video, though, in case you want to check it out. I used to play this all the time with my little brother!

Anyway, I was wondering if the research noted any differences between left- and right-handed players, or if they even used test subjects with different dominant hands. When we saw the graph showing where the players walked when catching the balls, it was separated into four quadrants depending on where the ball was landing. The front and back didn't look too different, but I noticed the paths differed from each other more on one side than the other. I thought this might be due to whether the player was left- or right-handed, but it didn't seem like the paper mentioned this at all. It would be interesting to see how a person's dominant hand affects their motions.

3/1 Stochastic Character Animation

I really liked the idea of this paper, being able to create different versions of the same action. I think this is important because we don't all do the same things the exact same way, and if you have one "stock" version of an action, not only is it repetitive but it might not look right on different characters. However, I didn't think it was necessarily successful in creating natural motions. The motions, for the most part, looked physically possible, but sometimes didn't look at all like what someone might naturally do. For example, in the video, there was a character swimming breast stroke, which looked pretty normal. Then we saw the output from this algorithm to reproduce the breast stroke, and it looked so bizarre to me! Yes, the motions were certainly physically plausible, but the timing between the arms and legs looked very strange - not like how someone who really knew how to swim would do. There's an important distinction between what motions are possible and what motions are natural, and this seems to be a problem a lot of people run into. How do you put constraints on your algorithm to only create natural movements? All the time we see outputs that are physically possible, but no one would ever actually do the actions that way. To a computer, they're all the same. I think this is what makes all of this stuff so difficult! There's really no way to separate out the awkward, unnatural motions in your algorithm.