Difference: MocapResequence (13 vs. 14)

Revision 142006-03-08 - DanielEaton

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META TOPICPARENT	name="CPSC526ComputerAnimation"

Animation using Motion Resequencing and Blending

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This paper presents a reasonably cool idea and explains some interesting issues. I like their explanation of why not to use a simple vector norm as a difference metric between frames. However, they deal with the problem of affine invariance by considering only rotation along one axis (the y axis), which greatly restricts the allowable translations (though makes the system actually solvable since it permits a closed solution). The graph pruning description is also interesting. Future work could possibly look at more interesting ways of creating transitions (e.g., throw an IK solver into it so that a run could transition to a backflip motion and the character would know to bend his knees... similar to motion doodles). Section 4.2 was a bit humourous; 10 paragraphs to explain a fancy way of doing exponential exploration. -- KenRose

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The output of this system is cool. I wonder if it could be integrated with motion doodles for 3D to simplify the notation (though, the running time of their algorithm would probably be prohibitive. Some criticisms: in section 3.1, it seems to me that choosing a k (window size) would be nontrivial, as it sets the resolution of transitions. Perhaps it would be better to choose a range of k's? (Or maybe I've misunderstood) I think a great limitation of this system is that the mocap data needs to be labelled, so it's not truly automatic (that said, my project is in the "statistical models" section, which almost always demands labelled data, so I guess I can't criticize!). In computing g (section 5.1) wouldn't you also pay attention to facing direction in addition to distance from the line? -- DanielEaton - 08 Mar 2006

Precomputing Avatar Behaviour From Human Motion Data

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I found this paper a little confusing, mostly because I read the papers in the wrong order (Motion graphs second)! The authors' usage of active learning terminology was also confusing (I doesn't seem like they're using value iteration, but rather something based on the stochastic approximation -- at least in the section titled "computing control policies"). I like that this paper was honest about the limitations -- especially where they point out that for it to extend, there will need to be some intense human labelling of mocap data (of physical interactions) or many algorithms for detecting very specific features of motion. -- DanielEaton - 08 Mar 2006

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Reinforcement learning in games and computer animations is a hot topic. I've seen many researches that leverage neural network and genetic algorithms in this area. While not too many statistical methods and dynamic programming approaches have been presented for specific problems in computer animation. Microsoft Research Cambridge once did a project to enhance NPC's skills in a fighting game using Q-Learning, and the results seem to be plausible. Anyhow, there might be some more interesting problems in this area that can be solved by well specified machine learning techniques and to identify them is a hard but interesting work. Using precomputing methods is a good approach to current video game. The demo of this paper is more persuasive than the other's. In addition, this paper provides some interesting referred literatures that are helpful to my AI course project. --Zhangbo(Zephyr) Liu

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