Animation using Motion Resequencing and Blending

Motion Graphs

Comments & Questions:

It presents a new way to re-assemble the captured data to create a realistic, controllable motion. But two problems are presented: 1. Not completely automatic. The special motions must recognized and labeled before. 2. Considering large database, the search part will be time-consuming even with parallelization. Especially in the time-critial application like game, it is the downside refering to the paper result. --jianfeng tong

As for the creating of transitions, smooth transition between frames might not give a pleasing motion; after all, it is interpolated between global positions and joint angles. So I am wondering how general this method could be? In addition, when extracting motion from a large graph, both efficiency and the acceptable final motion would be the concerns, even if user provides enough guidance. -- Steven Chang

An interesting paper. The model the authors described in this paper is more suitable for designing the games. So some AI concepts, such as reward function, are used in their algorithm. As a game, real-time and performance are the most important things need to be considered. In order to improve the performance, the authors adopted pre-compiled motion graph. By using the reward functions to control the actor's responses, the motion of the actor can't be forcasted. It makes the game more playable. The system is easy to extend by adding more reward functions. As an example used in the paper, a boxing game, all the motions are a little similar, so the transition from one clip to another can be easily handled. --Bo Liang

Although the authors listed several potential applications, I doubt motion graph can be easily imported into video games--either for non-player characters or for players. Realistic motion is definitely desirable, but in real time games: first, players and NPCs are acting every second which may cost much computation power if we use this kind of technique to every character; second, again, many imaginary characters appear in those virtual worlds which makes the original motion capture data not so easy to be used, let alone the modified data. Anyway, this paper also reminds me a question in our last assignment. --Zhangbo(Zephyr) Liu

(Motion Graphs) An interesting idea. It would be interesting to compare their detection of candidate transitions method with the Sederberg algorithm (Time Warping) that I presented on Monday. In the "Path Synthesis" section, it was not clear to me how they handle rotating the motion (if any); i.e. when two identical motions needs to be concatenated to follow a specific path (which requires rotating the original motion). If I understood correctly they do not handle that and will only use sub-portions of existing data in order to follow the path. In that case – wouldn’t it be relevant to support orientation changes of an existing motion? -- Hagit Schechter

Precomputing Avatar Behaviour From Human Motion Data

Comments & Questions:

The actual definition of an action is unclear to me. Is it a whole motion, or just some part of a motion? Also, I could use some clarification of the details of training/reward system. Scalability in terms of memory sounds like it might be a problem if you wanted the avatars to handle a wider variety of abilities. -Christopher Batty

Compared with the other paper, I am more interested in the precomputed control. It has a significant improvement in performance over the search. Precomputed control and runtime synthesis is a great combination to satisfy the requirement of time and a certain range of control. jianfeng tong

Compared with Lee's paper, this model is more suitable to be used in making a movie. You can define an error function, then the system will render a shore movie clip as you expected. So the performance is not as important as Lee's model. Maybe this is the reason the autor didn't use a pre-compiled motion database. The motions used to construct the motion graph are multiplicate, so the transition between the different clips are difficult to handle. --Bo Liang

Using dynamic programming to choose a pose on each iteration makes a lot sense, which would give more desirable result than with the method used in “Motion graph” to pick a target motion. Since I am lacking of knowledge on reinforcement learning and autonomous agents, further elaboration on that would be of help. -- Steven Chang

Using reinforcement learning to allow animated characters to come up with motions is a very neat idea. The authors mentioned that they added in some small actions and allowed for randomness in the model by allowing these actions to be chosen even when they aren’t optimal. Would it not be better if the authors used a HMM instead of a fully observable Markov Model? This would allow for the output states to have a probability distribution that could be randomly sampled and also keep the model more close to reality. -Disha Al Baqui

(Pre-Computing Avatar Behavior) I find the merge of machine learning and computer graphics quite interesting. I see the potential of actually using the paper's suggested technique for video games, but the paper lacks in my view a thorough discussion of the usability issue. Another question that comes to my mind is whether the reinforcement learning technique used in the paper also work for other scenarios where two concepts needs to be learned at the same time. For example, a two people dance. -- Hagit Schechter