5.1 Conclusion
We present a realtime horse locomotion system using manually capture motion data to generate a wide speed range of horse motions. When a user changes our horse advancing speed, our system will automatically select the proper gait for our horse to use and deal with the transition between gaits. We also plan the root trajectory in realtime. Controlling our horse translation and rotation. To achieve this result, we manually capture a series of Eadweard Muybridge’s horse locomotion photographs in MAYA. We also propose an asynchronous time warping method to translate motions between gaits. Our horse uses Walk, Amble, Trot, Rack, Canter and Gallop at different speeds. In every time frame we determine if the horse’s legs are standing on the ground and compare the horse’s moving distance to last time frame. Using the moving distance of the stance legs we can calculate the translation of horse root. When turning, we preserve the reality of the original motion. In the results, we also exhibit the motion of horse leaping. Furthermore, we modify our turning system to let our horse climbing up and walking down a slope. In the end, we change our horse skeleton to short legs and long legs to examine the effects.
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5.2 Limitation and Future work 48
5.2 Limitation and Future work
Our system manually captures animal locomotion through MAYA. Although this is an effective way to capture horse motion, it is not a procedural method. We need to spend a lot of time for every motion. In the future, we should improve our motion capture system to the automatic generating animal locomotion data via videos. In that case, we could capture more sets of mo-tion data in a short time. Abe et al [1] proposed an optimizamo-tion based algorithm which can let the captured motion satisfy high-level user constraints while preserving physical realism. Their method simulate a volleyball slam example by using a realtime inverse control mechanism. Af-ter a user specifies the position of the player’s hand in mid-flight, the system can deAf-termine the correct linear interpolation of the sampled motions to meet the positional constraint on the hand.
We could use their method to adjust our system to let users determine the height of the leaping motion. In the results, we present the motion of a horse climbing up and walking down a slope.
Johansen [17] has proposed a method to analyze foot movement. He introduces a concept of a footbase, which is a constraint that combined heel and toe to retain the information about the alignment of a foot relative to the ground. His method could predict the place where the feet should land on the ground; calculate the trajectories and alignments of the feet when taking steps; and adjust the legs and hip height to accommodate the new foot position. In the future, we would like to add this function into our system so that our horse can not only changing direction but also walking on different terrains. We will also apply our system to simulate other quadrupedal locomotion, e.g. lions, pigs or elephants. Furthermore, we hope our system can be extended to handle multi-pedal locomotion. In the end, we will be able to build a zoo with different kinds of animals walking arbitrarily in it.
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