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| Content Provider | ACM Digital Library |
|---|---|
| Author | Singh, Shawn Kapadia, Mubbasir Faloutsos, Petros Reinman, Glenn |
| Abstract | The majority of previous crowd 'steering algorithms model each character as an oriented particle that moves by choosing a force or velocity vector. In many cases, orientation is heuristically chosen to be the same as the particle's velocity. This approach has the two key disadvantages: Limited locomotion constraints: Vector commands do not account for constraints of real human movement. Trajectories may have discontinuous velocities, oscillations, awkward orientations, or may try to move a character unnaturally, and these side-effects make it harder to animate the character intelligently. or example, a character moving forward cannot easily step to the right when its left foot is in the air (swing phase). Limited navigation control: It is common to assume that an animation system will automatically know how to interpret a vector-based steering decision. However a vector does not have enough information to indicate appropriate subtle maneuvers, such as side-stepping versus reorienting the torso, stepping backwards versus turning around, stopping and starting, planting a foot to change momentum quickly, or carefully placing footsteps in exact locations. These details are critical to depicting a character's local steering intelligence, and thus it is appropriate for steering to have better control. |
| Starting Page | 203 |
| Ending Page | 203 |
| Page Count | 1 |
| File Format | |
| ISBN | 9781450305655 |
| DOI | 10.1145/1944745.1944783 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-02-18 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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