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We present a novel radial-view-based culling method for continuous self-collision detection (CSCD) of skeletal models. Our method targets closed triangular meshes used to represent the surface of a model. It can be easily integrated with bounding volume hierarchies (BVHs) and used as the first stage for culling non-colliding triangle pairs. A mesh is(More)
The radial view-based culling (RVBC) method has been presented for continuous self-collision detection to efficiently cull away non-colliding regions. While this technique mainly relies on the segmented clusters of the reference pose and the associated fixed observer points, it has several drawbacks during the animation and the reduced cost of executing(More)
Horse locomotion exhibits rich variations in gaits and styles. Although there have been many approaches proposed for animating quadrupeds, there is not much research on synthesizing horse locomotion. In this paper, we present a horse locomotion synthesis approach. A user can arbitrarily change a horse's moving speed and direction and our system would(More)
Generating rising up motions is an important problem but has less been addressed in computer animation. This problem is challenging as rising motions involve complex motor skills and exhibit wide varieties due to various lying postures and environments. In this paper, we present an approach that utilizes motion planning and dynamics filtering to produce(More)
Figure 1: (a) (b) The clusters are fixed in the radial view based culling (RVBC) method at runtime. (c) (d) Our method dynamically merges the atomic clusters to improve the overall performance of continuous self-collision detection over RVBC. The speedup of our method compared to RVBC is 5.2× in this octopus example. Abstract The radial view-based culling(More)
This paper proposes a cross recovery scheme to protect a group of 3D models. The lost or damaged models can be reconstructed using the mutual support of the survived authenticated models. In the encoding phase, we convert a group of n given models (called host models) into n stego* models. The n stego* models would still preserve the appearance of the n(More)
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