Collision detection is a vital task in almost all forms of computer animation and physical simulation. It is also one of the most computationally expensive, and therefore a frequent impediment to efficient implementation of real-time graphics applications. We describe how graphics hardware can be used as a geometric co-processor to carry out the bulk of the computation involved with collision detection. Hardware frame buffer operations are used to implement a ray-casting algorithm which detects static interference between solid polyhedral objects. The algorithm is linear in both the number of objects and number of polygons in the models. It also requires no preprocessing or special data structures.

In the animation of deformable objects, collision detection is crucial for the performance. Contrary to volumetric bodies, the accuracy requirements for the collision treatment of textiles are particularly strict because any overlapping is visible. Therefore, we apply methods specifically designed for deformable surfaces that speed up the collision detection. In this paper the efficiency of bounding volume hierarchies is improved by adapted techniques for building and traversing these hierarchies. An extended set of heuristics is described that allows pruning of the hierarchy. Oriented inflation of bounding volumes enables us to detect proximities with a minimum of extra cost.

Image-space techniques have shown to be very efficient for collision detection in dynamic simulation and animation environments. This paper proposes a new image-space technique for efficient collision detection of arbitrarily shaped, water-tight objects. In contrast to existing approaches that do not consider self-collisions, our approach combines the image-space object representation with information on face orientation to overcome this limitation. While

Figure 1: Tree with falling leaves: In this scene, leaves fall from the tree and undergo non-rigid motion. They collide with other leaves and branches. The environment consists of more than 40K triangles and 150 leaves. Our algorithm, FAR, can compute all the collisions in about 35 msec per time step. We present a reliable culling algorithm that enables fast and accurate collision detection between triangulated models in a complex environment. Our algorithm performs fast visibility queries on the GPUs for eliminating a subset of primitives that are not in close proximity. To overcome the accuracy problems caused by the limited viewport resolution, we compute the Minkowski sum of each primitive with a sphere and perform reliable 2.5D overlap tests between the primitives. We are able to achieve more effective collision culling as compared to prior object-space culling algorithms. We integrate our culling algorithm with CULLIDE [8] and use it to perform reliable GPU-based collision queries at interactive rates on all types of models, including non-manifold geometry, deformable models, and breaking objects.

We present a fast collision culling algorithm for performing interand intra-object collision detection among complex models using graphics hardware. Our algorithm is based on CULLIDE [8] and performs visibility queries on the GPUs to eliminate a subset of geometric primitives that are not in close proximity. We present an extension to CULLIDE to perform intra-object or selfcollisions between complex models. Furthermore, we describe a novel visibility-based classification scheme to compute potentiallycolliding and collision-free subsets of objects and primitives, which considerably improves the culling performance. We have implemented our algorithm on a PC with an NVIDIA GeForce FX 6800 Ultra graphics card and applied it to three complex simulations, each consisting of objects with tens of thousands of triangles. In practice, we are able to compute all the self-collisions for cloth simulation up to image-space precision at interactive rates.

Highly realistic virtual human models are rapidly becoming commonplace in computer graphics. These models, often represented by complex shape and requiring labor-intensive process, challenge the problem of automatic modeling. This paper studies the problem and solutions to automatic modeling of animatable virtual humans. Methods for capturing the shape of real people, parameterization techniques for modeling static shape (the variety of human body shapes) and dynamic shape (how the body shape changes as it moves) of virtual humans are classified, summarized and compared. Finally, methods for clothed virtual humans are reviewed.

Highly realistic virtual human models are rapidly becoming commonplace in computer graphics. These models, often represented by complex shape and requiring labor-intensive process, challenge the problem of automatic modeling. This paper studies the problem and solutions to automatic modeling of animatable virtual humans. Methods for capturing the shape of real people, parameterization techniques for modeling static shape (the variety of human body shapes) and dynamic shape (how the body shape changes as it moves) of virtual humans are classified, summarized and compared. Finally, methods for clothed virtual humans are reviewed.

Figure 1: Our scale-sensitive approach allows navigation between scales in 3D scenes. Here, the user navigates from thousands of kilometres above Earth’s surface to come to rest inside a jug on a table only centimetres in diameter. We present a comprehensive system for multiscale navigation of 3-dimensional scenes, and demonstrate our approach on multiscale datasets such as the Earth. Our system incorporates a novel imagebased environment representation which we refer to as the cubemap. Our cubemap allows consistent navigation at various scales, as well as real-time collision detection without pre-computation or prior knowledge of geometric structure. The cubemap is used to improve upon previous work on proximal object inspection (HoverCam), and we present an additional interaction technique for navigation which we call look-and-fly. We believe that our approach to the navigation of multiscale 3D environments offers greater flexibility and ease of use than mainstream applications such as Google Earth and Microsoft Virtual Earth, and we demonstrate our results with this system.

WhilemechanG clothsimulation systems are widely used forcreatin draped garmen ts on virtual characters, theanGG of virtual garmen ts raisechallen2 on its own The perception of garmen beauty is first related to thedyn accuracy of the cloth simulation model, which has to reproduce precisely the dissipative behaviors of the material, such as viscosity an d plasticity,renticit the visual features of realistic cloth motion The other importan aspect is the neG of adequate technG for the real-time visualization of an22 virtualgarmen2 required forman real-time or inG) applicationn This workpresen technG) yieldin g visible advanG for addressin these two aspects.

In this paper, we present an FPGA (Field Programmable Gate Array) based collision detection chip. The chip can be used as a co-processor for a traditional computer or several of them can be utilized to work in parallel to create a very fast collision detection server for real time environments. In our experiments we have seen speeds-up of 36 with respect to a fast Pentium 4 chip. Further improvements are possible by using more advanced collision detection techniques.