Full Papers 1:
Interaction
Wednesday, August 31st, 2005. 11:00 - 12:30.
VENUE: Burke Theatre.
SESSION CHAIR: Joaquim Jorge.
A Semantic Space Partitioning Approach to Virtual Camera Composition
Marc Christie,Jean-Marie Normand,
LINA FRE CNRS 2729 - University of Nantes
We present a semantic space partitioning (SSP) approach to the virtual camera composition problem. Virtual camera composition (VCC) consists in positioning a camera in a virtual world, such that the resulting image satisfies a set of visual cinematographic properties. Whereas most related works concentrate on numerically computing a unique camera position satisfying the problem, we offer to isolate identical possible solutions in 3D volumes with respect to their visual properties, and to propose them to the user. We introduce the notion of semantic volumes as an extension of visual aspects to characterize, compute and manipulate distinct solution sets. Our approach relies on (1) a space partitioning process derived from a study of possible camera locations w.r.t. to the objects in the scene and (2) local search numerical techniques to compute good representatives of each volume. This work is motivated by the lack of VCC tools in 3D software and the will to integrate cinematographic semantics in the description, solving and interaction processes. Experimental results illustrate the suitability of our approach for identifying and providing distinct solution sets. Furthermore, the exploitation of the semantic volumes lays the groundwork for natural and efficient user interaction by providing knowledge and reasoning on possible classes of solutions.
Predictive Feedback for Interactive Control of Physics-based Characters
Joe Laszlo,
Michael Neff,
Karan Singh,
University of Toronto
Interactive control of a physically simulated character is a challenging problem, due both to the complexity of controlling multiple degrees of freedom with lower dimensional input and because many interesting motions lie on the fringes of character stability. This paper addresses these problems using a novel technique called predictive feedback, where a glimpse into the near future for a few sample inputs is continuously presented to the animator. We discuss issues related to the spatio-temporal distribution of predictions so that they provide meaningful and timely feedback to an animator interactively controlling a physics-based character with simple input devices, like a mouse or keyboard. We propose a visual presentation of this predictive feedback in which control input samples are chosen in the proximity of the user's current input and the predicted results are co-located with the position of the input necessary to achieve them. We further show how the predictive samples may be automatically interpolated to control aspects of the character's motion, such as balance, thereby freeing the animator to focus on other details. The paper thus contributes a technique for physically simulated characters that simplifies interactive character control and increases the range of motion that can be performed by both novices and experts. Many of the presented concepts extend beyond our specific input device and dynamic character control setting to more general input tasks.
Pen-and-Ink for BlobTree Implicit Models
Kevin Foster,
Pauline Jepp,
Brian Wyvill,
Mario Costa Sousa,
Callum Galbraith,
Department of Computer Science, University of Calgary
Joaquim Jorge,
Departamento de Engenharia Informatica, Universidade Tecnica de Lisboa
New techniques are presented for rendering complex hierarchical, skeletal
implicit models in several pen-and-ink styles. A particle system is employed
to find interesting areas on the surface, and perform stroke
stylization guided by local shape features. Interesting areas include silhouette strokes and lines following local shape features, such as those caused by CSG junctions. Hidden line removal is performed either by applying a surfel technique for rapid prototyping, or
more accurately, by using ray tracing. Examples drawn from simple to
complex models illustrate the capabilities of our system.
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