Hardware-Accelerated Adaptive EWA Volume Splatting

Wei Chen, Liu Ren, Matthias Zwicker, and Hanspeter Pfister

Abstract

we present a hardware-accelerated adaptive EWA(elliptical weighted average) volume splatting algorithm. EWA splatting combines a Gaussian reconstruction kernel with a low-pass image filter for high image quality without aliasing artifacts or excessive blurring. We introduce a novel adaptive filtering scheme to reduce the computational cost of EWA splatting. We show how this algorithm can be efficiently implemented on modern graphics processing units (GPUs). Our implementation includes interactive classification and fast lighting. To accelerate the rendering we store splat geometry and 3D volume data locally in GPU memory. We present results for several rectilinear volume datasets that demonstrate the high image quality and interactive rendering speed of our method.

Citation

Wei Chen, Liu Ren, Matthias Zwicker, and Hanspeter Pfister. Hardware-accelerated adaptive EWA volume splatting. In Proceedings of IEEE Visualization 2004, October 2004. [BiBTeX]

Links

• Project page

Autonomous Behaviors for Interactive Vehicle Animations

Jared Go, Thuc Vu, and James J. Kuffner

Abstract

We present a method for synthesizing animations of autonomous space, water, and land-based vehicles in games or other interactive simulations. Controlling the motion of such vehicles to achieve a desirable behavior is difficult due to the constraints imposed by the system dynamics. We combine real-time path planning and a simplified physics model to automatically compute control actions to drive a vehicle from an input state to desirable output states based on a behavior cost function. Both offline trajectory preprocessing and online search are used to build an animation framework suitable for interactive vehicle simulations. We demonstrate synthesized animations of spacecraft performing a variety of autonomous behaviors, including Seek, Pursue, Avoid, Avoid Collision, and Flee. We also explore several enhancements to the basic planning algorithm and examine the resulting tradeoffs in runtime performance and quality of the generated motion.

Citation

Jared Go, Thuc Vu, and James J. Kuffner. Autonomous behaviors for interactive vehicle animations. In 2004 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, August 2004. [BiBTeX]

Links

• Project page

Evaluating Motion Graphs for Character Navigation

Paul S. A. Reitsma and Nancy S. Pollard

Abstract

Realistic and directable humanlike characters are an ongoing goal in animation. Motion graph data structures hold much promise for achieving this goal. However, the quality of the results obtained from a motion graph may not be easy to predict from the input motion segments. This paper introduces the idea of assessing a data structure such as a motion graph for its utility in a particular application. We focus on navigation tasks and define metrics for evaluating expected path quality and coverage for a given environment. One key to evaluating a motion graph for navigation tasks is to first embed it into the environment in a way that captures all possible paths that might result from playing back the motion graph within that environment. This paper describes an algorithm for accomplishing this embedding that preserves the flexibility of the original motion graph. We use the metrics defined in this paper to compare motion datasets and to highlight areas where these datasets could be improved.

Citation

Paul S. A. Reitsma and Nancy S. Pollard. Evaluating motion graphs for character navigation. In 2004 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, August 2004. [BiBTeX]

Links

• Project page

Flow-based Video Synthesis and Editing

Kiran S. Bhat, Steven M. Seitz, Jessica K. Hodgins, and Pradeep K. Khosla

Abstract

This paper presents a novel algorithm for synthesizing and editing video of natural phenomena that exhibit continuous flow patterns. The algorithm analyzes the motion of textured particles in the input video along user-specified flow lines, and synthesizes seamless video of arbitrary length by enforcing temporal continuity along a second set of user-specified flow lines. The algorithm is simple to implement and use. We used this technique to edit video of waterfalls, rivers, flames, and smoke.

Citation

Kiran S. Bhat, Steven M. Seitz, Jessica K. Hodgins, and Pradeep K. Khosla. Flow-based video synthesis and editing. ACM Transactions on Graphics (SIGGRAPH 2004), 23(3), August 2004. [BiBTeX]

Links

• Project page

Learning Silhouette Features for Control of Human Motion

Liu Ren, Gregory Shakhnarovich, Jessica K. Hodgins, Hanspeter Pfister, and Paul Viola

Abstract

We present a vision-based performance interface for controlling animated human characters. The system combines information about the user's motion contained in silhouettes from several viewpoints with domain knowledge contained in a motion capture database to interactively produce a high quality animation. Such an interactive system will be useful for authoring, teleconferencing, or as a control interface for a character in a game. In our system, the user performs in front of three video cameras; the resulting silhouettes are used to estimate his or her orientation and body configuration based on a set of discriminative local features. Those features are selected by a machine learning algorithm during a preprocessing step. Sequences of motions that approximate the user's actions are extracted from the motion database and scaled in time to match the speed of the user's motion. We use swing dancing, an example of complex human motion, to demonstrate the effectiveness of our approach and compare the results obtained with discriminative local features to those obtained with global features, Hu moments, and to ground truth measurement from a motion capture system.

Citation

Liu Ren, Gregory Shakhnarovich, Jessica K. Hodgins, Hanspeter Pfister, and Paul Viola. Learning silhouette features for control of human motion. In Proceedings of the SIGGRAPH 2004 Conference on Sketches & Applications. ACM Press, August 2004. [BiBTeX]

Links

• Project page

Markerless Human Motion Transfer

German Cheung, Simon Baker, Jessica K. Hodgins, and Takeo Kanade

Citation

German Cheung, Simon Baker, Jessica K. Hodgins, and Takeo Kanade. Markerless human motion transfer. In Proceedings of the SIGGRAPH 2004 Conference on Sketches & Applications. ACM Press, August 2004. [BiBTeX]

Near-Regular Texture Analysis and Manipulation

Yanxi Liu, Wen-Chieh Lin, and James Hays

Abstract

A near-regular texture deviates geometrically and photometrically from a regular congruent tiling. Although near-regular textures are ubiquitous in the man-made and natural world, they present computational challenges for state of the art texture analysis and synthesis algorithms. Using regular tiling as our anchor point, and with user-assisted lattice extraction, we can explicitly model the deformation of a near-regular texture with respect to geometry, lighting and color. We treat a deformation field both as a function that acts on a texture and as a texture that is acted upon, and develop a multimodal framework where each deformation field is subject to analysis, synthesis and manipulation. Using this formalization, we are able to construct simple parametric models to faithfully synthesize the appearance of a near-regular texture and purposefully control its regularity.

Citation

Yanxi Liu, Wen-Chieh Lin, and James Hays. Near-regular texture analysis and manipulation. ACM Transactions on Graphics (SIGGRAPH 2004), 23(3), August 2004. [BiBTeX]

Links

• Project page

Squashing Cubes: Automating Deformable Model Construction for Graphics

Doug L. James, Jernej Barbič, and Christopher D. Twigg

Abstract

The vast majority of geometric meshes used in computer graphics are optimized for rendering, and not deformable object simulation. Despite tools for volume (or surface) (re)meshing of geometric models to support physical simulation, in practice, the construction of physically based deformable models from arbitrary graphical models remains a tedious process for animators. Squashing Cubes automates the construction of physically based deformable objects from arbitrary geometric models. During preprocess, the geometric model (typically a surface mesh) is voxelized into tiny elastic cubes, i.e., the (squashing cubes model). Second, a generic deformable object simulator is used to deform the squashing cubes model. Finally, the resulting deformations are interpolated back onto the original model, thus producing the final animation.

Citation

Doug L. James, Jernej Barbič, and Christopher D. Twigg. Squashing cubes: Automating deformable model construction for graphics. In Proceedings of the SIGGRAPH 2004 Conference on Sketches & Applications. ACM Press, August 2004. [BiBTeX]

Links

• Project page

Synthesizing Animations of Human Manipulation Tasks

Katsu Yamane, James J. Kuffner, and Jessica K. Hodgins

Abstract

Even such simple tasks as placing a box on a shelf are difficult to animate, because the animator must carefully position the character to satisfy geometric and balance constraints while creating motion to perform the task with a natural-looking style. In this paper, we explore an approach for animating characters manipulating objects that combines the power of path planning with the domain knowledge inherent in data-driven, constraint-based inverse kinematics. A path planner is used to find a motion for the object such that the corresponding poses of the character satisfy geometric, kinematic, and posture constraints. The inverse kinematics computation of the character's pose resolves redundancy by biasing the solution toward natural-looking poses extracted from a database of captured motions. Having this database greatly helps to increase the quality of the output motion. The computed path is converted to a motion trajectory using a model of the velocity profile. We demonstrate the effectiveness of the algorithm by generating animations across a wide range of scenarios that cover variations in the geometric, kinematic, and dynamic models of the character, the manipulated object, and obstacles in the scene.

Citation

Katsu Yamane, James J. Kuffner, and Jessica K. Hodgins. Synthesizing animations of human manipulation tasks. ACM Transactions on Graphics (SIGGRAPH 2004), 23(3), August 2004. [BiBTeX]

Links

• Project page

Synthesizing Physically Realistic Human Motion in Low-Dimensional, Behavior-Specific Spaces

Alla Safonova, Jessica K. Hodgins, and Nancy S. Pollard

Abstract

Optimization is an appealing way to compute the motion of an animated character because it allows the user to specify the desired motion in a sparse, intuitive way. The difficulty of solving this problem for complex characters such as humans is due in part to the high dimensionality of the search space. The dimensionality is an artifact of the problem representation because most dynamic human behaviors are intrinsically low dimensional with, for example, legs and arms operating in a coordinated way. We describe a method that exploits this observation to create an optimization problem that is easier to solve. Our method utilizes an existing motion capture database to find a low-dimensional space that captures the properties of the desired behavior. We show that when the optimization problem is solved within this low-dimensional subspace, a sparse sketch can be used as an initial guess and full physics constraints can be enabled. We demonstrate the power of our approach with examples of forward, vertical, and turning jumps; with running and walking; and with several acrobatic flips.

Citation

Alla Safonova, Jessica K. Hodgins, and Nancy S. Pollard. Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Transactions on Graphics (SIGGRAPH 2004), 23(3), August 2004. [BiBTeX]

Links

• Project page

Team Teaching Animation Art and Technology

James Duesing and Jessica K. Hodgins

Abstract

In this paper we report on an interdisciplinary course, "Animation Art and Technology," which we have taught for the past two years at Carnegie Mellon University. Faculty and teaching assistants from computer science and art teach the class as a team and the students are an interdisciplinary mix. This class is a project-based course in which teams of students produce 4-5 animations. Most of the animations have a substantive technical component and the students are challenged to consider innovation with content to be equal with the technical. In this paper, we describe the structure of the class and assess the elements that have worked well and those that require improvement.

Citation

James Duesing and Jessica K. Hodgins. Team teaching animation art and technology. In Educators Program from the 31st Annual Conference on Computer Graphics and Interactive Techniques. ACM Press, August 2004. [BiBTeX]

Links

• Paper (PDF, 3.14MB)

BD-Tree: Output-Sensitive Collision Detection for Reduced Deformable Models

Doug L. James and Dinesh K. Pai

Abstract

We introduce the Bounded Deformation Tree, or BD-Tree, which can perform collision detection with reduced deformable models at costs comparable to collision detection with rigid objects. Reduced deformable models represent complex deformations as linear superpositions of arbitary displacement fields, and are used in a variety of applications of interactive computer graphics. The BD-Tree is a bounding sphere hierarchy for output-sensitive collision detection with such models. Its bounding spheres can be updated after deformation in any order, and at a cost independent of the geometric complexity of the model; in fact the cost can be as low as one multiplication and addition per tested sphere, and at most linear in the number reduced deformation coordinates. We show that the BD-Tree is also extremely simple to implement, and performs well in practice for a variety of real-time and complex off-line deformable simulation examples.

Citation

Doug L. James and Dinesh K. Pai. BD-Tree: Output-sensitive collision detection for reduced deformable models. ACM Transactions on Graphics (SIGGRAPH 2004), 23(3), August 2004. [BiBTeX]

Links

• Project page

Modeling Physical Variability for Synthetic MOUT Agents

Gita Sukthankar, Michael Mandel, Katia Sycara, and Jessica K. Hodgins

Abstract

Generating behavioral variability is an important prerequisite in the development of synthetic MOUT (Military Operations in Urban Terrain) agents for military simulations. Agents that lack variability are predictable and ineffective as opponents and teammates for human trainees. Along with cognitive differences, physical differences contribute towards behavioral variability. In this paper, we describe a novel method for modeling physical variability in MOUT soldiers using motion capture data acquired from human subjects. Motion capture data is commonly used to create animated characters since it retains the nuances of the original human movement. We build a cost model over the space of agent actions by creating and stochastically sampling motion graphs constructed from human data. Our results demonstrate how different cost models can induce variable behavior that remains consistent with military doctrine.

Citation

Gita Sukthankar, Michael Mandel, Katia Sycara, and Jessica K. Hodgins. Modeling physical variability for synthetic mout agents. In Proceedings of 2004 Conference on Behavior Representation in Modeling and Simulation, May 2004. [BiBTeX]

Links

Segmenting Motion Capture Data into Distinct Behaviors

Jernej Barbič, Alla Safonova, Jia-Yu Pan, Christos Faloutsos, Jessica K. Hodgins, and Nancy S. Pollard

Abstract

Much of the motion capture data used in animations, commercials, and video games is carefully segmented into distinct motions either at the time of capture or by hand after the capture session. As we move toward collecting more and longer motion sequences, however, automatic segmentation techniques will become important for processing the results in a reasonable time frame. We have found that straightforward, easy to implement segmentation techniques can be very effective for segmenting motion sequences into distinct behaviors. In this paper, we present three approaches for automatic segmentation. The first two approaches are online, meaning that the algorithm traverses the motion from beginning to end, creating the segmentation as it proceeds. The first assigns a cut when the intrinsic dimensionality of a local model of the motion suddenly increases. The second places a cut when the distribution of poses is observed to change. The third approach is a batch process and segments the sequence where consecutive frames belong to different elements of a Gaussian mixture model. We assess these three methods on fourteen motion sequences and compare the performance of the automatic methods to that of transitions selected manually.

Citation

Jernej Barbič, Alla Safonova, Jia-Yu Pan, Christos Faloutsos, Jessica K. Hodgins, and Nancy S. Pollard. Segmenting Motion Capture Data into Distinct Behaviors. In Proceedings of Graphics Interface 2004, pages 185–194, May 2004. [BiBTeX]

Links

• Project page