Subspace Clothing Simulation Using Adaptive Bases

We present a new approach to clothing simulation using low-dimensional linear subspaces with temporally adaptive bases. Our method exploits full-space simulation training data in order to construct a pool of low-dimensional bases distributed across pose space. For this purpose, we interpret the simulation data as offsets from a kinematic deformation model that captures the global shape of clothing due to body pose.

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Efficient Simulation of Secondary Motion in Rig-Space

We present an efficient method for augmenting keyframed character animations with physically-simulated secondary motion. Our method achieves a performance improvement of one to two orders of magnitude over previous work without compromising on quality.

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Memory Efficient Stereoscopy from Light Fields

We address the problem of stereoscopic content generation from light fields using multi-perspective imaging. Our proposed method takes as input a light field and a target disparity map, and synthesizes a stereoscopic image pair by selecting light rays that fulfill the given target disparity constraints. We formulate this as a variational convex optimization problem.

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Multi-Perspective Stereoscopy from Light Fields

This paper addresses stereoscopic view generation from a light field. We present a framework that allows for the generation of stereoscopic image pairs with per-pixel control over disparity, based on multi-perspective imaging from light fields. The proposed framework is novel and useful for stereoscopic image processing and post-production.

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Hierarchical Motion Brushes for Animation Instancing

Our work on “motion brushes” provides a new workflow for the creation and reuse of 3D animation with a focus on stylized movement and depiction. Conceptually, motion brushes expand existing brush models by incorporating hierarchies of 3D animated content including geometry, appearance information, and motion data as core brush primitives that are instantiated using a painting interface.

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Scalable Music: Automatic Music Retargeting and Synthesis

In this paper we propose a method for dynamic rescaling of music, inspired by recent works on image retargeting, video reshuffling and character animation in the computer graphics community. Given the desired target length of a piece of music and optional additional constraints such as position and importance of certain parts, we build on concepts from seam carving, video textures and motion graphs and extend them to allow for a global optimization of jumps in an audio signal.

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Generating and Ranking Diverse Multi-Character Interactions

Our novel `generate-and-rank' approach rapidly and semi-automatically generates data-driven fight scenes from high-level text descriptions composed of simple clauses and phrases. From a database of captured motions and its associated motion graph, we first generate a `cascade' of plausible scenes.

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