SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
What is the goal of sensory coding?
Neural Computation
Pyramid-based texture analysis/synthesis
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Video Rewrite: driving visual speech with audio
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Minimax entropy principle and its application to texture modeling
Neural Computation
Approximate and probabilistic algorithms for shading and rendering structured particle systems
SIGGRAPH '85 Proceedings of the 12th annual conference on Computer graphics and interactive techniques
Fast texture synthesis using tree-structured vector quantization
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Controlled animation of video sprites
Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
Motion texture: a two-level statistical model for character motion synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Texture Mixing and Texture Movie Synthesis Using Statistical Learning
IEEE Transactions on Visualization and Computer Graphics
Stability of Phase Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
A Generative Method for Textured Motion: Analysis and Synthesis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
A Lattice-Based MRF Model for Dynamic Near-Regular Texture Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Textons and the propagation of space in acousmatic music
Organised Sound
Non linear temporal textures synthesis: a monte carlo approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Dynamic texture synthesis in the YUV color-space
ICEC'07 Proceedings of the 6th international conference on Entertainment Computing
Computer Vision and Image Understanding
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Natural scenes contain a wide range of textured motion phenomena which are characterized by the movement of a large amount of particle and wave elements, such as falling snow, wavy water, and dancing grass. In this paper, we present a generative model for representing these motion patterns and study a Markov chain Monte Carlo algorithm for inferring the generative representation from observed video sequences. Our generative model consists of three components. The first is a photometric model which represents an image as a linear superposition of image bases selected from a generic and overcomplete dictionary. The dictionary contains Gabor and LoG bases for point/particle elements and Fourier bases for wave elements. These bases compete to explain the input images and transfer them to a token (base) representation with an O(10^2)\hbox{-}{\rm{fold}} dimension reduction. The second component is a geometric model which groups spatially adjacent tokens (bases) and their motion trajectories into a number of moving elements驴called "motons.驴 A moton is a deformable template in time-space representing a moving element, such as a falling snowflake or a flying bird. The third component is a dynamic model which characterizes the motion of particles, waves, and their interactions. For example, the motion of particle objects floating in a river, such as leaves and balls, should be coupled with the motion of waves. The trajectories of these moving elements are represented by coupled Markov chains. The dynamic model also includes probabilistic representations for the birth/death (source/sink) of the motons. We adopt a stochastic gradient algorithm for learning and inference. Given an input video sequence, the algorithm iterates two steps: 1) computing the motons and their trajectories by a number of reversible Markov chain jumps, and 2) learning the parameters that govern the geometric deformations and motion dynamics. Novel video sequences are synthesized from the learned models and, by editing the model parameters, we demonstrate the controllability of the generative model.