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A method generates a synthetic textured data signal by first acquiring a time-invariant input textured data signal. The input textured data signal is sampled to construct an observation matrix. The observation matrix is eigen-coding and factoring to identify a linear dynamic system modeling the inpu

A method generates a synthetic textured data signal by first acquiring a time-invariant input textured data signal. The input textured data signal is sampled to construct an observation matrix. The observation matrix is eigen-coding and factoring to identify a linear dynamic system modeling the input textured data signal. Then, the linear dynamic system can be run forward from an initial state using a quadratic regulator and a random noise signal to generate the synthetic textured data signal.

대표청구항 ▼

1. A method for generating a synthetic textured data signal, comprising:acquiring a time-invariant input textured data signal including a sequence of frames; sampling the input textured data signal to construct an observation matrix; eigen-coding and factoring the observation matrix to identify a li

1. A method for generating a synthetic textured data signal, comprising:acquiring a time-invariant input textured data signal including a sequence of frames; sampling the input textured data signal to construct an observation matrix; eigen-coding and factoring the observation matrix to identify a linear dynamic system modeling the input textured data signal; and running the linear dynamic system forward from an initial state using a quadratic regulator and a random noise signal to generate the synthetic textured data signal, further comprising; selecting a set of keyframes from the sequence, each keyframe having an associated state at a particular time; and running the linear dynamic system through the keyframes to pass through predetermined states at predetermined times. 2. The method of claim 1 wherein the input textured data signal includes a sequence of frames, and the initial state is estimated from a last frame of the sequence.3. The method of claim 1 wherein the input textured data signal includes a sequence of frames, and the initial state is estimated from a random frame of the sequence.4. The method of claim 1 further comprising:estimating the random noise signal from an estimated noise distributions of the input textured data signal. 5. The method of claim 1 wherein the quadratic regulator is linear.6. The method of claim 1 wherein the quadratic regulator is non-linear.7. The method of claim 6 wherein the non-linear quadratic regulator is modeled by a probability density function.8. The method of claim 7 wherein the probability density function is a Gaussian mixture model.9. The method of claim 1 wherein the input textured data signal is an acoustic signal.10. The method of claim 1 wherein the input textured data signal is a video sequence of a natural scene.11. The method of claim 1 wherein the input textured data signal is a video sequence, and further comprising:acquiring motion parameters from the input video sequence; estimating and extracting translations and rotations from the motion parameters to produce residual articulatory motion; eigen-coding the residual articularly motion to produce a sequence of morph weights; and concatenating the morph weights with the rotation parameters and the translation to produce the observation matrix.