Generalizing motion edits with Gaussian processes

By Leslie Ikemoto, Okan Arikan, D.A. Forsyth. 2009

1,155 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Anca Dragan
2. Taku Komura
3. Siddhartha S. Srinivasa
4. He Zhang
5. Sebastian Starke
6. Sergey Levine
7. Ziwei Liu
8. Jun Saito

In Control and Systems Engineering

1. Anca Dragan
2. Siddhartha S. Srinivasa
3. He Zhang
4. Sebastian Starke
5. Jun Saito
6. Taku Komura
7. Fangzhou Hong
8. Ziwei Liu

In Probabilistic & Bayesian Methods

1. Siddhartha S. Srinivasa
2. Ziwei Liu
3. Kenton C. T. Lee
4. Anca Dragan

Citations in the corpus

• Teleoperation with intelligent and customizable interfaces (2013, background): “Although the traditional way to do so is via constrained optimization (Gleicher, 1998), recent work has proposed to learn a retargetting function offline, based on key pose correspondences (Yamane et al., 2010), or based on a different, manually retargetted trajectory (Ikemoto et al., 2009).”
• MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model (2022, background): “Some early works focus on unconditional motion generation [20], [21], [22].”
• Online customization of teleoperation interfaces (2012, neutral): “Although the traditional way to do so is via constrained optimization [1], recent work has proposed to learn a retargetting function offline, based on key pose correspondences [6], or based on a different, manually retargetted trajectory [7].”