Digital Muybridge

This project is dedicated to Eadweard Muybridge, who has captured the first photographic recordings of humans and animals in motion in his famous publications "Animal Locomotion" in 1887. Click here to learn more about Muybridge's history.

Digital Muybridge is about the analysis and synthesis of human motion from video streams (or photo plate sequences as in Muybridge's motion studies). We focus on articulated full body motions. Examples are videos of people walking, running, dancing, and other body gestures. Earlier projects focus on facial motion like "talking lips and heads".

This is in part funded by Interval Research Corp. and California State MICRO program.

Video Motion Capture:

We are able to visually capture high degree-of-freedom (DOF) articulated human body configurations in complex video sequences. Video Motion Capture does not require any markers, body suits, or other devices attached to the subject. The only input needed is a video recording of the person in motion. For visual tracking we introduce the use of a novel mathematical technique, the product of exponential maps and twist motions, and its integration into a differential motion estimation. This results in solving simple linear systems, and enables us to recover robustly the kinematic degrees-of-freedom in noise and complex self occluded configurations.

Here are some example MPEG movies on articulated body tracking on lab recordings. Note that this is very challenging footage. Just watch the moving folds and changing appearance on the upper leg. Any standard region based tracker will fail on this. 3D!

We are also able to recover Eadweard Muybridge's motion study plates. Here is an example movie (play it with slow framerate!). We will put more plates online soon, but if you can't wait, ask for our video tape.

Learning and Recognition of Gaits:

In order to learn, annotate, and recognize human movements, we are intrested in statistical spatio-temporal models of human motion. We already implemented a system that demonstrates how to estimate low-level motion and color blobs based on mixtures of Gaussians and the ``Expectation Maximization'' method, mid-level ``moveme'' (motion-phoneme) categories based on second order linear dynamical systems, and high-level categories based on Hidden Markov Models. Although the dynamical models are linear, using them to compute the HMM emission probabilities for complex action categories results in a hybrid nonlinear system. We developed an iterative nonlinear induction algorithm, and tested it successfully on the domain of human gait recognition.

First Level: Motion Coherence Blobs: Given two consecutive images we are able to group pixel regions with coherent motion and spatial proximity using Expectation Maximization (EM) search. Blob models are initialized in clustering Optical Flow field vectors. The iterative improvement of the blob-models is done with repetitive E- and M-steps.

The full architecture can be seen in following mpeg movie. Only one motion blob is illustrated. In the center display you see the translation and angular velocities, and in the top display you see the state probabilities for the different moveme categories. Note that state one is learned to roughly correspond to "ground support".

Also check out the bookchapter, that talks about the early stages of this research:


Muybridge recorded each time-step with sevral camera views. This enables us to recover detailed 3D information. We were able to re-animate several peoples walk cycles with a computer model, and could inspect it from any desired view angle. This was done in Open-GL by Charles Ying. Movies will come soon to this web-page.

Maintained by Chris Bregler (