Mutation 50

A seven frame window through the original self portrait stack was used as input to a temporal rank min-max stack filter to generate this portrait.

Mutation 49

A seven frame temporal processing window was used with the temporal rank median filter to generate this portrait from the original self portrait stack.

Mutation 47

This portrait was generated by stack filtering the original set of self portraits with the temporal difference1 filter.

Mutation 46

This portrait was generated by stack filtering the original set of self portrait images with the temporal difference1 filter.

Mutation 45

We're going to switch gears for awhile and use the original self portrait stack as opposed to the spatially normalized self portrait stack. This portrait was generated with a paint action sequence that auto draws the eyes and mouth of every portrait in the input stack.

So it's an example of using a repetitive process encoded in a PASeq. All of the processing is done automatically, including the face feature detection. The diversity of the original portrait set is desired for this kind of processing (as opposed to the set of previous examples where it was desirable for the painted facial features to be spatially normalized.

Mutation 44

This portrait was generated by stack filtering the spatially normalized set of self portraits with the temporal difference1 filter.

Mutation 43

This portrait was generated by processing the spatially normalized self portraits with a temporal rank neighborhood filter.

Mutation 42

This portrait was generated by stack filtering the spatially aligned self portraits with a different temporal rank neighborhood filter preset.

Mutation 41

This portrait was generated by stack filtering the spatially normalized self portraits with the temporal rank neighborhood filter.

Mutation 40

This portrait was generated by stack filtering the spatially normalized slef portraits with the temporal scan tracker 2 smart seam processing using a horizontal scan and a wide temporal processing window.

Mutation 39

This portrait was generated by stack filtering the spatially normalized self portraits with the temporal scan tracker 2 smart seam processing, using a vertical scan direction.

Mutation 38

This portrait was generated by stack filtering the spatially normalized self portrait stack with the temporal scan tracker 2 processing, using the new smart seam feature.

Mutation 37

This portrait was generated by stack filtering the spatially normalized self portraits with the temporal difference1 filter.

Mutation 38

This image was generated by processing the spatially aligned portraits with a temporal displacement filter. The displacement is based on one of the normalized self portraits chosen at random.

Mutation 37

This portrait was generated by processing the spatially normalized self portraits with the temporal motion rank min filter.

Mutation 36

This portrait was generated by stack filtering the spatially normalized set of self portraits with the temporal motion rank max filter.

Mutation 35

This portrait was generated by processing the complete set of self-aligned spatially normalized portraits with the temporal rank mean difference mask filter.

Mutation 34

This portrait image was generated by processing the complete set of spatially aligned self-normalized portraits with the temporal rank MinMask filter.

Mutation 33

This image was generated by stack filtering the complete set of spatially normalized self portraits with the temporal rank median maximum distance filter.

Mutation 32

The daily portrait virus mutation posts are going to switch gears slightly for awhile. This new set is based using Michael's daily self portrait virus blog imagery as the source for stack filtering. As opposed to the Siggraph 2010 portrait virus imagery that has been used to date.

The other factor that is slightly different is that during the process of running this whole portrait virus mutation experiment we found a bug in the inverse warp code. I wasn't quite satisfied with the spatially aligned imagery being generated by the stack filtering algorithms. And indeed once the inverse warp code was improved the results of spatially aligned stack filtering did as well.

For example, the image above was generated by processing all of Michael's self portrait paintings to date with a temporal mean filter. So you can think of the image as being the generic self portrait. It's like the average portrait, but with all of the face paintings mutually aligned to a standardized portrait (one of the portrait virus images selected at random). The temporal median is also very similar (it isn't always the case).

There's a Studio Artist news blog post on stack filtering portrait images that explains the principals behind how this image was generated (and stack filtering in general) in detail.

At some point i may return to the Siggraph 2010 portrait virus images and redo some of the spatially aligned results shown here, because i think they would be more in line with how i originally thought they would turn out. I had thought about redoing all of the old posts, but discussed this in a Studio Artist Forum post. Part of the point of generating artistic imagery is that mistakes may be just as interesting as technically correct results (sometimes more so). So leaving things the way they progressed as this project developed seemed to make more sense.

Mutation 31

Another example of applying the temporal difference filter to the spatially aligned portrait virus image stack with a limited temporal window.

Mutation 30

Temporal rank neighborhood with the max setting. So it's looking for maximum energy in spatially local neighborhoods across the entire stack (the spatially aligned portrait stack in this case).

Mutation 29

Another smart seam effect using the temporal scan track. I'm using a vertical scan this time as opposed to a horizontal scan. With a limited temporal range, they get a little too venetian blind like for my tastes when you use the entire stack.

Mutation 28

Trying some experiments with the temporal scan track. This is using the new smart seam technology for building the transitions. Again with the spatially aligned portrait virus image stack as input.

Mutation 28

This was generated by processing the spatially aligned portrait virus image stack with the temporal rank variance filter. Again, i'm using temporal processing windows that are smaller than the entire sequence to get more interesting results (to my eye).

Mutation 27

Another example of working with the temporal difference filter. By modifying the temporal processing window and the effect parameters you can generate a lot of different interesting imagery. We're again using the spatially aligned portrait virus image stack because we're interested in building composite facial images based on the variations in the paint strokes associated with the different portraits. If we used the original portrait stack that isn't spatially aligned the temporal processing would pick up on the differences associated with where the objects in the various portraits are as opposed to focusing on the differences in how the faces themselves are painted.

Mutation 26

An example of processing the spatially aligned portrait virus image stack with the temporal difference filter.

Mutation 25

Another example of working with the temporal difference matte effect. This time processing the spatially aligned portrait virus image stack.

Mutation 24

An example of using a temporal median maximum distance filter on the spatially aligned portrait virus image stack.

Mutation 23

I'm using a temporal min-max filter for this particular example. Again, i'm using a limited temporal processing window with the spatially aligned portrait virus image stack.