(^ It’s actually 50 secs long idk why it shows up as over 2 mins??)
Timestamp
I decided to create a work to (sort of) recollect my experience from 2017 to 2020 when I was staring at my postcard holder that I used to collect all sorts of ephemera and pieces of printed matter over the last few years. I couldn’t recall the exact reason why I started to collect these things but I ended up with this postcard holder being all filled up (and even it weighs a lot, it was one of the first things that I decided to carry along with me when I moved from New York to Pittsburgh this Summer).

I was thinking about how this habit of collecting ephemera tracked the passing time and what I could reflect upon them, so I created this set of four AR sculptures using images from printed tickets I collected as tracking image sites. Each sculpture represents one of the four years and is made up of two parts: a 3D model of a roll of film using photos I’ve taken in that year as texture, covered by an orb with some text I wrote down when I was looking through the photos.

Besides learning about how to do augmented image and image tracking, figuring out how to show transparent background video in Unity was also a great learning experience (although it took a while…). I first recorded my writing in Procreate and then edit out the background with chroma key in Premiere, and finally imported into Unity as video texture with alpha channel. I also embedded sounds in the videos. The sounds are extracted from other videos I took in that year.
I kept the movement of the sculpture pretty minimal and focus on the composition of the scene, including the tickets that I used as trackers. Although the tickets have more specific dates other than the years (and they are actually all from MoMA), I focused on the years only.

As for why MoMA tickets… I actually don’t have a specific reason other than the quantity (ended up having 14 like why) and the quality of the images they used are good for being trackers.














I enjoyed playing around with the different gene categories in the general model. The results are quite unexpected and it’s fascinating to see that the influence of each gene is not entirely evenly distributed.
Besides the general model, I also tried the portraits model. I got these interesting results when I kept all other genes the same but only adjusted the ethnicity values from one end to another.