@freya_watkins_ on comprehension of signs depending on angles. Especially L2 signers have a harder task mentally rotating signs perceived from an angle (as opposed to front facing). #TISLR13
I'm wondering if mouthing plays a role. 👄👀
In a lexical naming task, scores for different angle representations were correlated with scores on a general mental rotation task. #TISLR13
Performance depends on frequency: you perform better with angles you were exposed to the most (yay frequency effects in language). Mental rotation a crucial skill that perhaps needs explicit training. #TISLR13
(funny that I was thinking about mouthing, having forgot that I shared some #Python code for video blurring for this specific study) #TISLR13
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Last night I was playing a little with Openpose data in #RStats. I realized it's not too hard to wrangle the Openpose output and plot signing directly using #ggplot2 and #gganimate, like so:
But I decided to make some tweaks so you can change the color of the signer+clothes, which makes seeing the hands a bit easier (contrast!)...
But also, why not give your signer a pretty turtleneck to wear?
You guys know that IKEA products are basically just #Swedish words and place names, right? Walking around an IKEA store is like walking through a dictionary.
This is a script simulating the idea in Swedish and other places/languages: github.com/borstell/fakea
So you can now input a video and it outputs it slower and/or repeated. Here's an example of a sign for 'deaf' in STS rendered with a repeated 30% speed playback!
(Oh, and passed to the make_gif() function as well!)
And the automatic face blurring works great! Even with multiple people in the image (or, like here, multiple repetitions of the same person in one composite image)!
So, it's like *very* easy to process and reconstruct actual images with only a few lines of code. As in plotting software redrawing the image, pixel by pixel.
Here's is a gif of me made with #ggplot2 and #gganimate. Sunday = fun day!