[AUTHOR]
Cailean Finn is a creative technologist and new media artist based in Waterford, Ireland. In his work, Cailean explores digital systems, and their underlying structure, providing insight into how contemporary technologies that shape our environment function. Cailean’s work aims to reflect on the relationship we have formed with machines, and how it can be (re)defined moving forward - searching for new modalities of collaboration, which is underpinned by the evolutionary processes we share. Currently, Cailean’s practice is focused around exploring artificial intelligence, machine learning and computer vision algorithms.
[EDITORS]
Karina Zavidova; **Ploipailin Flynn; Nadia Piet**
[PROGRAM]
This guide is part of our AI Playground program [S01] which ran from May 2022-February 2023. It was funded and made possible by Stimuleringsfonds Creatieve Industrie.
[COVER IMG]
Living archive by Ben Cullen-Williams (source)
In this guide, Cailean Finn tells us about human pose detection and recognition technology. In this application of AI and ML, the technology hasn’t been accessible to ‘outsiders’ until recently, and Cailean walks us through what it is, tells us about its history and shows examples of how it can be used for creative purposes.
This guide is a part of our community program AI Playground / Body. AI Playground is an event series and a collection of Guides, structured under four topics: Image, Text, Body and Sound. As part of the program we hosted 2 events (full recordings can be found on our YouTube channel):
https://www.youtube.com/watch?v=VEzXTB_rdWw
https://www.youtube.com/watch?v=MeD5rWUPJZo
→ Human body language is an intrinsic component of our lived experience. Through our movements we can engage in nonverbal communication, using our physical movements to express ideas and emotions often done instinctively rather than consciously. Subsequently, our perceptions of others are heavily influenced by their body language, communicating a plethora of information to the world. This flow of information does not cease when you stop speaking, even when you are silent you are still communicating.
→ In the digital age, we have unfortunately seen this complex language fade into the background. This has become even more evident during the pandemic, intoxicated by the copious amount of Zoom meetings, a domain where our level of communication is severely limited. So, how can we encompass our body language as a tool for communication in the digital age?
In this guide, I hope to provide a brief historical and technical overview of the many artificial intelligence and machine learning tools for Human Pose Recognition (HPR) that are currently available and in development.
→ Human Pose Recognition is a branch of Computer Vision research, and is essentially a technique that allows us to accurately detect and predict/estimate the pose of a person. This is achieved by identifying and classifying the coordinates of the joints of a human body, such as wrists, shoulders, knees, arms (..) commonly known as landmarks.
→ Through more accurate representations of our physical body it can enable us to create more natural and complex interactions with different virtual environments.
→ In the past, there has been many technical limitations for artist, designer and creative practitioners to utilise and experiment with Human Pose Recognition tools. As this technology becomes more accessible through the development of tools like *OpenPose* and MoveNet, it presents us with the opportunity to explore new modalities of bodily interaction. With this increase in accessibility and speed, human pose recognition is becoming more ubiquitous across numerous ecologies, and we must begin to critically observe how this information could be used when mediating our bodily movements digitally.
How can we use Human Pose Recognition to translate our intimate bodily movements in a digital environment? What elements do we lose during that process?
Ultimately, the aim of this guide is to provide a foundation for further exploration and experimentation of Human Pose Recognition.
Authors Ginés Hidalgo (left) and Hanbyul Joo (right) in front of the CMU Panoptic Studio, Open Pose