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Visualizing the impact of microplastic pollution in a personalized, data-driven social media campaign
Generative Design - AI - Three.js - WebGL - Adobe After Effects
Overview
Microplastic Profile is a concept for a generative, data-driven social media campaign, visualizing and addressing the pervasive, but invisible issue of microplastic pollution. The concept combines advanced AI technology with scientific data to create personalized narratives about microplastic accumulation in the human body and aims to bridge the gap between abstract data and personal relevance.
Goals
Develop a concept for a generative, data-driven, animated social media advertising campaign on the topic of sustainability. Results showcased during Munich Creative Business Week on 11th May 2023.
Deliverables:
1080x1920 px animated MP4s
15-30 seconds long
3 variants of animations demonstrating how data reflects on the designs
poster with additional information for exhibition visitors
Design Limitations:
one focus color per animation variant
only three forms: circle, rectangle, and triangle
only two fonts: Roboto and Lora
Result
Concept
Social media users are invited to upload or take a full body photo, along with anonymized data like age and region (used for calculations of microplastic accumulation). Following this, a 3D model of their body is generated from the photo using AI, and then slowly filled with the amount of microplastic particles they will have accumulated up to their current age. By using models generated from a photo, as well as age and region, every Microplastic Profile is unique and creates a personalized narrative. The interactivity of the campaign, along with the futuristic style of the resulting animations, aims to catch attention of users overstimulated by social media content and motivate them to engage in the campaign together with their fellow users.
Technical Framework
Photo Capture or Upload:
Utilizing OpenPose and homogenus libraries, keypoints and "gender" information are extracted from a person's photo.
Age and Region Specification:
Parameters such as age and region are inputted to calculate microplastic exposure based on scientific models.
3D Model Generation:
SMPL-X computes a 3D model, including body posture and facial expression, from the photo and extracted data.
Rendering and Particle Simulation:
The 3D model is rendered using three.js, by manipulating point cloud geometries to achieve the animation of microplastic particles filling the model according to calculated data. The code is available on GitHub.
Data Integration
To ensure accuracy and relevance, the animations incorporate data from scientific research on microplastic exposure. The lifetime microplastic exposure model from the publication "Lifetime Accumulation of Microplastic in Children and Adults" is utilized, accounting for microplastic intake through nutrients and other environmental exposure. Factors such as age, region and consumption habbits are also taken into account, with age determining how long the individual has been exposed, region determining which sources of microplastic are relevant and how often/ in what amounts they are being consumed on average.
Future
Currently, Microplastic Profile lacks direct integration between the 3D model generation process and the animation pipeline. To enhance the user experience, future iterations could streamline these components, creating a more seamless experience for the users.
The concept itself could also be expanded by allowing users to compare their microplastic profiles with others based on factors such as region or age group. This feature would not only raise awareness of microplastic pollution levels but also empower individuals and communities to make informed decisions and engage in political discourse surrounding environmental policy and regulation.
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