projects
MISMATCHING
NATURE
NATURE
Deep Learning
2023
Deep Learning | Animation | Speculative Design
mentored by Ludwig Zeller
collaborated with
Akshada Bhat, Paulina Ojeda
Akshada Bhat, Paulina Ojeda
MISMATCHING NATURE
an AI-driven visual journey to highlight the urgency of the climate crisis
Mis-Matching is a visual AI exploration that focuses on raising
awareness of climate change by focusing on ecological mismatching. In this phenomenon, species fall
out of sync due to changing environmental conditions. We created an
AI-driven animation featuring five plants, initially growing in harmony
but later misaligning to illustrate this disruption. The visuals,
generated with DALL-E and Stable Diffusion, are paired with a soundtrack
generated with the sound AI tool AIVA that intensifies as the mismatch occurs. A voiceover,
produced with ChatGPT and ElevenLabs, explains the concept, blending
scientific insight with emotional appeal.
Tools
Stable Diffusion, ChatGPT, Kaiber, AIVA, Eleven Labs, After Effects
Stable Diffusion, ChatGPT, Kaiber, AIVA, Eleven Labs, After Effects
PROCESS
1. explorations in Dall-E
1. explorations in Dall-E
First the visual language of the project was explored in Dall-E.
2. prompting in Stable Diffusion
Then we extracted prompts from Dall-E and adjusted them for prompting in Stable Diffusion. At this point Stable Diffusion offered more control in the generation of images and the possibility to process images in batches. Therefore, we first created basic animations of the plants in After Effects based on the plants created in Dall-E.
Subsequently, we extracted frames from these animations and used image to image generation in Stable Diffusion to return to the initial visual language explored in Dall-E.
3. animation
Finally, five plants were animated from the images created in Stable Diffusion and an AI generated voice over and soundtrack added to explain the phenomenon of ecological mismatching and support a dystopian effect in the final video.