For Daniel Ambrosi, computational photography is about more than just creating a sense of place. It’s about conveying how we feel about the environment—viscerally, cognitively, and emotionally.
Abstract Dreams
2020/2021 Computational Photography + Artificial Intelligence
The next step in the evolution of our Dreamscapes project, this collection further dissociates the generated artwork from the underlying photography and toward the subconscious. These Abstract Dream video loops are designed to invoke a meditative response that yields illusions of depth perception as similar features recede or advance through changes in colors and textures.
This scene was captured in October 2018 during rush hour from a window on the 16th floor of an office building near Bryant Park in midtown Manhattan. The artist created this Dreamscape in March 2020 during the coronavirus crisis, when a bustling scene like this started to feel like a distant dream. It fuses computational photography and AI to create an environment that’s fascinating to explore.
Developing the "full scene" AI-augmented artwork started with capturing 45 photos from a single location, which were then stitched (Autopano Pro) and blended (Aurora HDR Pro) into a giant (482+ megapixel) panorama and cropped and sweetened in Photoshop. It was processed with a proprietary version of Google DeepDream running on NVIDIA Quadro RTX GPUs.
Finally, Daniel experimented with tools and techniques (including effects available in Filter Forge) to further explore the graphic possibilities inherent in his “Dreamscapes.” His most recent attempts have led to a series he calls Infinite Dreams, an exploration of Cubism-inspired "refracted" dreaming.
This extended video loop illustrates the stages in the artist’s process—capture/assembly of the original 295+ megapixel, 63-shot panorama; first application of AI augmentation using DeepDream; abstractification using Filter Forge; second application of DeepDream; and blending between details pulled from three different regions of the original panorama.
TECHNIQUE: Computational Photography + Artificial Intelligence CAMERA: Sony RX1 or Sony RX1Rii GRAPHICS SOFTWARE/HARDWARE: Aurora HDR Pro, Autopano Pro, Adobe Photoshop, Filter Forge Pro/iMac AI SOFTWARE/HARDWARE: Proprietary version of Google's DeepDream open-source code customized for the artist by Joseph Smarr (Google) and Chris Lamb (NVIDIA)/Quad-GPU compute server on Amazon EC2 (Elastic Compute Cloud)
In this video, Dreamscapes: The Tech Behind the Art, engineers Joseph Smarr (Google) and Chris Lamb (NVIDIA) offer a layman's explanation on how they modified Google's open-source DeepDream software to operate on multi-hundred megapixel images. See how this innovative process enabled computational photography artist Daniel Ambrosi to take his work to new heights.
Daniel Ambrosi is one of the founding creators of the emerging AI art movement and is noted for the nuanced balance he achieves in human-AI hybrid art. He has been exploring novel methods of visual presentation for almost 40 years since Cornell University, where he earned a Bachelor of Architecture degree and a Masters in 3D Graphics. In 2011, Daniel devised a unique form of computational photography that generates exceptionally immersive landscape images.
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It's said that art imitates life, but what if that art is flora and fauna created by an artist using artificial intelligence? Join a discussion with artists Sofia Crespo, Feileacan McCormick, Anna Ridler, and Daniel Ambrosi and NVIDIA technical specialist Chris Hebert to explore how they use AI in their creative process of generating interpretations of natural forms.
This panel discussion with the GTC AI Art Gallery artists, Daniel Ambrosi, Refik Anadol, and Helena Sarin will explore their personal journeys that led to connecting AI and fine art, how the technology has influenced their artistic process, why AI is important in the broader field of fine art, how art education intersects with AI education, and whether AI will be capable of achieving autonomous control over the creative process.