Daito Manabe

Daito Manabe

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Credited on 3 works from 2020 to 2021. Main genres: Installation, Music video, Performance.

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Work

“Gold Rush” – Visualization + Sonification of Opensea activity - 映像作家: rhizomatiks
Installation

“Gold Rush” – Visualization + Sonification of Opensea activity

2021
00:02:19
from《NTFs and CryptArt-Experiment》 Venue: From the solo exhibition “rhizomatiks_multiplex” (Museum of Contemporary Art Tokyo, 2021) https://www.mot-art-museum.jp/en/exhibitions/rhizomatiks/ This installation uses event data from Opensea from one day before and after 3/11 10am EST, when MetaKova won Beeple’s NFT at Christies for $69,346,250. It looks back on this highly symbolic event in CryptoArt using machine learning techniques (Convolutional Neural Network, t-SNE) and data visualization. from《NTFs and CryptArt-Experiment》 Venue: Solo Exhibition “rhizomatiks_multiplex” (Museum of Contemporary Art Tokyo, 2021) https://www.mot-art-museum.jp/en/exhibitions/rhizomatiks/ This installation uses event data from Opensea around 3/11 10am EST, when MetaKova won Beeple’s NFT at Christies for $69,346,250. We look back at this symbolic event in CryptoArt using machine learning techniques (Convolutional Neural Network, t-SNE) and data visualization.
Squarepusher – Terminal Slam - 映像作家: rhizomatiks
Music video

Squarepusher – Terminal Slam

2020
00:05:33
Terminal Slam Music Video is a Diminished Reality / Mixed Reality music video in which all advertisements in the city are erased or replaced with Squarepusher-related ads. Daito Manabe created this MV, imagining that in the near future, as shown in this music video, AR/MR glasses will make it possible to freely rewrite cityscapes. Using machine learning technology, we performed: - Object Detection (identifying the positions of objects in an image) - Semantic Segmentation (labeling each pixel in an image and categorizing image regions according to their meaning) - Image Inpaint (image restoration) Most advertising billboards in the city change periodically, so with current technology it was difficult for machines to recognize them accurately; therefore, the billboard position data was created manually. In addition, mask information was generated from the analysis data and positional data, and post-processing was performed, such as applying glitch effects only to areas where people or advertisements exist, and applying optical camouflage effects. Furthermore, because the process of extracting advertisement regions could not be accomplished using existing machine learning technologies alone, manual operations were also carried out for elements that could not be realized with the technology available at present. We anticipate that in the near future, all of this will be done automatically.

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