先ず感謝申し上げます。
大学院修了後は、一貫して版画技術を応用したコンピューテーショナルフォトグラフィを手掛けています。表立った活動や交流はしておりませんが、本作は2021年からスタートした一連のコラージュプロジェクトの最新作になります。
今年の3/4（火）～3/9（日）の会期にて、福岡市美術館で些細ながら展示させて頂く機会を賜りました。展示の仕様上、簡易なパネル印刷では御座いますが、ご機会が御座いましたら、ご笑覧下さいましたら幸いです。
よろしくお願いいたします。
Since completing my graduate studies, I have consistently worked on computational photography applying woodcut printmaking techniques. This piece is the latest work in a series of collage projects I started in 2021.
I am pleased to announce that my work will be exhibited at the Fukuoka Art Museum from March 4 (Tue) – March 9 (Sun), 2025.
Due to the exhibition specifications, the work will be displayed as simple panel prints rather than formal framed giclée prints. If you have the chance, I would be honored if you could visit and view it.
Thank you for your kind consideration.
Ryo Kajitani.
ご興味のある方に向けて、近年の制作について端的に記しておきます：
When I obtained my doctoral degree in 2019, I worked as an image-processing operator, primarily in design, handling many image-processing tasks daily. At that time, the industry was constantly buzzing with the remarkable development of the Diffusion Process initiated by Jascha Sohl-Dickstein and others and the rapid democratization of image processing. *1
At the end of 2021, triggered by an experience of phantom pain, I began preparing to restore my accumulated record photos and studying for "editing."*2
In this artwork, 'editing' refers to a technical metaphor for Somatic excavation, in the sense of reintegrating fragmented experiences and memories within a local dataset space (i.e., a personal data repository of the data's feature space). In that sense, this work would not have been possible without such technological development and its open-minded democratization.
In Denoise Body Experiments, I begin with photography and then utilize latent diffusion models, several image processing filters (.py scripts) for image processing, and Photoshop. The clothing is based on the attire the model typically wears.
In the reverse diffusion process (reverse SDE: Stochastic Differential Equation, dx = [f(x,t) - g²(t)∇x log pt(x)]dt + g(t)dw), one of the theoretical frameworks of diffusion models, paradoxically learns generation methods by gradually destroying data. This process starts from a complete noise state and gradually reconstructs the original signal while extracting the structure. The generated data reflects results sampled based on probability distributions.
In these works, I constructed a local dataset comprising approximately 1,000 personal record photographs, which served as training data for the latent diffusion model. The model was implemented using a standard PyTorch framework, employing a U-Net architecture-based environment (a neural network designed to estimate either the mean μθ or the noise ϵθ of the diffusion process). For image processing, I created batch processing filters mainly using OpenCV, performing color control, blur, sharpness processing, etc. *3
NumPy was used to optimize computational processing. I consistently used a command-line interface (CLI) at all stages, practicing impromptu creative coding. This is akin to writing poetry, and while it is neither efficient nor is the code refined, there are creative moments similar to the instant of turning over a sheet of paper freshly printed from a woodcut. Forging ahead on an obscure and challenging difficult path was indispensable to my creative process.
After these processes complete the photographic base, it is printed as a giclée print. Finally, it is completed by hand coloring (dye ink) and drawing before framing. The local dataset is discarded after production.
References:
*Note1:
[1] Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., & Ganguli, S. (2015). Deep unsupervised learning using nonequilibrium thermodynamics. International Conference on Machine Learning.
[2] Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. ICLR.
Both references retrieved from Google Scholar, August 11, 2024.
*Note2:
This phantom pain in 2021 was an extremely physical bodily response stemming from experiences of violence (which I had been pretending never happened) during my childhood through adolescence. I was surprised because I couldn't control it with my own will. This experience provided me with a specific creative objective: the aesthetic sublimation of trauma.
*Note3:
However, to visualize progress, the tqdm library was utilized to track the waiting time during filter processing in the .py script.
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