K
Koki Nagano
Researcher at Institute for Creative Technologies
Publications - 42
Citations - 1495
Koki Nagano is an academic researcher from Institute for Creative Technologies. The author has contributed to research in topics: Computer science & Rendering (computer graphics). The author has an hindex of 13, co-authored 31 publications receiving 1007 citations. Previous affiliations of Koki Nagano include AmeriCorps VISTA & University of Southern California.
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Proceedings Article
Protecting World Leaders Against Deep Fakes
TL;DR: A forensic technique is described that models facial expressions and movements that typify an individual’s speaking pattern that can be used for authentication in the creation of deepfake videos.
Journal ArticleDOI
paGAN: real-time avatars using dynamic textures
Koki Nagano,Jaewoo Seo,Jun Xing,Lingyu Wei,Zimo Li,Shunsuke Saito,Aviral Agarwal,Jens Fursund,Hao Li +8 more
TL;DR: This work produces state-of-the-art quality image and video synthesis, and is the first to the knowledge that is able to generate a dynamically textured avatar with a mouth interior, all from a single image.
Journal ArticleDOI
Avatar digitization from a single image for real-time rendering
Liwen Hu,Shunsuke Saito,Lingyu Wei,Koki Nagano,Jaewoo Seo,Jens Fursund,Iman Sadeghi,Carrie Sun,Yen-Chun Chen,Hao Li +9 more
TL;DR: This work proposes a novel single-view hair generation pipeline, based on 3D-model and texture retrieval, shape refinement, and polystrip patching optimization, and demonstrates the flexibility of polystrips in handling hairstyle variations, as opposed to conventional strand-based representations.
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High-fidelity facial reflectance and geometry inference from an unconstrained image
Shugo Yamaguchi,Shunsuke Saito,Koki Nagano,Yajie Zhao,Weikai Chen,Kyle Olszewski,Shigeo Morishima,Hao Li +7 more
TL;DR: A deep learning-based technique to infer high-quality facial reflectance and geometry given a single unconstrained image of the subject, which may contain partial occlusions and arbitrary illumination conditions, and demonstrates the rendering of high-fidelity 3D avatars from a variety of subjects captured under different lighting conditions.
Proceedings ArticleDOI
Photorealistic Facial Texture Inference Using Deep Neural Networks
TL;DR: In this paper, a data-driven inference method was proposed to synthesize a photorealistic texture map of a complete 3D face model given a partial 2D view of a person in the wild.