Search Results for author: Ryota Natsume

Found 6 papers, 2 papers with code

Do We Need Sound for Sound Source Localization?

no code implementations11 Jul 2020 Takashi Oya, Shohei Iwase, Ryota Natsume, Takahiro Itazuri, Shugo Yamaguchi, Shigeo Morishima

Moreover, we show that the majority of sound-producing objects within the samples in this dataset can be inherently identified using only visual information, and thus that the dataset is inadequate to evaluate a system's capability to leverage aural information.

Sound Source Localization

PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

1 code implementation ICCV 2019 Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, Hao Li

We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object.

3D Human Pose Estimation 3D Human Reconstruction +4

SiCloPe: Silhouette-Based Clothed People

1 code implementation CVPR 2019 Ryota Natsume, Shunsuke Saito, Zeng Huang, Weikai Chen, Chongyang Ma, Hao Li, Shigeo Morishima

The synthesized silhouettes which are the most consistent with the input segmentation are fed into a deep visual hull algorithm for robust 3D shape prediction.

Generative Adversarial Network Image-to-Image Translation

FSNet: An Identity-Aware Generative Model for Image-based Face Swapping

no code implementations30 Nov 2018 Ryota Natsume, Tatsuya Yatagawa, Shigeo Morishima

We herein represent the face region with a latent variable that is assigned with the proposed deep neural network (DNN) instead of facial textures.

Face Swapping

Understanding Fake Faces

no code implementations22 Sep 2018 Ryota Natsume, Kazuki Inoue, Yoshihiro Fukuhara, Shintaro Yamamoto, Shigeo Morishima, Hirokatsu Kataoka

Face recognition research is one of the most active topics in computer vision (CV), and deep neural networks (DNN) are now filling the gap between human-level and computer-driven performance levels in face verification algorithms.

Face Recognition Face Verification

RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces

no code implementations10 Apr 2018 Ryota Natsume, Tatsuya Yatagawa, Shigeo Morishima

The proposed network independently handles face and hair appearances in the latent spaces, and then, face swapping is achieved by replacing the latent-space representations of the faces, and reconstruct the entire face image with them.

Attribute Face Swapping +1

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