2 code implementations • 25 Mar 2024 • Zicong Fan, Takehiko Ohkawa, Linlin Yang, Nie Lin, Zhishan Zhou, Shihao Zhou, Jiajun Liang, Zhong Gao, Xuanyang Zhang, Xue Zhang, Fei Li, Liu Zheng, Feng Lu, Karim Abou Zeid, Bastian Leibe, Jeongwan On, Seungryul Baek, Aditya Prakash, Saurabh Gupta, Kun He, Yoichi Sato, Otmar Hilliges, Hyung Jin Chang, Angela Yao
We interact with the world with our hands and see it through our own (egocentric) perspective.
1 code implementation • 7 Mar 2024 • Ruicong Liu, Takehiko Ohkawa, Mingfang Zhang, Yoichi Sato
These two stereo constraints are used in a complementary manner to generate pseudo-labels, allowing reliable adaptation.
no code implementations • 29 Nov 2023 • Yilin Wen, Hao Pan, Takehiko Ohkawa, Lei Yang, Jia Pan, Yoichi Sato, Taku Komura, Wenping Wang
We present a novel framework that concurrently tackles hand action recognition and 3D future hand motion prediction.
no code implementations • 28 Nov 2023 • Takehiko Ohkawa, Takuma Yagi, Taichi Nishimura, Ryosuke Furuta, Atsushi Hashimoto, Yoshitaka Ushiku, Yoichi Sato
We propose a novel benchmark for cross-view knowledge transfer of dense video captioning, adapting models from web instructional videos with exocentric views to an egocentric view.
no code implementations • CVPR 2023 • Takehiko Ohkawa, Kun He, Fadime Sener, Tomas Hodan, Luan Tran, Cem Keskin
To obtain high-quality 3D hand pose annotations for the egocentric images, we develop an efficient pipeline, where we use an initial set of manual annotations to train a model to automatically annotate a much larger dataset.
no code implementations • 5 Jun 2022 • Takehiko Ohkawa, Ryosuke Furuta, Yoichi Sato
In this survey, we present a systematic review of 3D hand pose estimation from the perspective of efficient annotation and learning.
no code implementations • 16 Mar 2022 • Takehiko Ohkawa, Yu-Jhe Li, Qichen Fu, Ryosuke Furuta, Kris M. Kitani, Yoichi Sato
We aim to improve the performance of regressing hand keypoints and segmenting pixel-level hand masks under new imaging conditions (e. g., outdoors) when we only have labeled images taken under very different conditions (e. g., indoors).
no code implementations • 28 Feb 2022 • Koya Tango, Takehiko Ohkawa, Ryosuke Furuta, Yoichi Sato
Detecting the positions of human hands and objects-in-contact (hand-object detection) in each video frame is vital for understanding human activities from videos.
1 code implementation • 6 Jul 2021 • Takehiko Ohkawa, Takuma Yagi, Atsushi Hashimoto, Yoshitaka Ushiku, Yoichi Sato
We validated our method on domain adaptation of hand segmentation from real and simulation images.
no code implementations • 29 Feb 2020 • Takehiko Ohkawa, Naoto Inoue, Hirokatsu Kataoka, Nakamasa Inoue
Herein, we propose Augmented Cyclic Consistency Regularization (ACCR), a novel regularization method for unpaired I2I translation.