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Hi, I am Take Ohkawa (大川 武彦).
I am a Principal Investigator of JST ACT-X Project (2020-2024) and a Research Fellow (DC1) of JSPS Research Fellowships for Young Scientists (2022-2025).
I recieved fellowships from Microsoft Research Asia (2023) and Leading House Asia ETH Zurich (2023).
I attained my master's degree with early completion in 1.5 years at The University of Tokyo under the supervision of Prof. Yoichi Sato and Prof. Jun Rekimoto.
Prior to that, I received my bachelor's degree with early completion in 3 years at the Tokyo Institute of Technology under the guidance of Prof. Nakamasa Inoue.
E-mail: ohkawa-t [at] iis.u-tokyo.ac.jp I'm looking for research positions in academia or industry starting from Fall 2025. Please feel free to contact me if you are interested in my research. |
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[Apr 2024] 8th HANDS workshop proposal got aceepted to ECCV 2024 with Dr. Linlin at CUC. See you in Miran! |
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My research focuses on computer vision for human sensing and understanding, striving to achieve this from dual perspectives: precisely estimating observable external states, such as physical poses, and inferring deeper internal states, such as intentions. This approach facilitates recognizing human interactions in the real world, connecting humans with the virtual world, and augmenting our perceptual capabilities via assistive AI systems. |
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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, and Angela Yao (* equal contribution) [Paper] We present a comprehensive summary of the HANDS23 challenge based on the AssemblyHands and ARCTIC datasets. Based on the results of the top submitted methods and more recent baselines on the leaderboards, we perform a thorough analysis on 3D hand(-object) reconstruction tasks. |
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Takehiko Ohkawa, Takuma Yagi, Taichi Nishimura, Ryosuke Furuta, Atsushi Hashimoto, Yoshitaka Ushiku, and Yoichi Sato [Paper] We present EgoYC2, 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. |
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Yilin Wen, Hao Pan, Takehiko Ohkawa, Lei Yang, Jia Pan, Yoichi Sato, Taku Komura, and Wenping Wang [Paper] We present a novel framework that concurrently tackles hand action recognition and 3D future hand motion prediction. |
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Ruicong Liu, Takehiko Ohkawa, Mingfang Zhang, Yoichi Sato IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 [Paper] We propose a novel Single-to-Dual-view adaptation (S2DHand) solution that adapts a pre-trained single-view estimator to dual views. |
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Takehiko Ohkawa, Kun He, Fadime Sener, Tomas Hodan, Luan Tran, and Cem Keskin IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 Invited Oral Presentation at Ego4D & EPIC Workshop, CVPRW, 2023 Poster Presentation at International Computer Vision Summer School (ICVSS), 2023 HANDS Workshop Benchmark Dataset, ICCVW, 2023 [Paper] [Project] [Leaderboard] [Code & Data] We present AssemblyHands, a large-scale benchmark dataset with accurate 3D hand pose annotations, to facilitate the study of challenging hand-object interactions from egocentric videos. |
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Takehiko Ohkawa, Ryosuke Furuta, and Yoichi Sato International Journal of Computer Vision (IJCV), 2023 [Paper] [Springer] [Slides] We present a systematic review of 3D hand pose estimation from the perspective of efficient annotation and learning. 3D hand pose estimation has been an important research area owing to its potential to enable various applications, such as video understanding, AR/VR, and robotics. |
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Takehiko Ohkawa, Yu-Jhe Li, Qichen Fu, Ryosuke Furuta, Kris M. Kitani, and Yoichi Sato European Conference on Computer Vision (ECCV), 2022 Invited Poster Presentation at HANDS and HBHA workshops, ECCVW, 2022 Invited Oral Presentation at Meeting on Image Recognition and Understanding (MIRU), 2023 [Paper] [Project] [Slides] We tackled domain adaptation of hand keypoint regression and hand segmentation to in-the-wild egocentric videos with new imaging conditions (e.g., Ego4D). |
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Koya Tango, Takehiko Ohkawa, Ryosuke Furuta, and Yoichi Sato HANDS, European Conference on Computer Vision Workshops (ECCVW), 2022 [Paper] We propose Background Mixup augmentation that leverages data-mixing regularization for hand-object detection while avoiding unintended effect produced by naive Mixup. |
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Takehiko Ohkawa, Takuma Yagi, Atsushi Hashimoto, Yoshitaka Ushiku, and Yoichi Sato IEEE Access, 2021 [Paper] [IEEE Xplore] [Project] [Code & Data] We developed a domain adaptation method for hand segmentation, consisting of appearance gap reduction by stylization and learning with pseudo-labels generated by network consensus. |
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Takehiko Ohkawa, Naoto Inoue, Hirokatsu Kataoka, and Nakamasa Inoue International Conference on Pattern Recognition (ICPR), 2020 [Paper] We developed extended consistency regularization for stabilizing the training of image translation models using real, fake, and reconstructed samples. |
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[Nov 2020 - Present] Research assistant, Sato Lab, UTokyo |
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JSPS Research Fellowship for Young Scientists (DC1), 2022-2025 |
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Professional Service:
Workshop organizers: HANDS (ECCV'24, ICCV'23) Lab Visits:
Conference Participation:
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© Takehiko Ohkawa / Design: jonbarron |