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Multimodal Active Measurement for Human Mesh Recovery in Close Proximity
Takahiro Maeda;Keisuke Takeshita;Norimichi Ukita;Kazuhito Tanaka
For physical human-robot interactions (pHRI), a robot needs to estimate the accurate body pose of a target person. However, in these pHRI scenarios, the robot cannot fully observe the target person's body with equipped cameras because the target person must be close to the robot for physical interaction. This close distance leads to severe truncation and occlusions and thus results in poor accurac...Show More
Fast Inference and Update of Probabilistic Density Estimation on Trajectory Prediction
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (12)
Safety-critical applications such as autonomous vehicles and social robots require fast computation and accurate probability density estimation on trajectory prediction. To address both requirements, this paper presents a new normalizing flow-based trajectory prediction model named FlowChain. FlowChain is a stack of conditional continuously-indexed flows (CIFs) that are expressive and allow analyt...Show More
MotionAug: Augmentation with Physical Correction for Human Motion Prediction
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (11)
This paper presents a motion data augmentation scheme incorporating motion synthesis encouraging diversity and motion correction imposing physical plausibility. This motion synthesis consists of our modified Variational AutoEncoder (VAE) and Inverse Kinematics (IK). In this VAE, our proposed sampling-near-samples method generates various valid motions even with insufficient training motion data. O...Show More
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
Goutam Bhat;Martin Danelljan;Radu Timofte;Kazutoshi Akita;Wooyeong Cho;Haoqiang Fan;Lanpeng Jia;Daeshik Kim;Bruno Lecouat;Youwei Li;Shuaicheng Liu;Ziluan Liu;Ziwei Luo;Takahiro Maeda;Julien Mairal;Christian Micheloni;Xuan Mo;Takeru Oba;Pavel Ostyakov;Jean Ponce;Sanghyeok Son;Jian Sun;Norimichi Ukita;Rao Muhammad;Umer Youliang Yan;Lei Yu;Magauiya Zhussip;Xueyi Zou
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (22)
This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times higher resolution. The challenge contained two tracks; Track 1 evaluating on synthetically generated data, and Track 2 using real-world bursts from mobile camera. In the final testing phase, 6 teams submitted results using ...Show More
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
Goutam Bhat;Martin Danelljan;Radu Timofte;Kazutoshi Akita;Wooyeong Cho;Haoqiang Fan;Lanpeng Jia;Daeshik Kim;Bruno Lecouat;Youwei Li;Shuaicheng Liu;Ziluan Liu;Ziwei Luo;Takahiro Maeda;Julien Mairal;Christian Micheloni;Xuan Mo;Takeru Oba;Pavel Ostyakov;Jean Ponce;Sanghyeok Son;Jian Sun;Norimichi Ukita;Rao Muhammad;Umer Youliang Yan;Lei Yu;Magauiya Zhussip;Xueyi Zou
Year: 2021 | Conference Paper |
Data Augmentation for Human Motion Prediction
2021 17th International Conference on Machine Vision and Applications (MVA)
Year: 2021 | Conference Paper |
Human motion prediction is seldom deployed to real-world tasks due to difficulty in collecting a huge amount of motion data. We propose two motion data augmentation approaches using Variational AutoEn-coder (VAE) and Inverse Kinematics (IK). Our VAE-based generative model with adversarial training and sampling near samples generates various motions even with insufficient original motion data. Our ...Show More
Automatic Human Pose Annotation for Loose-fitting Clothes
Takuya Matsumoto;Kodai Shimosato;Takahiro Maeda;Tatsuya Murakami;Koji Murakoso;Kazuhiko Mino;Norimichi Ukita
2019 16th International Conference on Machine Vision Applications (MVA)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
This paper proposes a framework for automatically annotating the keypoints of a human body in images for learning 2D pose estimation models. While considerable contributions in the community provide us a huge number of pose-annotated images, all of them mainly focus on people wearing common clothes, which are relatively easy to annotate the body keypoints. This paper, on the other hand, focuses on...Show More
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