no code implementations • 21 Mar 2024 • Leyuan Sun, Asako Kanezaki, Guillaume Caron, Yusuke Yoshiyasu
In this study, we propose a data-driven, modular-based approach, trained on a dataset that incorporates common-sense knowledge of object-to-room relationships extracted from a large language model.
no code implementations • 4 Jan 2024 • Lukas Meyer, Floris Erich, Yusuke Yoshiyasu, Marc Stamminger, Noriaki Ando, Yukiyasu Domae
We introduce Physically Enhanced Gaussian Splatting Simulation System (PEGASUS) for 6DOF object pose dataset generation, a versatile dataset generator based on 3D Gaussian Splatting.
1 code implementation • 21 Sep 2023 • Floris Erich, Naoya Chiba, Yusuke Yoshiyasu, Noriaki Ando, Ryo Hanai, Yukiyasu Domae
We present NeuralLabeling, a labeling approach and toolset for annotating a scene using either bounding boxes or meshes and generating segmentation masks, affordance maps, 2D bounding boxes, 3D bounding boxes, 6DOF object poses, depth maps and object meshes.
1 code implementation • 16 Apr 2023 • Leyuan Sun, Guanqun Ding, Yue Qiu, Yusuke Yoshiyasu, Fumio Kanehiro
A synthetic multi-modal dataset is made public to validate the generalization ability of the proposed fusion strategy, which also works for other combinations of different modalities.
1 code implementation • CVPR 2023 • Yusuke Yoshiyasu
DeFormer iteratively fits a body mesh model to an input image via a mesh alignment feedback loop formed within a transformer decoder that is equipped with efficient body mesh driven attention modules: 1) body sparse self-attention and 2) deformable mesh cross attention.
Ranked #21 on 3D Human Pose Estimation on 3DPW
1 code implementation • 27 Jul 2022 • Rohan Pratap Singh, Iori Kumagai, Antonio Gabas, Mehdi Benallegue, Yusuke Yoshiyasu, Fumio Kanehiro
In many robotic applications, the environment setting in which the 6-DoF pose estimation of a known, rigid object and its subsequent grasping is to be performed, remains nearly unchanging and might even be known to the robot in advance.
no code implementations • 24 Mar 2022 • Rui Fukushima, Kei Ota, Asako Kanezaki, Yoko SASAKI, Yusuke Yoshiyasu
This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-term observations of objects and scenes.
1 code implementation • 7 Nov 2020 • Rohan Pratap Singh, Mehdi Benallegue, Yusuke Yoshiyasu, Fumio Kanehiro
The sparse representation leads to the development of a dense model and the pose labels for each image frame in the set of scenes.
no code implementations • 31 Oct 2020 • Kei Ota, Devesh K. Jha, Tadashi Onishi, Asako Kanezaki, Yusuke Yoshiyasu, Yoko SASAKI, Toshisada Mariyama, Daniel Nikovski
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution.
no code implementations • 3 Mar 2020 • Kei Ota, Yoko SASAKI, Devesh K. Jha, Yusuke Yoshiyasu, Asako Kanezaki
Specifically, we train a deep convolutional network that can predict collision-free paths based on a map of the environment-- this is then used by a reinforcement learning algorithm to learn to closely follow the path.
no code implementations • 27 Jul 2019 • Yusuke Yoshiyasu, Lucas Gamez
In this paper, we address the problem of learning 3D human pose and body shape from 2D image dataset, without having to use 3D dataset (body shape and pose).
no code implementations • 29 Dec 2018 • Yusuke Yoshiyasu, Ryusuke Sagawa, Ko Ayusawa, Akihiko Murai
In this paper, we present Skeleton Transformer Networks (SkeletonNet), an end-to-end framework that can predict not only 3D joint positions but also 3D angular pose (bone rotations) of a human skeleton from a single color image.
Ranked #279 on 3D Human Pose Estimation on Human3.6M
no code implementations • 27 Sep 2016 • Sören Pirk, Vojtech Krs, Kaimo Hu, Suren Deepak Rajasekaran, Hao Kang, Bedrich Benes, Yusuke Yoshiyasu, Leonidas J. Guibas
We introduce a new general representation for proximal interactions among physical objects that is agnostic to the type of objects or interaction involved.
no code implementations • CVPR 2014 • Yusuke Yoshiyasu, Eiichi Yoshida, Kazuhito Yokoi, Ryusuke Sagawa
We present a nonrigid shape matching technique for establishing correspondences of incomplete 3D surfaces that exhibit intrinsic reflectional symmetry.