9 citations · 27 across the 17 of their papers we have counts for
23 papers
YUBI-Real2Sim: 3DGS Scene Verification for Reliable Policy Evaluation
Hiroki Nishizawa, Takehiko Ohkawa, Nicola Scianca +3
Evaluating VLA policies on real robots is costly, time-consuming, and difficult to reproduce, making real-to-sim simulation essential for scalable evaluation. While 3D Gaussian Spl…
YUBI-STAG: Contact and Semantic-Rich Alignment for VLAs via Automated Video-Language Grounding
Masatoshi Tateno, Takehiko Ohkawa, Yueh-Hua Wu +4
Vision-Language-Action (VLA) models acquire broad manipulation capabilities via large-scale pretraining, yet eliciting them through language requires fine-grained alignment between…
RoboPace: Contact-Aware Time-Optimal Retiming for Action-Chunk Policies
Mimo Shirasaka, Takehiko Ohkawa, Takuya Okubo +3
Robot manipulation data collection has been shifting from teleoperation toward robot-free demonstrations, through interfaces such as the Universal Manipulation Interface (UMI) or d…
SiMDex: Mining Similar Egocentric Videos for Cross-Embodiment Dexterous Manipulation
Nie Lin, Takehiko Ohkawa, Sijin Chen +10
Recent years have witnessed an explosive trend of scaling ego-centric human videos for robot manipulation, yet it remains unclear which data actually benefits dexterous manipulatio…
YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale
Takehiko Ohkawa, Jumpei Arima, Yuki Noguchi +16
We introduce Yielding Universal Bidigital Interface (YUBI), a finger-aligned gripper designed to enable intuitive, ergonomic, and scalable data collection for bimanual dexterous ma…
DF-Mamba: Deformable State Space Modeling for 3D Hand Pose Estimation in Interactions
Yifan Zhou, Takehiko Ohkawa, Guwenxiao Zhou +4
Modeling daily hand interactions often struggles with severe occlusions, such as when two hands overlap, which highlights the need for robust feature learning in 3D hand pose estim…
Affordance-Guided Diffusion Prior for 3D Hand Reconstruction
Naru Suzuki, Takehiko Ohkawa, Tatsuro Banno +3
How can we reconstruct 3D hand poses when large portions of the hand are heavily occluded by itself or by objects? Humans often resolve such ambiguities by leveraging contextual kn…
AssemblyHands-X: Modeling 3D Hand-Body Coordination for Understanding Bimanual Human Activities
Tatsuro Banno, Takehiko Ohkawa, Ruicong Liu +2
Bimanual human activities inherently involve coordinated movements of both hands and body. However, the impact of this coordination in activity understanding has not been systemati…
Generative Modeling of Shape-Dependent Self-Contact Human Poses
Takehiko Ohkawa, Jihyun Lee, Shunsuke Saito +7
One can hardly model self-contact of human poses without considering underlying body shapes. For example, the pose of rubbing a belly for a person with a low BMI leads to penetrati…
Leveraging RGB Images for Pre-Training of Event-Based Hand Pose Estimation
Ruicong Liu, Takehiko Ohkawa, Tze Ho Elden Tse +3
This paper presents RPEP, the first pre-training method for event-based 3D hand pose estimation using labeled RGB images and unpaired, unlabeled event data. Event data offer signif…
EmoSign: A Multimodal Dataset for Understanding Emotions in American Sign Language
Phoebe Chua, Cathy Mengying Fang, Takehiko Ohkawa +3
Unlike spoken languages where the use of prosodic features to convey emotion is well studied, indicators of emotion in sign language remain poorly understood, creating communicatio…
SiMHand: Mining Similar Hands for Large-Scale 3D Hand Pose Pre-training
Nie Lin, Takehiko Ohkawa, Yifei Huang +5
We present a framework for pre-training of 3D hand pose estimation from in-the-wild hand images sharing with similar hand characteristics, dubbed SimHand. Pre-training with large-s…
Pre-Training for 3D Hand Pose Estimation with Contrastive Learning on Large-Scale Hand Images in the Wild
Nie Lin, Takehiko Ohkawa, Mingfang Zhang +3
We present a contrastive learning framework based on in-the-wild hand images tailored for pre-training 3D hand pose estimators, dubbed HandCLR. Pre-training on large-scale images a…
Single-to-Dual-View Adaptation for Egocentric 3D Hand Pose Estimation
Ruicong Liu, Takehiko Ohkawa, Mingfang Zhang +1
The pursuit of accurate 3D hand pose estimation stands as a keystone for understanding human activity in the realm of egocentric vision. The majority of existing estimation methods…
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
Zicong Fan, Takehiko Ohkawa, Linlin Yang +21
We interact with the world with our hands and see it through our own (egocentric) perspective. A holistic 3Dunderstanding of such interactions from egocentric views is important fo…
Generative Hierarchical Temporal Transformer for Hand Pose and Action Modeling
Yilin Wen, Hao Pan, Takehiko Ohkawa +5
We present a novel unified framework that concurrently tackles recognition and future prediction for human hand pose and action modeling. Previous works generally provide isolated…
Exo2EgoDVC: Dense Video Captioning of Egocentric Procedural Activities Using Web Instructional Videos
Takehiko Ohkawa, Takuma Yagi, Taichi Nishimura +4
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.…
AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation
Takehiko Ohkawa, Kun He, Fadime Sener +3
We present AssemblyHands, a large-scale benchmark dataset with accurate 3D hand pose annotations, to facilitate the study of egocentric activities with challenging hand-object inte…
Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey
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. 3D hand pose estimation has been an important r…
Domain Adaptive Hand Keypoint and Pixel Localization in the Wild
Takehiko Ohkawa, Yu-Jhe Li, Qichen Fu +3
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…
Background Mixup Data Augmentation for Hand and Object-in-Contact Detection
Koya Tango, Takehiko Ohkawa, Ryosuke Furuta +1
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. For training an o…
Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation
Takehiko Ohkawa, Takuma Yagi, Atsushi Hashimoto +2
Hand segmentation is a crucial task in first-person vision. Since first-person images exhibit strong bias in appearance among different environments, adapting a pre-trained segment…
Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation
Takehiko Ohkawa, Naoto Inoue, Hirokatsu Kataoka +1
Unpaired image-to-image (I2I) translation has received considerable attention in pattern recognition and computer vision because of recent advancements in generative adversarial ne…