Hand Pose Estimation
85 papers with code • 10 benchmarks • 22 datasets
Hand pose estimation is the task of finding the joints of the hand from an image or set of video frames.
( Image credit: Pose-REN )
Libraries
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Latest papers
Fingerspelling PoseNet: Enhancing Fingerspelling Translation with Pose-Based Transformer Models
We also propose a novel two-stage inference approach that re-ranks the hypotheses using the language model capabilities of the decoder.
RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation
The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited.
OCHID-Fi: Occlusion-Robust Hand Pose Estimation in 3D via RF-Vision
To overcome the challenge in labeling RF imaging given its human incomprehensible nature, OCHID-Fi employs a cross-modality and cross-domain training process.
MuTr: Multi-Stage Transformer for Hand Pose Estimation from Full-Scene Depth Image
This work presents a novel transformer-based method for hand pose estimation—DePOTR.
A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image
3D interacting hand pose estimation from a single RGB image is a challenging task, due to serious self-occlusion and inter-occlusion towards hands, confusing similar appearance patterns between 2 hands, ill-posed joint position mapping from 2D to 3D, etc.. To address these, we propose to extend A2J-the state-of-the-art depth-based 3D single hand pose estimation method-to RGB domain under interacting hand condition.
Robustness Evaluation in Hand Pose Estimation Models using Metamorphic Testing
We found that on average more than 80\% of the hands could not be identified by BodyHands, and at least 50\% of hands could not be identified by MediaPipe hands when diagonal motion blur is introduced, while an average of more than 50\% of strongly underexposed hands could not be correctly estimated by NSRM hand.
HaMuCo: Hand Pose Estimation via Multiview Collaborative Self-Supervised Learning
Recent advancements in 3D hand pose estimation have shown promising results, but its effectiveness has primarily relied on the availability of large-scale annotated datasets, the creation of which is a laborious and costly process.
Handy: Towards a High Fidelity 3D Hand Shape and Appearance Model
Currently, most of the state-of-the-art reconstruction and pose estimation methods rely on the low polygon MANO model.
Harmonious Feature Learning for Interactive Hand-Object Pose Estimation
Notably, the performance of our model on hand pose estimation even surpasses that of existing works that only perform the single-hand pose estimation task.
HandR2N2: Iterative 3D Hand Pose Estimation Using a Residual Recurrent Neural Network
3D hand pose estimation is a critical task in various human-computer interaction applications.