Single-to-Dual-View Adaptation for Egocentric 3D Hand Pose Estimation: A Novel Approach

แนวคิดหลัก

The author proposes a novel Single-to-Dual-view adaptation (S2DHand) solution that adapts a pre-trained single-view estimator to dual views, eliminating the need for multi-view annotations and camera parameters.

บทคัดย่อ

The pursuit of accurate 3D hand pose estimation in egocentric vision is crucial. Existing methods face limitations with single-view inputs, leading to the proposal of an unsupervised adaptation method, S2DHand, which outperforms existing techniques under both in-dataset and cross-dataset settings.

Key points:

• Existing hand pose estimation methods rely on single-view images.
• Adding another camera can improve field-of-view and depth ambiguity.
• S2DHand adapts a pre-trained single-view estimator to dual views without requiring multi-view annotations or camera parameters.
• The method uses stereo constraints for adaptation and achieves significant improvements across all camera pairs.
• Comparison with state-of-the-art methods shows superior performance in cross-dataset settings.
• Ablation study confirms the effectiveness of each component in improving hand pose estimation.
• Hyper-parameter analysis reveals optimal values for α and β.
• Qualitative results demonstrate improved accuracy in predicting 3D hand poses under dual views.

สถิติ

"Evaluation results reveal that S2DHand achieves significant improvements on arbitrary camera pairs under both in-dataset and cross-dataset settings."
"Our method achieves substantial accuracy gains under both settings among all camera pairs."
"Quantitative results demonstrate that the S2DHand offers significant performance gains across all dual-view pairs."

คำพูด

"We propose a novel unsupervised Single-to-Dual-view adaptation framework (S2DHand) for egocentric 3D hand pose estimation."
"Our method can adapt a traditional single-view estimator for arbitrary dual views without requiring annotations or camera parameters."

ข้อมูลเชิงลึกที่สำคัญจาก

Single-to-Dual-View Adaptation for Egocentric 3D Hand Pose Estimation

by Ruicong Liu,... ที่ arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04381.pdf

สอบถามเพิ่มเติม

How does the proposed S2DHand method compare to traditional multi-camera setups

The proposed S2DHand method offers a significant advantage over traditional multi-camera setups in several key aspects. Firstly, S2DHand eliminates the need for expensive multi-view annotations during training. Traditional methods require labeled data from multiple camera views to train the model effectively, which can be time-consuming and costly. In contrast, S2DHand is unsupervised and adapts a pre-trained single-view estimator to dual views without the need for additional annotations.
Secondly, S2DHand does not rely on specific camera parameters or layouts during testing. In traditional multi-camera setups, the model becomes inapplicable if there are any changes to the camera layout or parameters. This limitation restricts the flexibility and adaptability of traditional methods. However, with S2DHand, the model can handle arbitrary dual-view pairs with unknown camera parameters, making it applicable to diverse camera settings without requiring retraining.
Overall, by offering an unsupervised adaptation process that eliminates the need for multi-view annotations and accommodates flexible camera settings, S2DHand presents a more practical and cost-effective solution compared to traditional multi-camera setups.

What are the potential implications of eliminating the need for multi-view annotations in hand pose estimation

Eliminating the need for multi-view annotations in hand pose estimation has several potential implications that can significantly impact research and applications in this field:

Cost-Effectiveness: By removing the requirement for expensive manual labeling of data from multiple viewpoints, researchers can save resources that would have been allocated towards annotation efforts.

Scalability: Without relying on labor-intensive annotation processes for each view angle or setup variation, algorithms like S2DHand can be easily scaled up to accommodate new datasets or real-world scenarios quickly.

Flexibility: The absence of multi-view annotations allows models like S2DHand to adapt seamlessly to different camera configurations or environments without needing extensive retraining or adjustments.

Generalization: Models trained without explicit knowledge of all possible viewpoints may exhibit better generalization capabilities when faced with novel perspectives or unseen scenarios during deployment.

How might advancements in AR/VR technology impact the adoption of dual-view adaptations like S2DHand

Advancements in AR/VR technology are poised to have a profound impact on the adoption of dual-view adaptations like S2DHand in various ways:

Increased Demand: As AR/VR technologies become more prevalent across industries such as gaming, education, healthcare, and manufacturing sectors; there will be a growing demand for accurate 3D hand pose estimation from egocentric viewpoints provided by dual cameras.

Enhanced Immersive Experiences: Dual-view adaptations like S2DHand can enhance immersive experiences in AR/VR applications by enabling more precise tracking of hand movements and interactions within virtual environments.

Technological Synergy: With advancements such as improved depth sensing capabilities and higher resolution cameras integrated into AR/VR headsets (e.g., Apple Vision Pro), leveraging dual views for hand pose estimation aligns well with technological trends driving industry development.

4 .Improved Interaction Design: Dual-view adaptations allow for more natural interaction design possibilities within AR/VR interfaces by accurately capturing intricate hand gestures from multiple angles.