Hand Visibility Detector: Per-Keypoint Visibility Estimation for Hands
Summary
This paper introduces Hand Visibility Detector, a dedicated model for per-joint hand visibility estimation, and demonstrates its benefit for multi-view 3D hand pose annotation via visibility-weighted triangulation.
View Cached Full Text
Cached at: 08/13/26, 03:33 PM
Paper page - Hand Visibility Detector: Per-Keypoint Visibility Estimation for Hands
Source: https://huggingface.co/papers/2608.11574
Abstract
This work introduces a dedicated model for per-joint hand visibility estimation and demonstrates its benefit for multi-view 3D hand pose annotation.
Hand Pose Estimation(HPE) is a fundamental technology for various applications such as AR/VR and robotics. In these applications, the visibility of each hand joint in the image is crucial for assessing the reliability of estimation results under occlusion. However, most existing HPE methods output joint positions without explicitly indicating their visibility. Although some methods account for occlusion or visibility,visibility estimationhas mainly been used as an auxiliary signal for improving pose estimation. To our knowledge, per-joint handvisibility estimationhas not been systematically studied as a standalone task. In this work, we proposeHand Visibility Detector, a model for estimating the visibility of individual hand joints, and present the first systematic investigation ofvisibility estimationas an independent task. We show that leveraging the prior knowledge of HPE models pretrained on large-scale data as a backbone yields high performance in this task. We further demonstrate the utility ofHand Visibility Detectoron a downstream task of 3D hand pose annotation viamulti-view triangulationof 2D keypoints, showing thatvisibility-weighted triangulationreducesreprojection error. Our method is released as a ready-to-use package, and the code and demo are available at https://github.com/ryhara/hand_visibility_detector .
View arXiv pageView PDFGitHub30Add to collection
Get this paper in your agent:
hf papers read 2608\.11574
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper1
#### ryhara/hand-visibility-detector Keypoint Detection• Updatedabout 14 hours ago • 1
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2608.11574 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2608.11574 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
The Surprising Effectiveness of Video Diffusion Models for Hand Motion Reconstruction
ViDiHand leverages pretrained video diffusion model representations to reconstruct 4D hand motion directly from egocentric video frames, outperforming existing methods on ARCTIC, HOT3D, and HOI4D without detectors or optimization.
Training-Free Dense Hand Contact Estimation with Multi-Modal Large Language Models
The paper presents ContactPrompt, a training-free and zero-shot approach for dense hand contact estimation using multi-modal large language models, outperforming supervised methods without training.
Active Perception for Embodied Disambiguation
This paper proposes an active-perception framework for embodied target disambiguation, using vision-language models to decide based on accumulated visual evidence and interaction information. Real-robot experiments demonstrate its effectiveness in combining physical observation with user-intent clarification.
PointDiT: Pixel-Space Diffusion for Monocular Geometry Estimation
PointDiT presents a minimalist pixel-space diffusion transformer using a plain ViT architecture for monocular geometry estimation, outperforming complex latent-based models while maintaining simplicity and robustness in ambiguous regions.
One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models
Introduces MultiDepth-3k, a benchmark to evaluate depth-layer preferences in monocular depth foundation models, and shows Laplacian Visual Prompting can alter reported depth layers, suggesting complementary geometric hypotheses exist across models.