#hand-tracking News & Analysis

4 articles tagged with #hand-tracking. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 116/10

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TextHOI-3D: Text-to-3D Hand-Object Interaction via Discrete Multi-View Generation and Joint Mesh Optimization

Researchers introduce TextHOI-3D, a framework that generates realistic 3D hand-object interactions from text descriptions by leveraging multi-view visual generation as an intermediate representation. The staged approach significantly improves geometric accuracy and physical plausibility compared to single-view methods, with penetration volume reduced by 96% and object distance error by 71%.

AINeutralarXiv – CS AI · May 126/10

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Monocular Biomechanical Tracking of Fingers with Inverse Kinematics to Foundation Models

Researchers developed a method combining SAM 3D Body foundation models with inverse kinematics to accurately track finger joint angles from single monocular video, achieving approximately 10-degree accuracy in finger tracking and 6mm hand position errors. The approach ports existing AI models to JAX and MuJoCo for GPU-accelerated optimization, enabling clinical applications for monitoring hand movement and range of motion from standard video without specialized multi-camera setups.

AINeutralarXiv – CS AI · May 126/10

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PhysHanDI: Physics-Based Reconstruction of Hand-Deformable Object Interactions

PhysHanDI introduces a physics-based framework for reconstructing 3D hand-object interactions involving deformable materials like cloth and soft objects. By simulating physically plausible object deformations driven by hand movements and using inverse physics to refine hand reconstruction, the method achieves superior performance in reconstruction and prediction tasks compared to existing approaches.

AINeutralarXiv – CS AI · Mar 176/10

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EgoGrasp: World-Space Hand-Object Interaction Estimation from Egocentric Videos

EgoGrasp introduces the first method to reconstruct world-space hand-object interactions from egocentric videos using open-vocabulary objects. The multi-stage framework combines vision foundation models with body-guided diffusion models to achieve state-of-the-art performance in 3D scene reconstruction and hand pose estimation.