A Skeleton-Driven Neural Occupancy Representation for Articulated Hands
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Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9665713/9665818/09665895.pdf?arnumber=9665895
Reference71 articles.
1. Learning to Estimate 3D Hand Pose from Single RGB Images
2. Monocular Real-Time Hand Shape and Motion Capture Using Multi-Modal Data
3. DeepHandMesh: A Weakly-Supervised Deep Encoder-Decoder Framework for High-Fidelity Hand Mesh Modeling
4. I2l-meshnet: Image-to-lixel prediction network for accurate 3d human pose and mesh estimation from a single rgb image;moon;European Conference on Computer Vision (ECCV),2020
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2. Neural Parametric Human Hand Modeling with Point Cloud Representation;Proceedings of the 2024 International Conference on Multimedia Retrieval;2024-05-30
3. GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistency;2024 International Conference on 3D Vision (3DV);2024-03-18
4. ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation;2024 International Conference on 3D Vision (3DV);2024-03-18
5. Handformer2T: A Lightweight Regression-based Model for Interacting Hands Pose Estimation from A Single RGB Image;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03
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