SiMA-Hand: Boosting 3D Hand-Mesh Reconstruction by Single-to-Multi-View Adaptation
Authors: Yinqiao Wang, Hao Xu, Pheng Ann Heng, Chi-Wing Fu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on the Dex-YCB and Han Co benchmarks with challenging objectand self-caused occlusion cases, manifesting that Si MA-Hand consistently achieves superior performance over the state of the arts. |
| Researcher Affiliation | Academia | Yinqiao Wang1,2, Hao Xu1,2, Pheng-Ann Heng1,2, Chi-Wing Fu1,2 1Department of Computer Science and Engineering, CUHK 2Institute of Medical Intelligence and XR, CUHK |
| Pseudocode | No | The paper describes its methods in detail with mathematical formulations and diagrams but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | Code will be released on https://github.com/Joyboy Wang/Si MA-Hand Pytorch. |
| Open Datasets | Yes | We conduct experiments on Dex-YCB (Chao et al. 2021) and Han Co (Zimmermann, Argus, and Brox 2021). |
| Dataset Splits | No | For Dex-YCB, the paper states 'We adopt the default S0 train/test split with 406,888/78,768 samples for training/testing.' For Han Co, it describes training and testing sets, but a specific validation split is not explicitly mentioned. |
| Hardware Specification | Yes | We train Si MA-Hand on four NVidia Titan V GPUs, and the Adam optimizer (Kingma and Ba 2014) is adopted. The batch size in training is set to 64 for MVR-Hand and 128/32 for SVRHand. The FPS is tested on an NVidia RTX 2080Ti. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or CUDA versions). |
| Experiment Setup | Yes | We follow (Chen et al. 2022) to pre-train the feature encoder network and adopt a two-stage strategy as (Xu et al. 2023) to stabilize the training. We train Si MA-Hand on four NVidia Titan V GPUs, and the Adam optimizer (Kingma and Ba 2014) is adopted. The batch size in training is set to 64 for MVR-Hand and 128/32 for SVRHand. The input image is resized to 128 128 and augmented by random scaling, rotating, and color jittering. All N = 8 views are used for training the MVR-Hand. |