Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Monocular 3D Hand Mesh Recovery via Dual Noise Estimation
Authors: Hanhui Li, Xiaojian Lin, Xuan Huang, Zejun Yang, Zhisheng Wang, Xiaodan Liang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the large-scale Interhand2.6M dataset demonstrate that the proposed method not only improves the performance of its baseline by more than 10% but also achieves state-of-the-art performance. |
| Researcher Affiliation | Collaboration | 1Shenzhen Campus of Sun Yat-sen University, Shenzhen, China 2Tencent, Shenzhen, China 3Dark Matter AI Research, Guangzhou, China |
| Pseudocode | No | The paper does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps in a code-like format. |
| Open Source Code | Yes | Project page: https://github.com/hanhuili/DNE4Hand. |
| Open Datasets | Yes | Our experiments are conducted on the large-scale Interhand2.6M dataset (Moon et al. 2020) |
| Dataset Splits | No | The paper states 'We use all single-hand (SH) and interacting-hand (IH) images in the training set for training' and 'the whole test set is used for evaluation', but does not explicitly define a separate validation set or its specific split for reproduction. |
| Hardware Specification | Yes | Our networks are trained with 4 Ge Force RTX 4090 graphics cards. |
| Software Dependencies | No | The paper mentions software components like the Adam optimizer and ResNet-50, and refers to PyTorch3D for camera definitions, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We adopt the Adam optimizer (Kingma and Ba 2014) with a batch size of 120 and 30 training epochs. ... The initial learning rate is 10 3 and is scaled by 0.5 after each epoch until it reaches 10 6. |