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..
Learning Interaction-aware 3D Gaussian Splatting for One-shot Hand Avatars
Authors: Xuan Huang, Hanhui Li, Wanquan Liu, Xiaodan Liang, Yiqiang Yan, Yuhao Cheng, CHENQIANG GAO
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed method is validated via extensive experiments on the large-scale Inter Hand2.6M dataset, and it significantly improves the state-of-the-art performance in image quality. |
| Researcher Affiliation | Collaboration | Xuan Huang1 , Hanhui Li1 , Wanquan Liu1, Xiaodan Liang1, Yiqiang Yan2, Yuhao Cheng2, Chengqiang Gao1 1Shenzhen Campus of Sun Yat-Sen University 2Lenovo Research |
| Pseudocode | No | The paper describes the methodology in text and diagrams but does not provide any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project Page: https://github.com/Xuan Huang0/Guassian Hand. |
| Open Datasets | Yes | Our experiments are conducted on the publicly available Interhand2.6M dataset [19] (CC-BY-NC 4.0 licensed) |
| Dataset Splits | No | The paper specifies a 'training set' and 'testing set' but does not explicitly detail a separate 'validation' split with sizes or percentages. |
| Hardware Specification | Yes | Our network is trained on three A6000 GPUs |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' but does not specify versions for programming languages, machine learning frameworks, or other key software libraries. |
| Experiment Setup | Yes | Our network is trained on three A6000 GPUs using the Adam optimizer [44] with the learning rates of 1 10 4 for eight epochs. Loss weights in Eq. (3) are set as λrgb = 10.0, λV GG = 0.1. For interaction detection, we set Nc = 100 and T = 90. For self-adaptive GRM, we set Td = 0.1 and Ts = 0.9. ... The one-shot fitting takes 50 optimization steps with the learning rate of 1 10 2. ... Loss weights in Eq. (4,5) are set as λmask = 1.0, λreg = 0.01. |