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 | Conference PDF | Archive PDF | Plain Text | 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. |