Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Geometry-aware Two-scale PIFu Representation for Human Reconstruction
Authors: Zheng Dong, Ke Xu, Ziheng Duan, Hujun Bao, Weiwei Xu, Rynson Lau
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 22,889 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,372 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of our approach in reconstructing facial details and bodies of different poses and its superiority over state-of-the-art methods. |
| Researcher Affiliation | Academia | Zheng Dong1 Ke Xu2 Ziheng Duan1 Hujun Bao1 Weiwei Xu 1 Rynson W.H. Lau2 1State Key Lab of CAD&CG, Zhejiang University 2 City University of Hong Kong |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] |
| Open Datasets | Yes | We use the THuman2.0 [100] dataset which contains 500 high-quality 3D human scans to train and validate our network. We split the dataset into training and test sets with a ratio of 4:1. For PIFu-Face, we pretrain Ff using the Face Scape [94] dataset, which contains 3D head models of different people and expressions. |
| Dataset Splits | No | We use the THuman2.0 [100] dataset which contains 500 high-quality 3D human scans to train and validate our network. We split the dataset into training and test sets with a ratio of 4:1. |
| Hardware Specification | No | This information is provided in the supplemental material. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers in the main text. |
| Experiment Setup | No | We explain the implementation details in the supplemental material. |