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.
Garment4D: Garment Reconstruction from Point Cloud Sequences
Authors: Fangzhou Hong, Liang Pan, Zhongang Cai, Ziwei Liu
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,928 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,034 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 | high-quality reconstruction results are qualitatively and quantitatively illustrated through extensive experiments. Codes are available at https://github.com/hongfz16/Garment4D. 3 Experiments 3.1 Datasets and Evaluation Protocols |
| Researcher Affiliation | Collaboration | 1S-Lab, Nanyang Technological University 2Sense Time Research 3Shanghai AI Laboratory EMAIL EMAIL |
| Pseudocode | No | The paper describes methods in prose and refers to networks, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are available at https://github.com/hongfz16/Garment4D. |
| Open Datasets | Yes | We establish point cloud sequence-based garment reconstruction dataset by adapting CLOTH3D [18] for our experiments. CLOTH3D is downloaded from http://chalearnlap.cvc.uab.es/dataset/38/description/. |
| Dataset Splits | No | We split the sequences to training and testing sets at the ratio of 8 : 2. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Point Net++' and adapting 'Multi-Garment Net' but does not provide specific version numbers for these or any other software components. |
| Experiment Setup | No | The paper describes the network architecture and loss functions, but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations in the main text. |