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..
DLCA-Recon: Dynamic Loose Clothing Avatar Reconstruction from Monocular Videos
Authors: Chunjie Luo, Fei Luo, Yusen Wang, Enxu Zhao, Chunxia Xiao
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on public and our own datasets validate that our method can produce superior results for humans with loose clothing compared to the SOTA methods. |
| Researcher Affiliation | Academia | School of Computer Science, Wuhan University, Wuhan, China EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | We evaluate our method on the Deep Cap dataset (Habermann et al. 2020), Dyna Cap dataset (Habermann et al. 2021) and our own captured data (LCJ, LYZ and ZJ). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | The optimization takes 200 epochs (about 48 hours) on a single NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions several software components like Py Torch3D, Py MAF, RVM, and SPIN, but does not provide specific version numbers for any of them or other underlying software dependencies. |
| Experiment Setup | Yes | The optimization takes 200 epochs (about 48 hours) on a single NVIDIA RTX 3090 GPU. Loss Implicit = losscolor + losseik + λlossnorm, where λ = 0.1. The optimizations for pose and skinning weights are disabled at the beginning of training, and they are gradually enabled when the non-rigid deformation network acquires a certain level of representation capacity. |