DLCA-Recon: Dynamic Loose Clothing Avatar Reconstruction from Monocular Videos
Authors: Chunjie Luo, Fei Luo, Yusen Wang, Enxu Zhao, Chunxia Xiao
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 luochunjie@whu.edu.cn, luofei@whu.edu.cn, wangyusen@whu.edu.cn, zhaoenxu@whu.edu.cn, cxxiao@whu.edu.cn |
| 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. |