Detailed Facial Geometry Recovery from Multi-View Images by Learning an Implicit Function
Authors: Yunze Xiao, Hao Zhu, Haotian Yang, Zhengyu Diao, Xiangju Lu, Xun Cao2839-2847
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use Face Scape(Yang et al. 2020; Zhu et al. 2021a) dataset to train and validate our method. The scores of CD-mean, CD-rms, and completeness are reported in Table 1. The rendered results, as well as heat maps of error distance from predicted mesh to ground-truth mesh, are shown in Figure 4, and these results of previous methods are from the fine-tuned models. From the quantitative comparison in Table 1, we can see that our method outperforms previous methods in CD-mean, CD-rms, and completeness for facial reconstruction. |
| Researcher Affiliation | Collaboration | Yunze Xiao1 , Hao Zhu1 , Haotian Yang1, Zhengyu Diao1, Xiangju Lu2, Xun Cao1 1 Nanjing University, Nanjing, China 2 i QIYI Inc, Beijing, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and data are released in https://github.com/zhuhao-nju/mvfr. |
| Open Datasets | Yes | We use Face Scape(Yang et al. 2020; Zhu et al. 2021a) dataset to train and validate our method. The Face Scape dataset contains 7120 multi-view images and corresponding 3D models... |
| Dataset Splits | No | We use Face Scape(Yang et al. 2020; Zhu et al. 2021a) dataset to train and validate our method. ...selected 80% of the remaining data as the training set and the other 20% as the testing set. While validation is mentioned, a specific percentage or count for a distinct validation split (separate from the 80/20 train/test split) is not provided. |
| Hardware Specification | Yes | We trained the model using Nvidia RTX 3090 for about 100 hours. |
| Software Dependencies | No | The paper mentions several components and baselines (e.g., Adam optimizer, group normalization, MVSNet, Pix2Pix HD) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | MSE loss is used to train the feature extractor + implicit function, and also the post-regularizer. We train our network using Adam optimizer, with the learning rate as 10^-3, and our model is trained in 200 epochs. The batch size is set to 1. |