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 [1].
Learning to Decouple the Lights for 3D Face Texture Modeling
Authors: Tianxin Huang, Zhenyu Zhang, Ying Tai, Gim Hee Lee
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | According to experiments on both single images and video sequences, we demonstrate the effectiveness of our approach in modeling facial textures under challenging illumination affected by occlusions. Our codes would be open sourced at https://github.com/Tianxinhuang/De Face.git. |
| Researcher Affiliation | Academia | Tianxin Huang1 Zhenyu Zhang2 Ying Tai2 Gim Hee Lee1 1School of Computing, National University of Singapore 2Nanjing University |
| Pseudocode | Yes | Algorithm 1 Training Process |
| Open Source Code | Yes | Our codes would be open sourced at https://github.com/Tianxinhuang/De Face.git. |
| Open Datasets | Yes | For our experiments, we utilize two datasets: Voxceleb2 [8] and Celeb AMask-HQ [29, 24]. Vox Celeb2 [8] is a diverse dataset encompassing numerous videos collected from interviews, movies and videos, where the same person may have multiple separate videos. Celeb AMask-HQ [29, 24] is a large scale face image dataset with fine attributes annotation and high resolution, widely used in face editing and generation. |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly specify a separate validation dataset split with percentages or counts. |
| Hardware Specification | Yes | We conduct all experiments on a Nvidia 2080ti GPU with a 2.9Ghz i5-9400 CPU. |
| Software Dependencies | No | The paper mentions 'Py Torch version' and 'Adam optimizer [19]' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Detailed hyper-parameter settings can be found in our supplementary. Table 6: Hyper-parameter settings. n is the number of initial light conditions presented in Fig. 2. w1 w7 2e3, 1e-3, 1.5e2, 0.5, 25, 2e3, 2.0, 1.0 Landmark Mediapipe iter0 iter3 100, 2000, 400, 200 ϵ 0.17 n 5 |