FNeVR: Neural Volume Rendering for Face Animation
Authors: Bohan Zeng, Boyu Liu, Hong Li, Xuhui Liu, Jianzhuang Liu, Dapeng Chen, Wei Peng, Baochang Zhang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our FNe VR obtains the best overall quality and performance on widely used talking-head benchmarks. |
| Researcher Affiliation | Collaboration | 1Institute of Artificial Intelligence, School of Automation Science and Electrical Engineering, Beihang University, Beijing, P.R.China 2Sino-French Engineer School, Beihang University, Beijing, P.R.China 3Huawei Noah s Ark Lab, Shenzhen, P.R.China 4Huawei, Shenzhen, P.R.China 5Zhongguancun Laboratory, Beijing, P.R.China |
| Pseudocode | No | The paper includes architectural diagrams and descriptions of methods but no explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available1. 1https://github.com/zengbohan0217/FNe VR |
| Open Datasets | Yes | Our evaluation is performed on Vox Celeb [34], which contains more than 100,000 videos covering 1,251 speakers of different identities, and Vox Celeb2 [9], which contains about 1M videos of different celebrities. |
| Dataset Splits | No | The paper mentions training and testing sets, and evaluation on 'Vox Celeb' and 'Vox Celeb2', but does not provide explicit percentages or sizes for training/validation/test splits, nor does it explicitly mention a distinct validation set with quantitative details. |
| Hardware Specification | Yes | Since our model is lightweight, we only use 2 24GB NVIDIA 3090 GPUs during training. |
| Software Dependencies | No | The paper mentions 'Mind Spore' and 'CANN' but does not specify their version numbers or other software dependencies with version details. |
| Experiment Setup | Yes | FNe VR is trained for 100 epochs, repeating the video image set 75 times per epoch. We adopt Adam [29] optimizer with learning rate η = 2 10 4, β1 = 0.5 and β2 = 0.9 for each module. |