Video Inpainting by Jointly Learning Temporal Structure and Spatial Details
Authors: Chuan Wang, Haibin Huang, Xiaoguang Han, Jue Wang5232-5239
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide qualitative and quantitative evaluation on three datasets, demonstrating that our method outperforms previous learning-based video inpainting methods. |
| Researcher Affiliation | Collaboration | 1Megvii (Face++) {wangchuan, huanghaibin, wangjue}@megvii.com 2Shenzhen Research Inst. of Big Data, The Chinese University of Hong Kong, Shenzhen, China hanxiaoguang@cuhk.edu.cn |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper provides a 'Project Page' URL (http://wangchuan.github.io/archive/research/videoinp/) but does not explicitly state that the source code for the methodology is available there, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | To validate our 3D-2D combined completion network, we tested on three datasets, Face Forensics (R ossler et al. 2018), 300VW (Chrysos et al. 2015) and Caltech (Doll ar et al. 2012). |
| Dataset Splits | Yes | For each dataset, we separate the whole data samples into training and validation sets and control their proportion 5 : 1. |
| Hardware Specification | Yes | The implementation is based on Tensor Flow and the network training is performed on a single NVIDIA Ge Force GTX 1080 Ti. |
| Software Dependencies | No | The paper mentions 'TensorFlow' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The Comb CN is trained with 100k iterations by an Adam optimizer, whose regression weight and learning rate are set to 0.01 and 0.001, respectively. Each frame is in 128^2 resolution, F, H, W, r are set to 32, 128, 128 and 2 respectively. We randomly generate a hole across all frames in the [0.375l, 0.5l] pixel range. |