Revealing Disocclusions in Temporal View Synthesis through Infilling Vector Prediction

Vijayalakshmi Kanchana, Nagabhushan Somraj, Suraj Yadwad and Rajiv Soundararajan

Indian Institute of Science

Sample comparison videos with other competing methods
Comparison with EdgeConnect
Comparison with SynSin
Blur
Shape Distortion

Abstract

 We consider the problem of temporal view synthesis, where the goal is to predict a future video frame from the past frames using knowledge of the depth and relative camera motion. In contrast to revealing the disoccluded regions through intensity based infilling, we study the idea of an infilling vector to infill by pointing to a non-disoccluded region in the synthesized view. To exploit the structure of disocclusions created by camera motion during their infilling, we rely on two important cues, temporal correlation of infilling directions and depth. We design a learning framework to predict the infilling vector by computing a temporal prior that reflects past infilling directions and a normalized depth map as input to the network. We conduct extensive experiments on a large scale dataset we build for evaluating temporal view synthesis in addition to the SceneNet RGB-D dataset. Our experiments demonstrate that our infilling vector prediction approach achieves superior quantitative and qualitative infilling performance compared to other approaches in literature.

Temporal view synthesis for frame rate upsampling

 Alternate frames are graphically rendered and the intermediate frames are predicted using temporal view synthesis. Summary Figure

Infilling Vectors - Idea

 Disocclusions can be infilled by copying intensities from the neighborhood background regions, pointed by the predicted infilling vectors. Summary Figure

IISc VEED Database

 The database consists of 200 diverse indoor and outdoor scenes (see samples below). We use Blender to render the videos. We obtain the blend files for the scenes mainly from blendswap and turbosquid. 4 different camera trajectories are added to each scene and thus we have a total of 800 videos. The videos are rendered at full HD resolution (1920 x 1080) and at 30fps and contain 12 frames each.

Download

 Link: OneDrive
The above link contains the following data:
  1. Sample data
  2. Train and Test sets with RGB-D data, camera intrinsics and extrinsics. This data is password protected. Please fill this form to get the password. Please check your spam/junk folders for the reply mail containing the password.
  3. Links to the original blend files.
  4. License

Citation

 If you use our work, please cite our paper:
Vijayalakshmi Kanchana, Nagabhushan Somraj, Suraj Yadwad and Rajiv Soundararajan, "Revealing Disocclusions in Temporal View Synthesis through Infilling Vector Prediction", In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022.
Bibtex:
@inproceedings{kanchana2022ivp,
    title = {Revealing Disocclusions in Temporal View Synthesis through Infilling Vector Prediction},
    author = {Kanchana, Vijayalakshmi and Somraj, Nagabhushan and Yadwad, Suraj and Soundararajan, Rajiv},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    pages = {3541--3550},
    month = {January},
    year = {2022},
    doi = {10.1109/WACV51458.2022.00315}
}