Joint Stabilization and Direction of 360°Videos
January 14, 2019 · Declared Dead · 🏛 ACM Transactions on Graphics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Chengzhou Tang, Oliver Wang, Feng Liu, Ping Tan
arXiv ID
1901.04161
Category
cs.GR: Graphics
Citations
26
Venue
ACM Transactions on Graphics
Last Checked
5 months ago
Abstract
360° video provides an immersive experience for viewers, allowing them to freely explore the world by turning their head. However, creating high-quality 360° video content can be challenging, as viewers may miss important events by looking in the wrong direction, or they may see things that ruin the immersion, such as stitching artifacts and the film crew. We take advantage of the fact that not all directions are equally likely to be observed; most viewers are more likely to see content located at ``true north'', i.e. in front of them, due to ergonomic constraints. We therefore propose 360° video direction, where the video is jointly optimized to orient important events to the front of the viewer and visual clutter behind them, while producing smooth camera motion. Unlike traditional video, viewers can still explore the space as desired, but with the knowledge that the most important content is likely to be in front of them. Constraints can be user guided, either added directly on the equirectangular projection or by recording ``guidance'' viewing directions while watching the video in a VR headset, or automatically computed, such as via visual saliency or forward motion direction. To accomplish this, we propose a new motion estimation technique specifically designed for 360° video which outperforms the commonly used 5-point algorithm on wide angle video. We additionally formulate the direction problem as an optimization where a novel parametrization of spherical warping allows us to correct for some degree of parallax effects. We compare our approach to recent methods that address stabilization-only and converting 360° video to narrow field-of-view video.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
📜 Similar Papers
In the same crypt — Graphics
R.I.P.
👻
Ghosted
R.I.P.
👻
Ghosted
Deep Bilateral Learning for Real-Time Image Enhancement
R.I.P.
👻
Ghosted
Animating Human Athletics
R.I.P.
👻
Ghosted
BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration
R.I.P.
👻
Ghosted
Shape Transformation Using Variational Implicit Functions
R.I.P.
👻
Ghosted
ABC: A Big CAD Model Dataset For Geometric Deep Learning
Died the same way — 👻 Ghosted
R.I.P.
👻
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
👻
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
👻
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
👻
Ghosted