PG2021 Short Papers, Posters, and Work-in-Progress Papers

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2633094

Browse

Browsing PG2021 Short Papers, Posters, and Work-in-Progress Papers by Subject "Computer graphics"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Neural Proxy: Empowering Neural Volume Rendering for Animation
    (The Eurographics Association, 2021) Sin, Zackary P. T.; Ng, Peter H. F.; Leong, Hong Va; Lee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkhard
    Achieving photo-realistic result is an enticing proposition for the computer graphics community. Great progress has been achieved in the past decades, but the cost of human expertise has also grown. Neural rendering is a promising candidate for reducing this cost as it relies on data to construct the scene representation. However, one key component for adapting neural rendering for practical use is currently missing: animation. There seems to be a lack of discussion on how to enable neural rendering works for synthesizing frames for unseen animations. To fill this research gap, we propose neural proxy, a novel neural rendering model that utilizes animatable proxies for representing photo-realistic targets. Via a tactful combination of components from neural volume rendering and neural texture, our model is able to render unseen animations without any temporal learning. Experiment results show that the proposed model significantly outperforms current neural rendering works.
  • Thumbnail Image
    Item
    User-centred Depth Estimation Benchmarking for VR Content Creation from Single Images
    (The Eurographics Association, 2021) Dickson, Anthony; Knott, Alistair; Zollmann, Stefanie; Lee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkhard
    The capture and creation of 3D content from a device equipped with just a single RGB camera has a wide range of applications ranging from 3D photographs and panoramas to 3D video. Many of these methods rely on depth estimation models to provide the necessary 3D data, mainly neural network models. However, the metrics used to evaluate these models can be difficult to interpret and to relate to the quality of 3D/VR content derived from these models. In this work, we explore the relationship between the widely used depth estimation metrics, image similarly metrics applied to synthesised novel viewpoints, and user perception of quality and similarity on these novel viewpoints. Our results indicate that the standard metrics are indeed a good indicator of 3D quality, and that they correlate with human judgements and other metrics that are designed to follow human judgements.