92 research outputs found
Fehlerkaschierte Bildbasierte Darstellungsverfahren
Creating photo-realistic images has been one of the major goals in computer graphics since its early days. Instead of modeling the complexity of nature with standard modeling tools, image-based approaches aim at exploiting real-world footage directly,as they are photo-realistic by definition. A drawback of these approaches has always been that the composition or combination of different sources is a non-trivial task, often resulting in annoying visible artifacts. In this thesis we focus on different techniques to diminish visible artifacts when combining multiple images in a common image domain. The results are either novel images, when dealing with the composition task of multiple images, or novel video sequences rendered in real-time, when dealing with video footage from multiple cameras.Fotorealismus ist seit jeher eines der großen Ziele in der Computergrafik. Anstatt die Komplexität der Natur mit standardisierten Modellierungswerkzeugen nachzubauen, gehen bildbasierte Ansätze den umgekehrten Weg und verwenden reale Bildaufnahmen zur Modellierung, da diese bereits per Definition fotorealistisch sind. Ein Nachteil dieser Variante ist jedoch, dass die Komposition oder Kombination mehrerer Quellbilder eine nichttriviale Aufgabe darstellt und häufig unangenehm auffallende Artefakte im erzeugten Bild nach sich zieht. In dieser Dissertation werden verschiedene Ansätze verfolgt, um Artefakte zu verhindern oder abzuschwächen, welche durch die Komposition oder Kombination mehrerer Bilder in einer gemeinsamen Bilddomäne entstehen. Im Ergebnis liefern die vorgestellten Verfahren
neue Bilder oder neue Ansichten einer Bildsammlung oder Videosequenz, je nachdem, ob die jeweilige Aufgabe die Komposition mehrerer Bilder ist oder die Kombination mehrerer Videos verschiedener Kameras darstellt
Semi-supervised Deep Multi-view Stereo
Significant progress has been witnessed in learning-based Multi-view Stereo
(MVS) under supervised and unsupervised settings. To combine their respective
merits in accuracy and completeness, meantime reducing the demand for expensive
labeled data, this paper explores the problem of learning-based MVS in a
semi-supervised setting that only a tiny part of the MVS data is attached with
dense depth ground truth. However, due to huge variation of scenarios and
flexible settings in views, it may break the basic assumption in classic
semi-supervised learning, that unlabeled data and labeled data share the same
label space and data distribution, named as semi-supervised distribution-gap
ambiguity in the MVS problem. To handle these issues, we propose a novel
semi-supervised distribution-augmented MVS framework, namely SDA-MVS. For the
simple case that the basic assumption works in MVS data, consistency
regularization encourages the model predictions to be consistent between
original sample and randomly augmented sample. For further troublesome case
that the basic assumption is conflicted in MVS data, we propose a novel style
consistency loss to alleviate the negative effect caused by the distribution
gap. The visual style of unlabeled sample is transferred to labeled sample to
shrink the gap, and the model prediction of generated sample is further
supervised with the label in original labeled sample. The experimental results
in semi-supervised settings of multiple MVS datasets show the superior
performance of the proposed method. With the same settings in backbone network,
our proposed SDA-MVS outperforms its fully-supervised and unsupervised
baselines.Comment: This paper is accepted in ACMMM-2023. The code is released at:
https://github.com/ToughStoneX/Semi-MV
Interactive Free-Viewpoint Video Generation
Background
Free-viewpoint video (FVV) is processed video content in which viewers can freely select the viewing position and angle. FVV delivers an improved visual experience and can also help synthesize special effects and virtual reality content. In this paper, a complete FVV system is proposed to interactively control the viewpoints of video relay programs through multimedia terminals such as computers and tablets.
Methods The hardware of the FVV generation system is a set of synchronously controlled cameras, and the software generates videos in novel viewpoints from the captured video using view interpolation. The interactive interface is designed to visualize the generated video in novel viewpoints and enable the viewpoint to be changed interactively.
Results
Experiments show that our system can synthesize plausible videos in intermediate viewpoints with a view range of up to 180°
Deviation magnification: Revealing departures from ideal geometries
Structures and objects are often supposed to have idealized geometries such as straight lines or circles. Although not always visible to the naked eye, in reality, these objects deviate from their idealized models. Our goal is to reveal and visualize such subtle geometric deviations, which can contain useful, surprising information about our world. Our framework, termed Deviation Magnification, takes a still image as input, fits parametric models to objects of interest, computes the geometric deviations, and renders an output image in which the departures from ideal geometries are exaggerated. We demonstrate the correctness and usefulness of our method through quantitative evaluation on a synthetic dataset and by application to challenging natural images.Shell ResearchQatar Computing Research InstituteUnited States. Office of Naval Research (Grant N00014-09-1-1051)National Science Foundation (U.S.) (Grant CGV-1111415
Automatic Animation of Hair Blowing in Still Portrait Photos
We propose a novel approach to animate human hair in a still portrait photo.
Existing work has largely studied the animation of fluid elements such as water
and fire. However, hair animation for a real image remains underexplored, which
is a challenging problem, due to the high complexity of hair structure and
dynamics. Considering the complexity of hair structure, we innovatively treat
hair wisp extraction as an instance segmentation problem, where a hair wisp is
referred to as an instance. With advanced instance segmentation networks, our
method extracts meaningful and natural hair wisps. Furthermore, we propose a
wisp-aware animation module that animates hair wisps with pleasing motions
without noticeable artifacts. The extensive experiments show the superiority of
our method. Our method provides the most pleasing and compelling viewing
experience in the qualitative experiments and outperforms state-of-the-art
still-image animation methods by a large margin in the quantitative evaluation.
Project url: \url{https://nevergiveu.github.io/AutomaticHairBlowing/}Comment: Accepted to ICCV 202
Methods for reducing visual discomfort in stereoscopic 3D: A review
This work was supported by the EPSRC Grant EP/M01469X/1, “Geometric Evaluation of Stereoscopic Video”
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