On the impact of VR assessment on the Quality of Experience of Highly Realistic Digital Humans

Abstract

Fuelled by the increase in popularity of virtual and augmented reality applications, point clouds have emerged as a popular 3D format for acquisition and rendering of digital humans, thanks to their versatility and real-time capabilities. Due to technological constraints and real-time rendering limitations, however, the visual quality of dynamic point cloud contents is seldom evaluated using virtual and augmented reality devices, instead relying on prerecorded videos displayed on conventional 2D screens. In this study, we evaluate how the visual quality of point clouds representing digital humans is affected by compression distortions. In particular, we compare three different viewing conditions based on the degrees of freedom that are granted to the viewer: passive viewing (2DTV), head rotation (3DoF), and rotation and translation (6DoF), to understand how interacting in the virtual space affects the perception of quality. We provide both quantitative and qualitative results of our evaluation involving 78 participants, and we make the data publicly available. To the best of our knowledge, this is the first study evaluating the quality of dynamic point clouds in virtual reality, and comparing it to traditional viewing settings. Results highlight the dependency of visual quality on the content under test, and limitations in the way current data sets are used to evaluate compression solutions. Moreover, influencing factors in quality evaluation in VR, and shortcomings in how point cloud This work is funded by the European Commission H2020 program, under the grant agreement 762111, VRTogether, http://vrtogether.eu/.

Textured Mesh Quality Assessment: Large-Scale Dataset and Deep Learning-based Quality Metric

“…The pioneering studies were conducted on colorless point cloud content Javaheri et al 2017;Javaheri et al 2019;Su et al 2019]. Subsequently, the majority of the studies focused on evaluating the quality of colored point clouds Alexiou et al , 2020da Silva Cruz et al 2019;Liu et al 2021;Mekuria et al 2017;Perry et al 2020;Subramanyam et al 2020;Torlig et al 2018;van der Hooft et al 2020;Viola et al 2022;Wu et al 2021;Zerman et al 2019Zerman et al , 2020.…”

“…The experimental methodologies used were inspired by existing image/video subjective methodologies. They are mainly derived from single stimulus methods, in which participants see only one stimulus and rate its quality [Corsini et al 2007;Gutiérrez et al 2020;Lavoué et al 2006;Subramanyam et al 2020;Torkhani et al 2015;Viola et al 2022;Zerman et al 2020], double stimulus methods in which participants rate the visual degradation after seeing the reference and distorted stimuli [da Silva Cruz et al 2019;Javaheri et al 2019;Lavoué 2009;Liu et al 2021;Nehmé et al 2021b;Perry et al 2020;Silva et al 2009;Su et al 2019;Torlig et al 2018;Watson 2001], and pairwise comparison methods in which participants choose the better quality stimulus from two stimuli presented to them Christaki et al 2018;Guo et al 2016;Vanhoey et al 2017;Váša and Rus 2012]. Recently, a couple of comprehensive/comparative studies Nehmé et al 2020] evaluated the impact of the subjective methodologies on the obtained quality scores.…”

“…This approach avoids cognitive overload that can alter human judgments. The effect of adopting different modes of inspection for subjective quality assessment is still unclear/an open question as very few comparisons have been made to date [Torkhani et al 2015;Viola et al 2022].…”

“…Most of the reported experiments for both meshes and point clouds were conducted on desktop settings. Only the studies presented in [Alexiou et al 2020;Christaki et al 2018;Nehmé et al 2020;Nehmé et al 2021b;Subramanyam et al 2020;Viola et al 2022;Wu et al 2021] considered a VR environment, and those presented in [Gutiérrez et al 2020] and ] a MR and AR environment, respectively. An early attempt of 3D tele-immersion was reported in [Mekuria et al 2015].…”

Emerging Immersive Communication Systems: Overview, Taxonomy, and Good Practices for QoE Assessment

“…The conventional solution for this problem is conducting targeted experiments to only assess low-level features with shorter stimuli. For instance, to evaluate the visual quality of 360 ° (Gutiérrez et al, 2021) or point clouds (Viola et al, 2022), or the effect of delay in task performance (Brunnström et al, 2020). As an alternative approach, Orduna et al (2022) propose a methodology to combine frequent assessment of low-level features during the execution of the test with the evaluation of high-level features using post-experience questionnaires.…”