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Tech Papers 2019: This paper summarises the first year of commercial volumetric video production at Volucap GmbH, highlighting lessons learned from evolving the 3D human body reconstruction workflow and adapting to customer-driven requirements.
Tech Papers 2019: This paper presents methods to infer user information from voice search for enhanced personalization and reduced computational cost without explicit enrolment.
Tech Papers 2019: This paper presents a modular web-based platform for photo-realistic Social VR, enabling natural communication and collaboration, and evaluates it with 313 users in 360° video and 3D volumetric VR experiences.
Tech Papers 2019: This paper presents a method for including regional content in a national DVB-T2 SFN using multiple PLPs and DVB-SIS, demonstrating in lab tests that national and regional programs can be broadcast efficiently and flexibly, with application to the Italian case.
Tech Papers 2019: This paper evaluates how voice-controlled digital assistants impact radio services, presenting a prototype that offers personalized audio features and assessing user interaction with flexible voice-driven radio access.
Tech Papers 2019: This paper presents OMAF and NBMP standards to enable efficient cloud-based 360° video creation and immersive VR experiences.
Tech Papers 2019: This paper proposes a method to evaluate multi-camera capture and view-synthesis quality for 6DoF AR/VR by comparing ray-traced reference images with depth-estimated synthesized views.
Tech Papers 2019: This paper introduces the MPEG-5 Essential Video Coding (EVC) standard, developed to balance technical performance with business requirements such as licensing, aiming to enable efficient video compression and rapid, widespread adoption across the media industry.
Tech Papers 2019: This paper introduces HEVC-based tools for encoding, decoding, and rendering ultra-high-resolution 8K–16K video, validated in immersive installations under the Immersify Project, showing improved user experience with simpler workflows.
Tech Papers 2019: This paper presents a machine learning approach to adapt TV user interfaces by analysing behavioural patterns, enabling truly personalised and adaptive viewing experiences beyond traditional remote or voice-based customisation.
