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Mateo Vallejo

dblp:374/3549 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2025
0009-0008-8365-2405ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 3 · 3 since 2021Human-computer interaction and ubiquitous computing · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
3 papers
Virtual and augmented reality · 68% Image and video processing · 16% Multimedia analysis and retrieval · 16%
Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 100%

Topics — the 7 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Virtual and augmented reality
immersive interaction
0.912025
Audiovisual Disparities in VR: Impact on Spatial Perception · ISMAR 2025
Virtual and augmented reality
spatial perception
0.912025
Audiovisual Disparities in VR: Impact on Spatial Perception · ISMAR 2025
Multimedia analysis and retrieval › audio-visual learning
audio-visual saliency
0.812024
AViSal360: Audiovisual Saliency Prediction for 360° Video · ISMAR 2024
Image and video processing
saliency detection
0.812024
AViSal360: Audiovisual Saliency Prediction for 360° Video · ISMAR 2024
Virtual and augmented reality
presence
0.312025
Audiovisual Disparities in VR: Impact on Spatial Perception · ISMAR 2025
Virtual and augmented reality › immersive video
360-degree video
0.212024
AViSal360: Audiovisual Saliency Prediction for 360° Video · ISMAR 2024
Virtual and augmented reality › auditory perception › psychoacoustics
spatial audio perception
0.212024
Modeling the Impact of Head-Body Rotations on Audio-Visual Spatial Perception for Virtual Reality Applications · IEEE Trans. Vis. Comput. Graph. 2024

Methods — techniques the papers use, named apart from their topics

user study · 2.4psychometric functions · 1.5multimodal fusion · 0.8deep learning · 0.8
YearPublicationVenuePosition
2025 Audiovisual Disparities in VR: Impact on Spatial Perception
abstract
Virtual reality (VR) experiences often leverage rich and spatialized multimodal environments to increase immersion and engagement. This demands a consistent spatial perception of audiovisual stimuli, since perceived discrepancies can disrupt the sense of presence. In this work, we investigate the consequences of two types of spatial audiovisual disparities: true disparity, where there is a measurable spatial offset between auditory and visual cues, and perceptual disparity, where users report misalignment despite cues being colocated. Unlike most previous studies that employed controlled but simplified experimental setups, our research focuses on complex, realistic VR environments, allowing us to assess the actual implications for VR content design. Our experiments indicate that users are highly sensitive to true audiovisual disparities in controlled environments, detecting even minor misalignments. However, when engaged in additional tasks within realistic settings, their ability to notice such discrepancies diminishes significantly. We also observed that previously found perceptual disparities persist in complex audiovisual environments. However, we identify self-initiated head rotations as a key factor; its absence prevents the effect entirely. We hope our findings offer practical insights for designing more immersive and perceptually coherent VR experiences.
Edurne Bernal-Berdun, Mateo Vallejo, Qi Sun 0003, Ana Serrano, Diego Gutierrez
ISMAR2
2024 AViSal360: Audiovisual Saliency Prediction for 360° Video
abstract
Saliency prediction in 360° video plays an important role in modeling visual attention, and can be leveraged for content creation, compression techniques, or quality assessment methods, among others. Visual attention in immersive environments depends not only on visual input, but also on inputs from other sensory modalities, primarily audio. Despite this, only a minority of saliency prediction models have incorporated auditory inputs, and much remains to be explored about what auditory information is relevant and how to integrate it in the prediction. In this work, we propose an audiovisual saliency model for 360° video content, AViSal360. Our model integrates both spatialized and semantic audio information, together with visual inputs. We perform exhaustive comparisons to demonstrate both the actual relevance of auditory information in saliency prediction, and the superior performance of our model when compared to previous approaches.
Edurne Bernal-Berdun, Jorge Pina, Mateo Vallejo, Ana Serrano, Belén Masiá
ISMAR3
2024 Modeling the Impact of Head-Body Rotations on Audio-Visual Spatial Perception for Virtual Reality Applications
abstract
Humans perceive the world by integrating multimodal sensory feedback, including visual and auditory stimuli, which holds true in virtual reality (VR) environments. Proper synchronization of these stimuli is crucial for perceiving a coherent and immersive VR experience. In this work, we focus on the interplay between audio and vision during localization tasks involving natural head-body rotations. We explore the impact of audio-visual offsets and rotation velocities on users' directional localization acuity for various viewing modes. Using psychometric functions, we model perceptual disparities between visual and auditory cues and determine offset detection thresholds. Our findings reveal that target localization accuracy is affected by perceptual audio-visual disparities during head-body rotations, but remains consistent in the absence of stimuli-head relative motion. We then showcase the effectiveness of our approach in predicting and enhancing users' localization accuracy within realistic VR gaming applications. To provide additional support for our findings, we implement a natural VR game wherein we apply a compensatory audio-visual offset derived from our measured psychometric functions. As a result, we demonstrate a substantial improvement of up to 40% in participants' target localization accuracy. We additionally provide guidelines for content creation to ensure coherent and seamless VR experiences.
Edurne Bernal-Berdun, Mateo Vallejo, Qi Sun 0003, Ana Serrano, Diego Gutierrez
IEEE Trans. Vis. Comput. Graph.2