Realistic avatar eye and head animation using a neurobiological model of visual attention

By Laurent Itti, Nitin Dhavale, Frédéric H. Pighin. SPIE Optics + Photonics, 2004

350 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6c35413b3458762d2d62035fd64a0c979cc41fe3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Computer Vision and Pattern Recognition

In Molecular Biology

Citations in the corpus

What is XR? Towards a Framework for Augmented and Virtual Reality (2022, background): “Failure to accurately animate the head and the eyes may lead to avatars being perceived as unrealistic or inattentive, and this can significantly reduce users ’ perception of telepresence (Itti et al., 2003).”
Automatic foveation for video compression using a neurobiological model of visual attention (2004, background): “Note that our low-level motion detectors, however, are naturally insensitive to full-field translational motion, due to the global spatial competition for saliency operated in each feature map [57], [61].”
Social eye gaze in human-robot interaction (2017, background): “Neurobiological models of gaze behavior that use bottom-up saliency maps can successfully track salient targets and perform visual search in demanding scenes (Itti & Koch, 2000), leading to realistic visual attention behavior in virtual avatars (Itti, Dhavale, & Pighin, 2004, 2006).”
Making 360° Video Watchable in 2D: Learning Videography for Click Free Viewing (2017, background): “Although the research originated in static images, there is increasing work that studies video saliency [21, 35, 43].”
Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study (2013, background): “, [34]) have investigated other aspects of eye fixations, such as saccade dynamics, sequencing (Wang et al.”
Computational visual attention systems and their cognitive foundations: A survey (2010, background): “2001], flicker [Itti et al. 2003], or corners [Fraundorfer and Bischof 2003; Heidemann et al.”
Cognitive Developmental Robotics: A Survey (2009, neutral): “[97].”
Exploiting local and global patch rarities for saliency detection (2012, background): “Some models have added features such as symmetry [20], texture contrast [36], curvedness [21], or motion [41] to the basic structure.”
State-of-the-Art in Visual Attention Modeling (2013, methodology): “Some studies have also added specific features for directing attention like skin hue [120], face [167], horizontal line [93], wavelet [133], gist [92], [93], center-bias [123], curvature [124], spatial resolution [125], optical flow [15], [126], flicker [119], multiple superimposed orientations (cro”