Itziar Irigoien

Eneko Atxa Landa, Elena Lazkano, Igor Rodriguez et al.

Animating realistic avatars requires using high quality animations for every possible state the avatar can be in. This includes actions like walking or running, but also subtle movements that convey emotions and personality. Idle animations, such as standing, breathing or looking around, are crucial for realism and believability. In games and virtual applications, these are often handcrafted or recorded with actors, but this is costly. Furthermore, recording realistic idle animations can be very complex, because the actor must not know they are being recorded in order to make genuine movements. For this reasons idle animation datasets are not widely available. Nevertheless, this paper concludes that both acted and genuine idle animations are perceived as real, and that users are not able to distinguish between them. It also states that handmade and recorded idle animations are perceived differently. These two conclusions mean that recording idle animations should be easier than it is thought to be, meaning that actors can be specifically told to act the movements, significantly simplifying the recording process. These conclusions should help future efforts to record idle animation datasets. Finally, we also publish ReActIdle, a 3 dimensional idle animation dataset containing both real and acted idle motions.

Martin Bikandi, Gorka Velez, Naiara Aginako et al.

Anomaly detection, or outlier detection, is a crucial task in various domains to identify instances that significantly deviate from established patterns or the majority of data. In the context of autonomous driving, the identification of anomalies is particularly important to prevent safety-critical incidents, as deep learning models often exhibit overconfidence in anomalous or outlier samples. In this study, we explore different strategies for training an image semantic segmentation model with an anomaly detection module. By introducing modifications to the training stage of the state-of-the-art DenseHybrid model, we achieve significant performance improvements in anomaly detection. Moreover, we propose a simplified detector that achieves comparable results to our modified DenseHybrid approach, while also surpassing the performance of the original DenseHybrid model. These findings demonstrate the efficacy of our proposed strategies for enhancing anomaly detection in the context of autonomous driving.

Unai Zabala, Igor Rodriguez, José María Martínez-Otzeta et al.

Social robot capabilities, such as talking gestures, are best produced using data driven approaches to avoid being repetitive and to show trustworthiness. However, there is a lack of robust quantitative methods that allow to compare such methods beyond visual evaluation. In this paper a quantitative analysis is performed that compares two Generative Adversarial Networks based gesture generation approaches. The aim is to measure characteristics such as fidelity to the original training data, but at the same time keep track of the degree of originality of the produced gestures. Principal Coordinate Analysis and procrustes statistics are performed and a new Fréchet Gesture Distance is proposed by adapting the Fréchet Inception Distance to gestures. These three techniques are taken together to asses the fidelity/originality of the generated gestures.