arXiv subjects

Victor Araujo

Publications and source records attributed to Victor Araujo.

8 recordsLinked to original sources

arXiv2026

True to Tone? Quantifying Skin Tone Fidelity and Bias in Photographic-to-Virtual Human Pipelines

Accurate reproduction of facial skin tone is essential for realism, identity preservation, and fairness in Virtual Human (VH) rendering. However, most accessible avatar creation pipelines rely on photographic inputs that lack colorimetric calibration, which can introduce inconsistencies and bias. We propose a fully automatic and scalable methodology to systematically evaluate skin tone fidelity across the VH generation pipeline. Our approach defines a full workflow that integrates skin color and illumination extraction, texture recolorization, real-time rendering, and quantitative color analysis. Using facial images from the Chicago Face Database (CFD), we compare skin tone extraction strategies based on cheek-region sampling, following the literature, and multidimensional masking derived from full-face analysis. Additionally, we test both strategies with lighting isolation, using the pre-trained TRUST framework, employed without any training or optimization within our pipeline. Extracted skin tones are applied to MetaHuman textures and rendered under multiple lighting configurations. Skin tone consistency is evaluated objectively in the CIELAB color space using the $ΔE$ metric and the Individual Typology Angle (ITA). The proposed methodology operates without manual intervention and, with the exception of pre-trained illumination compensation modules, the pipeline does not include learning or training stages, enabling low computational cost and large-scale evaluation. Using this framework, we generate and analyze approximately 19,848 rendered instances. Our results show phenotype-dependent behavior of extraction strategies and consistently higher colorimetric errors for darker skin tones.

cs.CV↗

arXiv2024

Perceptual Analysis of Groups of Virtual Humans Animated using Interactive Platforms

Virtual humans (VH) have been used in Computer Graphics (CG) for many years, and perception studies have been applied to understand how people perceive them. Some studies have already examined how realism impacts the comfort of viewers. In some cases, the user's comfort is related to human identification. For example, people from a specific group may look positively at others from the same group. Gender is one of those characteristics that have in-group advantages. For example, in terms of VHs, studies have shown that female humans are more likely to recognize emotions in female VHs than in male VHs. However, there are many other variables that can impact the user perception. To aid this discussion, we conducted a study on how people perceive comfort and realism in relation to interactive VHs with different genders and expressing negative, neutral, or positive emotions in groups. We created a virtual environment for participants to interact with groups of VHs, which are interactive and should evolve in real-time, using a popular game engine. To animate the characters, we opted for cartoon figures that are animated by tracking the facial expressions of actors, using available game engine platforms to conduct the driven animation. Our results indicate that the emotion of the VH group impacts both comfort and realism perception, even by using simple cartoon characters in an interactive environment. Furthermore, the findings suggest that individuals reported feeling better with a positive emotion compared to a negative emotion, and that negative emotion recognition is impacted by the gender of the VHs group. Additionally, although we used simple characters, the results are consistent with the perception obtained when analysing realistic the state-of-the-art virtual humans, which positive emotions tend to be more correctly recognized than negative ones.

cs.HC↗

arXiv2023

Evaluating the Uncanny Valley Effect in Dark Colored Skin Virtual Humans

With the rapid advancement of technology, the design of virtual humans has led to a very realistic user experience, such as in movies, video games, and simulations. As a result, virtual humans are becoming increasingly similar to real humans. However, following the Uncanny Valley (UV) theory, users tend to feel discomfort when watching entities with anthropomorphic traits that differ from real humans. This phenomenon is related to social identity theory, where the observer looks for something familiar. In Computer Graphics (CG), techniques used to create virtual humans with dark skin tones often rely on approaches initially developed for rendering characters with white skin tones. Furthermore, most CG characters portrayed in various media, including movies and games, predominantly exhibit white skin tones. Consequently, it is pertinent to explore people's perceptions regarding different groups of virtual humans. Thus, this paper aims to examine and evaluate the human perception of CG characters from different media, comparing two types of skin colors. The findings indicate that individuals felt more comfortable and perceived less realism when watching characters with dark colored skin than those with white colored skin. Our central hypothesis is that dark colored characters, rendered with classical developed algorithms, are considered more cartoon than realistic and placed on the left of the Valley in the UV chart.

cs.HC↗

arXiv2023

Crafting Realistic Virtual Humans: Unveiling Perspectives on Human Perception, Crowds, and Embodied Conversational Agents

Virtual Humans (VHs) were first developed more than 50 years ago and have undergone significant advancements since then. In the past, creating and animating VHs was a complex task. However, contemporary commercial and freely available technology now empowers users, programmers, and designers to create and animate VHs with relative ease. These technologies have even reached a point where they can replicate the authentic characteristics and behaviors of real actors, resulting in VHs that are visually convincing and behaviorally lifelike. This paper explores three closely related research areas in the context of virtual humans and discusses the far-reaching implications of highly realistic characters within these domains.

cs.HC↗

arXiv2023

Revisiting Micro and Macro Expressions in Computer Graphics Characters

This paper presents the reproduction of two studies focused on the perception of micro and macro expressions of Virtual Humans (VHs) generated by Computer Graphics (CG), first described in 2014 and replicated in 2021. The 2014 study referred to a VH realistic, whereas, in 2021, it referred to a VH cartoon. In our work, we replicate the study by using a realistic CG character. Our main goals are to compare the perceptions of micro and macro expressions between levels of realism (2021 cartoon versus 2023 realistic) and between realistic characters in different periods (i.e., 2014 versus 2023). In one of our results, people more easily recognized micro expressions in realistic VHs than in a cartoon VH. In another result, we show that the participants' perception was similar for both micro and macro expressions in 2014 and 2023.

cs.GR↗

arXiv2022

Mitigating Bias in Facial Analysis Systems by Incorporating Label Diversity

Facial analysis models are increasingly applied in real-world applications that have significant impact on peoples' lives. However, as literature has shown, models that automatically classify facial attributes might exhibit algorithmic discrimination behavior with respect to protected groups, potentially posing negative impacts on individuals and society. It is therefore critical to develop techniques that can mitigate unintended biases in facial classifiers. Hence, in this work, we introduce a novel learning method that combines both subjective human-based labels and objective annotations based on mathematical definitions of facial traits. Specifically, we generate new objective annotations from two large-scale human-annotated dataset, each capturing a different perspective of the analyzed facial trait. We then propose an ensemble learning method, which combines individual models trained on different types of annotations. We provide an in-depth analysis of the annotation procedure as well as the datasets distribution. Moreover, we empirically demonstrate that, by incorporating label diversity, our method successfully mitigates unintended biases, while maintaining significant accuracy on the downstream tasks.

cs.CV↗

arXiv2019

A Software to Detect OCC Emotion, Big-Five Personality and Hofstede Cultural Dimensions of Pedestrians from Video Sequences

This paper presents a video analysis application to detect personality, emotion and cultural aspects from pedestrians in video sequences, along with a visualizer of features. The proposed model considers a series of characteristics of the pedestrians and the crowd, such as number and size of groups, distances, speeds, among others, and performs the mapping of these characteristics in personalities, emotions and cultural aspects, considering the Cultural Dimensions of Hofstede (HCD), the Big-Five Personality Model (OCEAN) and the OCC Emotional Model. The main hypothesis is that there is a relationship between so-called intrinsic human variables (such as emotion) and the way people behave in space and time. The software was tested in a set of videos from different countries and results seem promising in order to identify these three different levels of psychological traits in the filmed sequences. In addition, the data of the people present in the videos can be seen in a crowd viewer.

cs.CV↗

arXiv2019

How much do you perceive this? An analysis on perceptions of geometric features, personalities and emotions in virtual humans (Extended Version)

This work aims to evaluate people's perception regarding geometric features, personalities and emotions characteristics in virtual humans. For this, we use as a basis, a dataset containing the tracking files of pedestrians captured from spontaneous videos and visualized them as identical virtual humans. The goal is to focus on their behavior and not being distracted by other features. In addition to tracking files containing their positions, the dataset also contains pedestrian emotions and personalities detected using Computer Vision and Pattern Recognition techniques. We proceed with our analysis in order to answer the question if subjects can perceive geometric features as distances/speeds as well as emotions and personalities in video sequences when pedestrians are represented by virtual humans. Regarding the participants, an amount of 73 people volunteered for the experiment. The analysis was divided in two parts: i) evaluation on perception of geometric characteristics, such as density, angular variation, distances and speeds, and ii) evaluation on personality and emotion perceptions. Results indicate that, even without explaining to the participants the concepts of each personality or emotion and how they were calculated (considering geometric characteristics), in most of the cases, participants perceived the personality and emotion expressed by the virtual agents, in accordance with the available ground truth.

cs.GR↗