Senescence: An Aging based Character Simulation Framework

The \u27Senescence\u27 framework is a character simulation plug-in for Maya that can be used for rigging and skinning muscle deformer based humanoid characters with support for aging. The framework was developed using Python, Maya Embedded Language and PyQt. The main targeted users for this framework are the Character Technical Directors, Technical Artists, Riggers and Animators from the production pipeline of Visual Effects Studios. The characters that were simulated using \u27Senescence\u27 were studied using a survey to understand how well the intended age was perceived by the audience. The results of the survey could not reject one of our null hypotheses which means that the difference in the simulated age groups of the character is not perceived well by the participants. But, there is a difference in the perception of simulated age in the character between an Animator and a Non-Animator. Therefore, the difference in the simulated character\u27s age was perceived by an untrained audience, but the audience was unable to relate it to a specific age group

Perceptual Evaluation and Metric for Terrain Models

The use of Procedural Modeling for the creation of 3D models such as Buildings, Terrains, Trees etc., is becoming increasingly common in Films, Video Games, Urban Modeling and Architectural Visualization. This is due to the primary factor that using procedural models in comparison to traditional hand-modeled models helps in saving time, cost and aids in generation of a larger variety in comparison to a few. However, there are so many open problems in procedural modeling methods that does not rely on any user assistance or aid in generating models especially in terms of their visual quality and perception. Although, it is easy to identify realistic looking models from procedural models, the metrics that make them ’Real’ or ’Procedural’ is still in the indeterminable and remains uncanny in nature. The perceptual metrics (intrinsic factors such as surface features and details, extrinsic factors such as environmental attributes and visual cues) that contributes to the visual perception of Procedural models have not been studied in detail or quantified yet. This dissertation presents a first step in the direction of perceptual evaluation of procedural models of terrains. We gathered and categorized several types of real and synthetic terrains generated by methods used in computer graphics and conducted two large studies with 70 participants ranking them perceptually. The results show that synthetic terrains lack in visual quality and are perceived worse than real terrains with statistical significance. We performed a quantitative study by using localized geomorphology based landform features on terrains (geomorphons) that indicate that valleys, ridges, and hollows have significant perceptual importance. We then used generative deep generative neural network to transfer the features from real terrains to synthetic ones and vice versa to further confirm their importance. A second perceptual experiment with 128 participants confirmed the importance of the transferred features for visual perception. Based on these results, we introduce PTQM (Perceived Terrain Quality Metrics); a novel perceptual metrics based on geomorphons that assigns a number of estimated visual quality of a terrain represented as a digital elevation map. The introduced perceptual metric based on geomorphons indicate that features such as Valley (0.66), Ridge (0.64), Summit (0.44), Depression (0.42), Spur(0.33), and Hollow (0.22) in order have significant perceptual importance. By using linear regression, we show that the presented features are strongly correlated with perceived visual quality

PTRM: Perceived Terrain Realism Metric

International audienceTerrains are visually prominent and commonly needed objects in many computer graphics applications. While there are many algorithms for synthetic terrain generation, it is rather difficult to assess the realism of a generated output. This paper presents a first step towards the direction of perceptual evaluation for terrain models. We gathered and categorized several classes of real terrains, and we generated synthetic terrain models using computer graphics methods. The terrain geometries were rendered by using the same texturing, lighting, and camera position. Two studies on these image sets were conducted, ranking the terrains perceptually, and showing that the synthetic terrains are perceived as lacking realism compared to the real ones. We provide insight into the features that affect the perceived realism by a quantitative evaluation based on localized geomorphology-based landform features (geomorphons) that categorize terrain structures such as valleys, ridges, hollows, etc. We show that the presence or absence of certain features has a significant perceptual effect. The importance and presence of the terrain features were confirmed by using a generative deep neural network that transferred the features between the geometric models of the real terrains and the synthetic ones. The feature transfer was followed by another perceptual experiment that further showed their importance and effect on perceived realism. We then introduce Perceived Terrain Realism Metrics (PTRM) that estimates human perceived realism of a terrain represented as a digital elevation map by relating distribution of terrain features with their perceived realism. This metric can be used on a synthetic terrain, and it will output an estimated level of perceived realism. We validated the proposed metrics on real and synthetic data and compared them to the perceptual studies