3D Face Synthesis Driven by Personality Impression

Synthesizing 3D faces that give certain personality impressions is commonly needed in computer games, animations, and virtual world applications for producing realistic virtual characters. In this paper, we propose a novel approach to synthesize 3D faces based on personality impression for creating virtual characters. Our approach consists of two major steps. In the first step, we train classifiers using deep convolutional neural networks on a dataset of images with personality impression annotations, which are capable of predicting the personality impression of a face. In the second step, given a 3D face and a desired personality impression type as user inputs, our approach optimizes the facial details against the trained classifiers, so as to synthesize a face which gives the desired personality impression. We demonstrate our approach for synthesizing 3D faces giving desired personality impressions on a variety of 3D face models. Perceptual studies show that the perceived personality impressions of the synthesized faces agree with the target personality impressions specified for synthesizing the faces. Please refer to the supplementary materials for all results.Comment: 8pages;6 figure

Association between Self-Reported Prior Nights’ Sleep and Single-Task Gait in Healthy Young Adults: An Exploratory Study Using Machine Learning

Failure to obtain 7-9 hours of sleep has been associated with decreased gait speed in young adults. While Machine Learning (ML) has been used to identify sleep quality in young adults, there are no current studies that have used ML to identify prior night’s sleep in a sample of young adults. PURPOSE: To use ML to identify prior night’s sleep in healthy young adults using single-task walking gait. METHODS: Participants (n=126, age 24.3±4.0yrs; 65% female) completed a survey on their prior night’s sleep and performed a 2-minute walk around a 6m track. Gait data were collected using inertial sensors. Participants were split into 2 groups (\u3c7hs or \u3e9hs: poor sleepers; 7-9hs: good sleepers) and gait characteristics were used to classify participants into each group using ML models via a 10-fold cross validation. A post-hoc ANCOVA was used to assess gait differences. RESULTS: Using Random Forest Classifiers (RFC), top 9 features were extracted. Classification results suggest a 0.79 correlation between gait parameters and prior night’s sleep. The RFC models had a 65.03% mean classification accuracy rate. Top 0.3% of the models had 100% classification accuracy rate. The top 9 features were primarily characteristics that measured variance between lower limb movements. Post-hoc analyses suggest significantly greater variances between lower limb characteristics. CONCLUSION: Good sleepers had more asymmetrical gait patterns (faster gait speed, less trunk motion). Poor sleepers had trouble maintaining gait speed (increased variance in cadence, larger stride lengths, and less time spent in single leg support time). Although the mechanisms of these gait changes are unknown, these findings provide evidence that gait is different for individuals who not receive 7-9 hours of sleep the night before. As evidenced by the high correlation co-efficient of our classification models, gait may be a good way of identifying prior night’s sleep

Data-Driven Optimization for Modeling in Computer Graphics and Vision

In view of the immense and rapidly increasing quantity of user-created 3D content and real-world scene data publicly available on the internet, as well as the widespread popularity of data acquisition devices such as low-cost depth cameras, it has become convenient to acquire or access data that can potentially be utilized for modeling. In this thesis, we explore how data-driven optimization can be adapted to the essential task of modeling, both from the computer graphics and computer vision perspectives.We first discuss the conceptual innovations inherent to model synthesis through data-driven optimization, along with the advantages of and considerations in its application. We then tackle various challenging modeling problems within our novel framework. In the context of computer graphics, we devise data-driven optimization methods for virtual world modeling, virtual character modeling, and interactive scene modeling. In the context of computer vision, we devise data-driven optimization methods for 3D surface reconstruction from images

3D reconstruction and synthesis of facial expressions using a manifold alignment framework

The capture, reconstruction and synthesis of facial expressions often involves specialized hardware support and considerable computation time. This prohibits its widespread deployment and use in real-time applications. In this paper, we aim at tackling this limitation via a learning-based approach, which is efficient and requires only modest hardware support. Our approach is based on a semi-supervised manifold alignment framework, where feature points extracted from 2D face images are aligned with data expressed as morph-target values for a 3D face model. By applying a kernel embedding method known as kernel locality preserving projections (KLPP) and a method for solving the pre-image problem in kernel methods, our framework is capable of handling nonlinearity and is defined everywhere. Experiments are conducted to demonstrate two possible applications of our proposed framework: 3D reconstruction of facial expressions and dynamic synthesis of facial expression sequences

Outdoor Photometric Stereo

We introduce a framework for outdoor photometric stereo utilizing natural environmental illumination. Our framework extends beyond existing photometric stereo methods intended for laboratory environments to encompass robust outdoor operation in the real world. In this paper, we motivate our framework, describe the components of its processing pipeline, and assess its performance in synthetic experiments as well as in natural experiments including objects in outdoor environments with complex realworld illuminations. 1