{HiFECap}: {M}onocular High-Fidelity and Expressive Capture of Human Performances

Monocular 3D human performance capture is indispensable for many applicationsin computer graphics and vision for enabling immersive experiences. However,detailed capture of humans requires tracking of multiple aspects, including theskeletal pose, the dynamic surface, which includes clothing, hand gestures aswell as facial expressions. No existing monocular method allows joint trackingof all these components. To this end, we propose HiFECap, a new neural humanperformance capture approach, which simultaneously captures human pose,clothing, facial expression, and hands just from a single RGB video. Wedemonstrate that our proposed network architecture, the carefully designedtraining strategy, and the tight integration of parametric face and hand modelsto a template mesh enable the capture of all these individual aspects.Importantly, our method also captures high-frequency details, such as deformingwrinkles on the clothes, better than the previous works. Furthermore, we showthat HiFECap outperforms the state-of-the-art human performance captureapproaches qualitatively and quantitatively while for the first time capturingall aspects of the human.<br

Using data visualization to deduce faces expressions

Conferência Internacional, realizada na Turquia, de 6-8 de setembro de 2018.Collect and examine in real time multi modal sensor data of a human face, is an important problem in computer vision, with applications in medical and monitoring analysis, entertainment and security. Although its advances, there are still many open issues in terms of the identification of the facial expression. Different algorithms and approaches have been developed to find out patterns and characteristics that can help the automatic expression identification. One way to study data is through data visualizations. Data visualization turns numbers and letters into aesthetically pleasing visuals, making it easy to recognize patterns and find exceptions. In this article, we use information visualization as a tool to analyse data points and find out possible existing patterns in four different facial expressions.info:eu-repo/semantics/publishedVersio

Machine Analysis of Facial Expressions

No abstract

Recognising facial expressions in video sequences

We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89\% recognition rate in a set of 333 sequences from the Cohn-Kanade data base

Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior

Automatic facial expression tracking for 4D range scans

This paper presents a fully automatic approach of spatio-temporal facial expression tracking for 4D range scans without any manual interventions (such as specifying landmarks). The approach consists of three steps: rigid registration, facial model reconstruction, and facial expression tracking. A Scaling Iterative Closest Points (SICP) algorithm is introduced to compute the optimal rigid registration between a template facial model and a range scan with consideration of the scale problem. A deformable model, physically based on thin shells, is proposed to faithfully reconstruct the facial surface and texture from that range data. And then the reconstructed facial model is used to track facial expressions presented in a sequence of range scans by the deformable model

HeadOn: Real-time Reenactment of Human Portrait Videos

We propose HeadOn, the first real-time source-to-target reenactment approach for complete human portrait videos that enables transfer of torso and head motion, face expression, and eye gaze. Given a short RGB-D video of the target actor, we automatically construct a personalized geometry proxy that embeds a parametric head, eye, and kinematic torso model. A novel real-time reenactment algorithm employs this proxy to photo-realistically map the captured motion from the source actor to the target actor. On top of the coarse geometric proxy, we propose a video-based rendering technique that composites the modified target portrait video via view- and pose-dependent texturing, and creates photo-realistic imagery of the target actor under novel torso and head poses, facial expressions, and gaze directions. To this end, we propose a robust tracking of the face and torso of the source actor. We extensively evaluate our approach and show significant improvements in enabling much greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at Siggraph'1

Synthesis and Control of High Resolution Facial Expressions for Visual Interactions

The synthesis of facial expression with control of intensity and personal styles is important in intelligent and affective human-computer interaction, especially in face-to-face inter-action between human and intelligent agent. We present a facial expression animation system that facilitates control of expressiveness and style. We learn a decomposable genera-tive model for the nonlinear deformation of facial expressions by analyzing the mapping space between low dimensional embedded representation and high resolution tracking data. Bilinear analysis of the mapping space provides a compact representation of the nonlinear generative model for facial expressions. The decomposition allows synthesis of new fa-cial expressions by control of geometry and expression style. The generative model provides control of expressiveness pre-serving nonlinear deformation in the expressions with simple parameters and allows synthesis of stylized facial geometry. In addition, we can directly extract the MPEG-4 Facial Ani-mation Parameters (FAPs) from the synthesized data, which allows using any animation engine that supports FAPs to ani-mate new synthesized expressions. 1