Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression

We present techniques for improving performance driven facial animation, emotion recognition, and facial key-point or landmark prediction using learned identity invariant representations. Established approaches to these problems can work well if sufficient examples and labels for a particular identity are available and factors of variation are highly controlled. However, labeled examples of facial expressions, emotions and key-points for new individuals are difficult and costly to obtain. In this paper we improve the ability of techniques to generalize to new and unseen individuals by explicitly modeling previously seen variations related to identity and expression. We use a weakly-supervised approach in which identity labels are used to learn the different factors of variation linked to identity separately from factors related to expression. We show how probabilistic modeling of these sources of variation allows one to learn identity-invariant representations for expressions which can then be used to identity-normalize various procedures for facial expression analysis and animation control. We also show how to extend the widely used techniques of active appearance models and constrained local models through replacing the underlying point distribution models which are typically constructed using principal component analysis with identity-expression factorized representations. We present a wide variety of experiments in which we consistently improve performance on emotion recognition, markerless performance-driven facial animation and facial key-point tracking.Comment: to appear in Image and Vision Computing Journal (IMAVIS

Breathing Life into Faces: Speech-driven 3D Facial Animation with Natural Head Pose and Detailed Shape

The creation of lifelike speech-driven 3D facial animation requires a natural and precise synchronization between audio input and facial expressions. However, existing works still fail to render shapes with flexible head poses and natural facial details (e.g., wrinkles). This limitation is mainly due to two aspects: 1) Collecting training set with detailed 3D facial shapes is highly expensive. This scarcity of detailed shape annotations hinders the training of models with expressive facial animation. 2) Compared to mouth movement, the head pose is much less correlated to speech content. Consequently, concurrent modeling of both mouth movement and head pose yields the lack of facial movement controllability. To address these challenges, we introduce VividTalker, a new framework designed to facilitate speech-driven 3D facial animation characterized by flexible head pose and natural facial details. Specifically, we explicitly disentangle facial animation into head pose and mouth movement and encode them separately into discrete latent spaces. Then, these attributes are generated through an autoregressive process leveraging a window-based Transformer architecture. To augment the richness of 3D facial animation, we construct a new 3D dataset with detailed shapes and learn to synthesize facial details in line with speech content. Extensive quantitative and qualitative experiments demonstrate that VividTalker outperforms state-of-the-art methods, resulting in vivid and realistic speech-driven 3D facial animation

3d Computer Modeling Of Human Mandible Motion With Application To Human Facial Motion

Computer facial modeling and animation has been an interest of computer graphics researchers for many years. This is not only because the face itself is an interesting object, but also because facial animation finds application in many other disciplines (for example, entertainment, medical education, telecommunication, psychology, medicine, and linguistics). Because the mandible motion plays a major role in modeling facial motion, its study is of significance to each of those disciplines as well. In addition, the mandible itself is an object of study in the area of clinical science.;Current facial movement models in computer animation have difficulty dealing with facial movements that are strongly determined by the mandible, such as chewing. This thesis proposes new computer models of the mandible that address this problem. First, a geometric mandible model is proposed to simulate typical motion features of the mandible such as opening, closing, protruding, and lateral shifting. While this model is simple and successful, it has drawbacks when applied to motions as complicated as chewing. Therefore, a physically-based model is also proposed to deal with these drawbacks. This physically-based mandible model is then integrated with a spring-based physical facial model to automatically simulate motions such as chewing

Final Report to NSF of the Standards for Facial Animation Workshop

The human face is an important and complex communication channel. It is a very familiar and sensitive object of human perception. The facial animation field has increased greatly in the past few years as fast computer graphics workstations have made the modeling and real-time animation of hundreds of thousands of polygons affordable and almost commonplace. Many applications have been developed such as teleconferencing, surgery, information assistance systems, games, and entertainment. To solve these different problems, different approaches for both animation control and modeling have been developed

Face modeling and animation language for MPEG-4 XMT framework

This paper proposes FML, an XML-based face modeling and animation language. FML provides a structured content description method for multimedia presentations based on face animation. The language can be used as direct input to compatible players, or be compiled within MPEG-4 XMT framework to create MPEG-4 presentations. The language allows parallel and sequential action description, decision-making and dynamic event-based scenarios, model configuration, and behavioral template definition. Facial actions include talking, expressions, head movements, and low-level MPEG-4 FAPs. The ShowFace and iFACE animation frameworks are also reviewed as example FML-based animation systems

DualTalker: A Cross-Modal Dual Learning Approach for Speech-Driven 3D Facial Animation

In recent years, audio-driven 3D facial animation has gained significant attention, particularly in applications such as virtual reality, gaming, and video conferencing. However, accurately modeling the intricate and subtle dynamics of facial expressions remains a challenge. Most existing studies approach the facial animation task as a single regression problem, which often fail to capture the intrinsic inter-modal relationship between speech signals and 3D facial animation and overlook their inherent consistency. Moreover, due to the limited availability of 3D-audio-visual datasets, approaches learning with small-size samples have poor generalizability that decreases the performance. To address these issues, in this study, we propose a cross-modal dual-learning framework, termed DualTalker, aiming at improving data usage efficiency as well as relating cross-modal dependencies. The framework is trained jointly with the primary task (audio-driven facial animation) and its dual task (lip reading) and shares common audio/motion encoder components. Our joint training framework facilitates more efficient data usage by leveraging information from both tasks and explicitly capitalizing on the complementary relationship between facial motion and audio to improve performance. Furthermore, we introduce an auxiliary cross-modal consistency loss to mitigate the potential over-smoothing underlying the cross-modal complementary representations, enhancing the mapping of subtle facial expression dynamics. Through extensive experiments and a perceptual user study conducted on the VOCA and BIWI datasets, we demonstrate that our approach outperforms current state-of-the-art methods both qualitatively and quantitatively. We have made our code and video demonstrations available at https://github.com/sabrina-su/iadf.git

Fully Automatic Facial Deformation Transfer

Facial Animation is a serious and ongoing challenge for the Computer Graphic industry. Because diverse and complex emotions need to be expressed by different facial deformation and animation, copying facial deformations from existing character to another is widely needed in both industry and academia, to reduce time-consuming and repetitive manual work of modeling to create the 3D shape sequences for every new character. But transfer of realistic facial animations between two 3D models is limited and inconvenient for general use. Modern deformation transfer methods require correspondences mapping, in most cases, which are tedious to get. In this paper, we present a fast and automatic approach to transfer the deformations of the facial mesh models by obtaining the 3D point-wise correspondences in the automatic manner. The key idea is that we could estimate the correspondences with different facial meshes using the robust facial landmark detection method by projecting the 3D model to the 2D image. Experiments show that without any manual labelling efforts, our method detects reliable correspondences faster and simpler compared with the state-of-the-art automatic deformation transfer method on the facial models