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

DF-3DFace: One-to-Many Speech Synchronized 3D Face Animation with Diffusion

Speech-driven 3D facial animation has gained significant attention for its ability to create realistic and expressive facial animations in 3D space based on speech. Learning-based methods have shown promising progress in achieving accurate facial motion synchronized with speech. However, one-to-many nature of speech-to-3D facial synthesis has not been fully explored: while the lip accurately synchronizes with the speech content, other facial attributes beyond speech-related motions are variable with respect to the speech. To account for the potential variance in the facial attributes within a single speech, we propose DF-3DFace, a diffusion-driven speech-to-3D face mesh synthesis. DF-3DFace captures the complex one-to-many relationships between speech and 3D face based on diffusion. It concurrently achieves aligned lip motion by exploiting audio-mesh synchronization and masked conditioning. Furthermore, the proposed method jointly models identity and pose in addition to facial motions so that it can generate 3D face animation without requiring a reference identity mesh and produce natural head poses. We contribute a new large-scale 3D facial mesh dataset, 3D-HDTF to enable the synthesis of variations in identities, poses, and facial motions of 3D face mesh. Extensive experiments demonstrate that our method successfully generates highly variable facial shapes and motions from speech and simultaneously achieves more realistic facial animation than the state-of-the-art methods

Capture, Learning, and Synthesis of 3D Speaking Styles

Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers. We then train a neural network on our dataset that factors identity from facial motion. The learned model, VOCA (Voice Operated Character Animation) takes any speech signal as input - even speech in languages other than English - and realistically animates a wide range of adult faces. Conditioning on subject labels during training allows the model to learn a variety of realistic speaking styles. VOCA also provides animator controls to alter speaking style, identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball rotations) during animation. To our knowledge, VOCA is the only realistic 3D facial animation model that is readily applicable to unseen subjects without retargeting. This makes VOCA suitable for tasks like in-game video, virtual reality avatars, or any scenario in which the speaker, speech, or language is not known in advance. We make the dataset and model available for research purposes at http://voca.is.tue.mpg.de.Comment: To appear in CVPR 201

Personalized Speech-driven Expressive 3D Facial Animation Synthesis with Style Control

Different people have different facial expressions while speaking emotionally. A realistic facial animation system should consider such identity-specific speaking styles and facial idiosyncrasies to achieve high-degree of naturalness and plausibility. Existing approaches to personalized speech-driven 3D facial animation either use one-hot identity labels or rely-on person specific models which limit their scalability. We present a personalized speech-driven expressive 3D facial animation synthesis framework that models identity specific facial motion as latent representations (called as styles), and synthesizes novel animations given a speech input with the target style for various emotion categories. Our framework is trained in an end-to-end fashion and has a non-autoregressive encoder-decoder architecture with three main components: expression encoder, speech encoder and expression decoder. Since, expressive facial motion includes both identity-specific style and speech-related content information; expression encoder first disentangles facial motion sequences into style and content representations, respectively. Then, both of the speech encoder and the expression decoders input the extracted style information to update transformer layer weights during training phase. Our speech encoder also extracts speech phoneme label and duration information to achieve better synchrony within the non-autoregressive synthesis mechanism more effectively. Through detailed experiments, we demonstrate that our approach produces temporally coherent facial expressions from input speech while preserving the speaking styles of the target identities.Comment: 8 page

Interactive speech-driven facial animation

One of the fastest developing areas in the entertainment industry is digital animation. Television programmes and movies frequently use 3D animations to enhance or replace actors and scenery. With the increase in computing power, research is also being done to apply these animations in an interactive manner. Two of the biggest obstacles to the success of these undertakings are control (manipulating the models) and realism. This text describes many of the ways to improve control and realism aspects, in such a way that interactive animation becomes possible. Specifically, lip-synchronisation (driven by human speech), and various modeling and rendering techniques are discussed. A prototype that shows that interactive animation is feasible, is also described.Mr. A. Hardy Prof. S. von Solm

Facial Capture Lip-Sync

Facial model lip-sync is a large field of research within the animation industry. The mouth is a complex facial feature to animate, thus multiple techniques have arisen to simplify this process. These techniques, however, can lead to unappealing flat animation that lack full facial expression or eerie over-expressive animations that make the viewer uneasy. This thesis proposes an animation system that produces natural speech movements while conveying facial expression and compares them to previous techniques. This system used a text input of the dialogue to generate a phoneme-to-blend shape map to automate the facial model. An actor was motion captured to record the audio, provide speech motion data, and to directly control the facial expression in the regions of the face other than the mouth. The actor\u27s speech motion and the phoneme-to-blend shape map worked in conjunction to create a final lip-synced animation that viewers compared to phonetic driven animation and animation created with just motion capture. In this comparison, this system\u27s resultant animation was the least favorite, while the dampened motion capture animation gained the most preference

FaceDiffuser: Speech-Driven 3D Facial Animation Synthesis Using Diffusion

Speech-driven 3D facial animation synthesis has been a challenging task both in industry and research. Recent methods mostly focus on deterministic deep learning methods meaning that given a speech input, the output is always the same. However, in reality, the non-verbal facial cues that reside throughout the face are non-deterministic in nature. In addition, majority of the approaches focus on 3D vertex based datasets and methods that are compatible with existing facial animation pipelines with rigged characters is scarce. To eliminate these issues, we present FaceDiffuser, a non-deterministic deep learning model to generate speech-driven facial animations that is trained with both 3D vertex and blendshape based datasets. Our method is based on the diffusion technique and uses the pre-trained large speech representation model HuBERT to encode the audio input. To the best of our knowledge, we are the first to employ the diffusion method for the task of speech-driven 3D facial animation synthesis. We have run extensive objective and subjective analyses and show that our approach achieves better or comparable results in comparison to the state-of-the-art methods. We also introduce a new in-house dataset that is based on a blendshape based rigged character. We recommend watching the accompanying supplementary video. The code and the dataset will be publicly available.Comment: Pre-print of the paper accepted at ACM SIGGRAPH MIG 202