Neuromuscular Control of the Face-Head-Neck
Biomechanical Complex With Learning-Based
Expression Transfer From Images and Videos
Xiao S. Zeng1? , Surya Dwarakanath1? , Wuyue Lu1 ,Masaki Nakada2 , and Demetri Terzopoulos1
1

Computer Science Department, University of California, Los Angeles, CA, USA
2
NeuralX, Inc., Los Angeles, CA, USA

Abstract. The transfer of facial expressions from people to 3D facemodels is a classic computer graphics problem. In this paper, we present a
novel, learning-based approach to transferring facial expressions and head
movements from images and videos to a biomechanical model of the facehead-neck complex. Leveraging the Facial Action Coding System (FACS)
as an intermediate representation of the expression space, we train a deepneural network to take in FACS Action Units (AUs) and output suitablefacial muscle and jaw activation signals for the musculoskeletal model.
Through biomechanical simulation, the activations deform the facial soft
tissues, thereby transferring the expression to the model. Our approach
has advantages over previous approaches. First, the facial expressions areanatomically consistent as our biomechanical model emulates the relevant
anatomy of the face, head, and neck. Second, by training the neuralnetwork using data generated from the biomechanical model itself, we
eliminate the manual effort of data collection for expression transfer. The
success of our approach is demonstrated through experiments involving
the transfer onto our face-head-neck model of facial expressions and headposes from a range of facial images and videos.
Keywords: Animation · Physical Simulation · Computer Vision · Deep
Learning.

1

Introduction

The face, actuated by the muscles of facial expression, and head movements,
actuated by the cervical muscles, are a powerful mode of nonverbal communication
between humans. The simulation of the face-head-neck musculoskeletal complex isof importance in understanding how we convey thinking and feeling in fields from
affective science to 3D computer animation. Biomechanical human musculoskeletal
models realistically capture the anatomy and physics underlying human motion
generation. In particular, those pertaining to the simulation of the human facehave succeeded in convincingly emulating facial expressiveness; however, theyrequire significant effort in parameter tuning to produce realistic results.
?

Co-first author.

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(a) Subject 1(b) Subject 2(c) Subject 3

Fig. 1: Our deep learning framework enables the transfer of facial expressions
and head poses from images or videos to a biomechanical face-head-neck model.

In this paper, we show how to endow a biomechanical, musculoskeletal modelof the human face with the ability to produce facial expressions via machinelearning from real-world reference images and videos (Fig. 1). To this end, we
introduce a deep neural-network-based method for learning the representation ofhuman facial expressions through Ekman’s Facial Action Coding System (FACS)[5] in the context of the muscle actuators that drive the musculoskeletal facemodel augmented with a musculoskeletal cervicocephalic (neck-head) system toanimate head movement during facial expression synthesis.
The novelty of our framework lies in the following features: We propose thefirst biomechanical face-head-neck animation system that is capable of learning
to reproduce expressions and head orientations through neuromuscular control.
Our novel deep neuromuscular motor controller learns to map between FACS
Action Units (AUs) extracted from human facial images and videos and theactivations of the muscle actuators that drive the biomechanical system. As aproof of concept, we demonstrate an automated processing pipeline (Fig. 2) for
animating expressions and head poses using an improved version of the physicsbased neck-head-face animation system developed by Lee and Terzopoulos [10],but which can potentially be applied to any muscle-driven model.

2

Related Work

Muscle-based facial animation has been studied for decades [19]. A series of studies
have endeavored to build increasingly anatomically accurate musculoskeletal facemodels. Using such models, Terzopoulos and Waters [17] and Sifakis et al. [15],among others, have addressed the automatic determination of facial muscle
activations from videos, but these methods require nonrigid motion trackers
or motion capture markers. We present a markerless, real-time biomechanicalface-head-neck simulation system that can automatically learn muscle activation
parameters for a variety of expressions from reference images and videos. Most
relevant to our work in the present paper is an existing biomechanical model ofthe cervicocephalic complex [10], which incorporates an improved version of theface model developed by Lee et al. [11].

Learning-Based Expression Transfer from Images and Videos(a) Framework

3(b) Controller

Fig. 2: The structure of our expression and head pose transfer framework (a)and the components of the facial expression and head pose controller (b). The
expression learning neural network (yellow) is first trained offline. In a transfer
task, an image or a video sequence of a real face is fed into the online transfer module of the expression and pose controller to output the desired facialmuscle activations and head orientation information. The muscle activations are
input to the biomechanical face model (orange) to perform the corresponding
expression and the head orientation is provided to the head-neck biomechanicalneuromuscular system (green) to produce the desired head pose.

Many facial animation researchers have made use of the well-known FACS [8],a quantitative phenomenological abstraction that decomposes facial expressions
into the intensity levels of Action Units (AUs), each of which corresponds to theactions of one or more facial muscles. An advantage of using the FACS is that it
encodes anatomical and psychological semantics [5].
Expression transfer or retargeting is a trending topic and recent approaches often use FACS-based blendshapes as the basic parametric representation [20,18,24].
Others have used techniques such as interactive mesh deformation control [22]and neural-network-based perceptual models [1] to represent expressions in blendshapes. However, transferring expressions using a musculoskeletal system is morenatural since facial actions are the result of coordinated muscle contractions
inducing soft tissue deformation. Despite the existing literature, there remains a
deficiency of work in transferring expressions to musculoskeletal facial systems.

3

Musculoskeletal Model

Our real-time musculoskeletal model is based on the one of Lee and Terzopoulos [10], but both the underlying face-head-neck control system and the facial
expression system are significantly improved. Its overall architecture is controlled in a hierarchical manner, as illustrated in Fig. 2a. Specifically, the skeletal
structure is an articulated multibody dynamics system, with bones and joints
consistent with human anatomy. The skeletal system is driven by a Hill-type
muscle actuator model.

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The biomechanical face component consists of a facial soft tissue modelcomprising epidermis, dermal-fatty, fascia, and muscle layers supported by anunderlying skull, which is constructed based on the work of Lee et al. [11]. The
soft tissue is a deformable model assembled from discrete uniaxial finite elements,
which simulates dynamic facial deformation in an anatomically consistent yet
simplified way compared to the models described in [15], [21], and [6], thus maintaining a low computational cost that affords real-time simulation performanceon readily available hardware.
There are 26 pairs of primary facial muscles embedded in the biomechanicalface, including the frontalis, corrugator, levator labii, orbicularis oculi, mentalis,and orbicularis oris muscle groups. The contractions of these muscles apply forces
to the facial soft tissue layers, inducing deformations that produce meaningfulfacial expressions. We have augmented the expressive details, such as wrinkles on
the face model, by applying multiple levels of subdivision to increase significantly
the number of surface nodes that can be activated by muscle forces, and applieda high resolution texture map to the surface mesh for a natural appearance.
The appendix of reference [23] explains the biomechanical components of ourmodel in greater detail.
3.1

Control

To control the face-head-neck system, our novel neural network-based expression
and pose controller generates facial muscle activations that produce recognizable
expressions. It concurrently outputs head pose estimates to the head-neck musculoskeletal complex, where voluntary and reflex neuromuscular control layers
generate muscle activation signals to achieve the desired head orientations.
The higher-level voluntary controller receives the current head-neck postureand velocity information, as well as the desired adjustment of the posture, andgenerates a feedforward muscle activation signal and a setpoint control signal.
The latter, which encodes the desired muscle strains and strain rates, is input
to the reflex controller that then generates a feedback muscle activation signal
and adds it to the feedforward signal generated by the voluntary controller.
As a result, each cervical muscle receives an activation signal a and, throughsimulation, generates a contractile muscle force accordingly. Together with theexternal environmental forces and gravity, the whole system is simulated throughtime and rendered as a physics-based animation. The voluntary controller runs
at 25Hz (in simulation time), while the reflex controller runs at 4KHz along withthe physical simulation time steps.

4

Expression Learning

We next explain our machine learning approach of using a deep neural network
to transfer facial expressions to our biomechanical face model. We leverage theFACS and synthesize the muscle activations for the model using the trained deepneural network. The following sections describe the architecture of the network,Learning-Based Expression Transfer from Images and Videos(a) Neural network architecture5(b) Convergence of the neural network

Fig. 3: The expression learning neural network.

the pipeline for using the biomechanical face model to generate training data,
and the process of training the neural network.
4.1

Network Architecture

The function of the neural network is to generate activation signals for the musclesof the biomechanical face model such that they generate contractile forces that
deform the synthetic facial tissues to produce facial expressions. We employ afully connected deep neural network architecture for this purpose (Fig. 3a). The
input layer consists of a total of 17 neurons representing the important AUs thatare involved in the majority of facial movements, each neuron corresponding toa single normalized AU. We include 4 hidden layers with 100 neurons in eachlayer and ReLU activation.3 The output layer consists of 56 neurons, 52 of which
encode the activations ai , with 1 ≤ i ≤ 52, for each of the 26 pairs of facialmuscles, and the remaining 4 encode the jaw rotation, jaw slide, jaw twist, and an
auxiliary value. Given its architecture, the network has a total of 37,300 weights.
It is implemented in Keras with a TensorFlow backend.
4.2

Training Data Generation

The training data generation process is divided into two steps:
1. generation of muscle activations, and2. generation of AUs for the corresponding muscle activations.
Each basic expression requires a combination of muscles to be activated.
Given n muscles, we define We as a set of weights wi , with 1 ≤ i ≤ n, which
determine the effect each muscle will have on an expression e. The activation
3

This choice was based on observed Mean Squared Errors (MSE) on test data of
networks with different numbers of hidden layers and different numbers of neurons
per hidden layer.

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for each muscle ai for an expression is then defined as ai = wi s, where wi ∈ We
and s is a scale term. For a single expression, we determine the weights in aset We manually by visually analyzing the facial expressions formed by the facemodel. We repeat the process for all the basic expressions, namely (1) Joy, (2)Sadness, (3) Anger, (4) Fear, (5) Disgust, and (6) Surprise to generate the sets
WJoy , WSadness , WAnger , WFear , WDisgust and WSurprise , respectively.
We then sample the value of s randomly in the range [0.0, 1.0] to generate all
the muscle activations ai . We also assign a random value between 0 and 1 for thejaw rotation. For the purpose of our experiments, we maintain the jaw twist andjaw slide at a value of 0.5. We further form a set A consisting of all the muscle
activations ai , where 1 ≤ i ≤ n, and the jaw activations including jaw rotation,
jaw twist and jaw slide. We iterate the above process, generating the set A byrepeatedly sampling the value of s for a single expression. We finally extend thisto all basic expressions and obtain multiple sets AJoy , ASadness , AAnger , AFear ,ADisgust and ASurprise for each expression.
We leverage the functionality of OpenFace 2.0 [2] for facial expression recognition and head pose estimation. OpenFace is a computer vision tool capableof facial landmark detection, head pose estimation, facial AU recognition, andeye-gaze estimation. OpenFace employs Convolutional Experts Constrained Local
Models (CE-CLMs) for facial landmark detection and tracking. CE-CLMs use a
3D representation of the detected facial landmarks by which OpenFace estimates
the head pose. Eye gaze estimation is done using a Constrained Local Neural
Fields (CLNF) landmark detector. The final task, facial expression recognition,is performed using Support Vector Machines and Support Vector Regression.
We use a single set A formed by the above described procedure to activate themuscles and jaw of the biomechanical face model. We then render the model toform an image. The image is input to OpenFace, which performs facial expression
recognition and head pose estimation, outputting the estimated AUs and headorientation associated with the input image. We repeat this process for each set
A formed (as described previously) to obtain the corresponding AUs and headorientations.
Thus, we synthesize a large quantity of training data pairs each consisting of (i)muscle and jaw activations A and (ii) the associated AUs and head orientations.

4.3

Network Training

We use the aforementioned data to train our deep neural network to input AUs
and output corresponding muscle and jaw activations.
The AUs from each training pair, generated as described in the previous
section, are passed to the network as input. We then compare the corresponding
muscle and jaw activations; i.e., the ground truth compared to the predictions ofmuscle and jaw activations given by the neural network. We use a Mean Square
Error (MSE) training loss between the predictions and the ground truth, which
is backpropagated to update the weights, thus training the neural network.

Learning-Based Expression Transfer from Images and Videos

7

We normalize the intensity values of each AU class across all pairs to remove
the disparity of intensity values between synthetic faces and real faces. We use a
total of 6,000 pairs, with about 1,000 pairs for each basic expression.
To train the neural network, we use the ADAM stochastic gradient descentoptimizer with an MSE loss, a batch size of 32, and a learning rate of 0.01. We
train the network in a total of 100 epochs, running on an NVIDIA GeForce GTX
1650 GPU installed on a Windows 10 machine with a 2.6GHz Intel Core i7-9750H
CPU. Fig. 3b shows the convergence of the training error.
4.4

Expression Transfer Pipeline

To transfer real facial expressions on the fly, we use a pipeline similar to the offline
training module, again leveraging OpenFace for facial expression recognition andhead pose estimation. We input an image of an expressive face into OpenFace toobtain all of the corresponding AUs and head orientations. The AUs are thenpassed into the trained neural network which outputs predictions of the muscle
and jaw activations, driving the biomechanical face to deform the muscles andtransfer the expressions onto it.
We transfer both image and video inputs. Each frame in a video is processedindependently and a resulting video is created using the transferred frames. The
intensity values for each AU class are normalized across all the images or frames
as in the case of the training pipeline.

5

Experiments and Results

We next present the results of transferring facial expressions from the wildusing our trained neural network. We evaluate our expression transfer pipeline
on different expressions while using a variation of AUs and muscles in thebiomechanical face model.
5.1

Facial Expression Datasets

Several facial expression datasets are available online. The datasets that we usedin our experiments are as follows:
Karolinska Directed Emotional Faces (KDEF) [3]. The KDEF dataset consistsof 4,900 pictures of human facial expressions. It covers 70 subjects (35 female and35 male) enacting all the basic facial expressions, namely Neutral, Joy, Sadness,
Anger, Fear, Disgust, and Surprise. Each expression performed by the subject isimaged from multiple directions. We use the dataset to transfer facial expressions
onto the biomechanical face model and visually analyze the performance of ourtrained neural network in this paper.
Cohn Kanade Dataset (CK) and Extended Cohn Kanade Dataset (CK+)[9,13]. The CK and the CK+ dataset combined consist of 593 video sequences of
123 subjects. Each sequence consists of images from a neutral expression (first
frame) to a peak expression (last frame). The peak expressions are FACS coded

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X. Zeng et al.
MSEJoy MSESadness MSEFear MSEAnger MSESurprise MSEDisgust MSEAll
0.000591 0.002925 0.001611 0.009689

0.000266

0.002516 0.000729

Table 1: Comparison of the Mean Squared Errors for training individual neuralnetworks for each expression and for training a single neural network for all.

for AUs. We use the sequences in the CK+ dataset to transfer videos of expression
transitions onto the biomechanical face.
Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)[12]. The original RAVDESS dataset consists of 24 actors vocalizing two lexicallymatched statements. An extension of the dataset, named RAVDESS Facial
Landmark Tracking, contains tracked facial landmark movements from the originalRAVDESS datasets [16]. Each actor performs 60 speech trials and about 44 song
trials. This yields a total of 2,452 video files for the complete dataset. We
leverage this dataset to test the transfer of actor faces in speech videos onto thebiomechanical face.
5.2

Action Units and Muscle Activations

There exist a total of 30 Action Units (AUs) corresponding to facial expressions.
OpenFace provides occurrence predictions for 18 out the 30 AUs and measuresintensity values for 17 out of the 30 AUs. We consider the 17 AUs for which theintensity values are present as the super-set of the AUs for our use case.
Due to the correlation between the AUs and muscles in the face, there also
exists a correlation between a basic facial expression and the AUs activatedby it. Our initial experiments focused on training the neural network for each
expression in an isolated manner. The neural network was trained to output
muscle activation for muscles corresponding to a single expression using AUs
which pertained to the same expression. In further trials, we observed that usageof all 17 AUs and all facial muscles improved the performance and the scalabilityof the expression transfer pipeline.
Table. 1 provides a comparison between training a single neural network for
all expressions and training individual neural networks for each expression, where
each network is trained only from the AUs and muscles relevant to its expression(using the mappings of expressions to AUs and AUs to muscles presented in[7], [4] and [14]). We observe better performance when training a single neuralnetwork for all the expressions, suggesting that AUs not directly relevant to an
expression also play a role in expression transfer.
Due to limitations in the biomechanical model, the range of each AU classdiffers from that of the real faces. Hence, we use normalization to overcome thebias and better transfer real facial expressions onto the biomechanical model.
The expression intensities in transferred expressions with normalization better
represent the real faces than those without normalization.

Learning-Based Expression Transfer from Images and Videos
Dataset9

MSEAU1 MSEAU4 MSEAU6 MSEAU9 MSEAU12 MSEAU15 MSEAU20 MSEAU25 Average

KDEF (unnormalized)
KDEF (normalized)
RAVDESS (video)
CK (video)

0.191
0.118
0.012
0.038

0.088
0.064
0.222
0.054

0.186
0.186
0.107
0.059

0.133
0.068
0.044
0.015

0.011
0.028
0.083
0.008

0.155
0.109
0.016
0.023

0.129
0.146
0.077
0.057

0.092
0.052
0.036
0.034

0.157
0.129
0.086
0.048

Table 2: Average MSE of selected AUs in the original data and transfer results
using different settings. Note that we randomly select 8 out of 17 AUs output by
OpenFace. The last column is the average MSE over all the AUs in each setting.

(a) Subject 1(b) Subject 3

Fig. 4: Transfer of head orientation along with facial expression.

We choose to activate only jaw rotation so as to maintain the symmetry of theexpression for our use case. We observe, that without jaw activations, expressions
such as surprise are not well synthesized by the biomechanical face model.
5.3

Head Orientation

Leveraging OpenFace for head pose estimation, we pass the estimated orientationof the head into the trained neck controller to activate the neck muscles. This inturn actuates the neck to adjust the head orientation in accordance with the input
image. Fig. 4 presents sample transferred results including head orientations fromthe KDEF dataset.
5.4

Facial Expression Transfer

The results for the facial expression transfer for each of the expressions is shownin Fig. 5. We present transfer results of a small sample of the KDEF dataset,selecting two subjects (a female and a male) enacting all the basic expressions.
We also evaluate the transfer results by comparing the MSE of selected AUs
in Table 2. We calculate the average MSE by AUs over all transferred expressions
and their corresponding reference data, then compute the mean value of theaverage MSEs over all AUs in each experimental setting. We report such mean

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(a) Subject 4(b) Subject 5

Fig. 5: Transfer of facial expressions (fear, anger, disgust, joy, sadness, surprise)and head poses from KDEF images to the biomechanical face-head-neck model.

values together with 8 randomly chosen AUs. The normalization step decreases
the MSEs of most selected AUs and yields results with higher AU similarity. The
more subtle expressions performed by subjects in the videos result in the lower
average MSEs compared with those of the image dataset.

6

Conclusion and Future WorkOur expression transfer approach is uniquely advantageous. First, it is anatomically consistent as the biomechanical model emulates the human cervicocephalicmusculoskeletal system. Further, our approach is based on the Facial Action
Coding System, which is a widely adopted representation of facial expressions,Learning-Based Expression Transfer from Images and Videos

11

including in the computer animation field. Finally, our approach is based on deeplearning, which can capture the complex, non-linear relation between the Action
Units of the FACS and associated facial muscle activations.
Additionally, our neural-network-based approach does not require any manual
data collection as the training data is generated directly using the biomechanicalmodel itself. This only needs to take place once, in advance. Once trained offline
using the synthesized training data, the neural network can quickly transfer alarge number of facial expressions. Unlike popular face tracking software suchas Faceware, which requires manual calibration steps to transfer expressionsof different subjects, our proposed approach needs no additional parameter
adjustments to perform the same task.
Although we have achieved satisfactory transfer results for all the basic facial
expressions, note that the simulation and transfer of complex mouth and eye
movements lies outside the scope of our current work. To improve the fidelity of
the results, we plan to extend our musculoskeletal model (e.g., by adding more
muscles to help activate facial AUs in a more anatomically accurate manner) and
its associated control system so as to explicitly control the lips and eyes, as wellas to transfer mixtures of expressions, subtle expressions, and microexpressions,which the human face is capable of producing.

Acknowledgements
We thank CyberAgent, Inc., for providing the high resolution texture for thefacial mesh that we used in our model. We also thank the curators of RAVDESS,CK/CK+, and KDEF for granting us permission to use their datasets.

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