FNeVR: Neural Volume Rendering for Face Animation
Anonymous Author(s)
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Abstract
Face animation, one of the hottest topics in computer vision, has achieved a promising performance with the help of generative models.
However, it remains a criticalchallenge to generate identity preserving and photo-realistic images due to thesophisticated motion deformation and complex facial detail modeling.
To addressthese problems, we propose a Face Neural Volume Rendering (FNeVR) networkto fully explore the potential of 2D motion warping and 3D volume rendering ina unified framework.
In FNeVR, we design a 3D Face Volume Rendering (FVR)module to enhance the facial details for image rendering.
Specifically, we firstextract 3D information with a well-designed architecture, and then introduce anorthogonal adaptive ray-sampling module for efficient rendering.
We also designa lightweight pose editor, enabling FNeVR to edit the facial pose in a simple yeteffective way.
Extensive experiments show that our FNeVR obtains the best overallquality and performance on widely used talking-head benchmarks.
Introduction
Aiming at animating a still head, face animation has far-reaching applications, such as photography,social media, movie production and virtual assistance, and has become a popular research topic incomputer vision and computer graphics [10, 44].
It synthesizes a realistic talking-head video bycombining the appearance and identity from one source face image with poses and motions extractedfrom a given driving video, possibly of a different person’s identity from the source.
With theprogress of generative models, especially Generative Adversarial Networks (GANs) and Variational
Auto-Encoders (VAEs), recent methods have achieved a promising performance in face animation[57, 46, 51, 12].
However, it is still a challenging task because of the difficulty of guaranteeing themotion transform validity and generating authentic images simultaneously.
Face animation methods can be mainly divided into three categories: model-free, landmark-based,and 3D structure-based.
Model-free methods [55, 45, 46, 52, 7] realize compelling reenactmentby modeling the relative motion field from the source to the driving faces.
Notably, First Order
Motion Model (FOMM) [46] significantly improves the performance of image animation by usingself-supervised keypoints with 2D motion warping.
Our studies show that 2D warping is effective togenerate high-quality motion transfer.
However, it has limited ability to produce sufficiently realisticimages (see Fig.
1(c)).
Landmark-based methods [2, 15, 56, 49, 57] introduce 2D facial landmarks andobtain considerable progress in face animation, but they are intrinsically disadvantageous in identitypreservation.
Motivated by the advanced 3D Morphable Models (3DMMs) [3, 40, 16, 4, 5, 6, 31],researchers tend to integrate the 3D geometric prior to condition synthesis and achieve impressiveperformance to generate realistic images [30, 12].
Nonetheless, these 3D structure-based methodsoften focus on introducing 3D facial information in the motion model to improve the authenticity ofthe results, such as Face vid2vid [51], neglecting the essential advantages of 2D warping in motiontransfer. As shown in Fig. 1(d), Face vid2vid produces a clear head profile but with poor facialdetails.
[Figure]
In this paper, we propose a Face Neural Volume Rendering (FNeVR) network for more realisticface animation, by taking the merits of 2D motion warping [46] on facial expression transformationand 3D volume rendering on high-quality image synthesis in a unified framework.
Inspired bythe remarkable properties of neural rendering [33, 22] in establishing high fidelity representations,we design a novel 3D Face Volume Rendering (FVR) module for face animation, which enablesour FNeVR to learn more realistic facial details efficiently and thus achieve photo-realistic results.
Specifically, the proposed FVR module first adopts two convolutional neural networks (CNNs), whichwe design to extract the 3D color and shape information from the 2D warped feature with the aidof the 3D reconstruction results.
We only apply the 3D reconstruction module to supervise the 3D
CNNs in the training process without any overhead for inference, which is different from existing 3Dstructure-based methods [30, 12, 51].
After that, we introduce an orthogonal adaptive ray-samplingmodule to efficiently calculate the sampled color field and the voxel probability for subsequent imagerendering.
Instead of using hierarchical sampling as in [33], our orthogonal adaptive ray-samplingonly needs one MLP to process the 3D information directly.
Additionally, we design a perceptualloss to guarantee the quality of rendering feature maps.
All in all, our FNeVR can not only generatemore realistic images than 2D-based methods, but also obtain more accurate motion transfer than3D-based methods (see Fig. 1(e)).
Moreover, we present a Lightweight Pose Editing (LPE) module,which is simple yet effective and only takes Euler angle as the input to efficiently realize facial poseediting.
We further design a new loss to supervise the face editing model.
The contributions of thispaper include:
• A Face Neural Volume Rendering (FNeVR) network is presented to take the merits of 2D
motion warping on face expression transformation and 3D volume rendering on high-qualityimage synthesis in a unified framework for realistic face animation.• A Face Volume Rendering (FVR) module with orthogonal adaptive ray-sampling is proposedto capture facial details effectively and improve animation performance.
Additionally, asimple yet effective Lightweight Pose Editing (LPE) module is presented only based on theEuler angle.• Extensive experiments are conducted to compare FNeVR with state-of-the-art methods.
The
results show that our FNeVR obtains the best overall quality and performance on widelyused talking-head benchmarks.
Related Work
Face Animation.
Model-free methods [55, 45, 46, 52, 7, 51] complete the deformation of faceimages by establishing the motion field without extracting prior facial information in advance.
Earlymodels [55] can extract identity and pose information, but they may fail to capture the appropriatedeformations in some problematic cases.
MonkeyNet [45] handles motion transfer by acquiringsparse keypoints.
Subsequently, the First Order Motion Model (FOMM) [46] significantly improvesthe performance of face animation with a rigorous first-order mathematical model.
While reportingpromising synthesized images, most 2D model-free methods have limited ability to model out-ofplane rotations and expressional motions.
Face vid2vid [51] further improves FOMM by introducing3D representations to model a 3D motion field for face generation.
Nevertheless, it has a considerablecomputational cost and still lacks the capability of expression transformation.
Landmark-basedmethods [2, 15, 56, 19, 49, 57] utilize 2D facial landmarks as conditions for reenactment.
However,since 2D facial landmarks cause the injection of identity-related information from the driving face tothe generated one, these methods often cannot handle identity preservation well during the generationprocess.
[Figure]
We first extract the warped feature and 3D face reconstruction representation, on which FVR is then performed to obtain the 3D shape and color information for face rendering. The 3D face reconstruction result supervises LPE and FVR by providing pose angles and 3D face vertices. In order to address the above issues, many works [28, 27, 30, 12, 41, 48] focus more on 3D structurebased methods by means of the geometric prior of 3D faces and produce impressive results onsubject-agnostic face synthesis.
HeadGAN [12] employs the rendered 3D mesh as input to synthesizedeformed images, but it does not work well in facial expression transformation.
Conditioned on theparameters of the 3D Morphable Model (3DMM) [3], StyleRig [47] and GIF [17] respectively employpre-trained StyleGAN [25] and StyleGAN2 [26] to warp face images.
PIRenderer [41] controls theface motions and predicts a flow field for deformation.
Nonetheless, these methods are intrinsicallylimited to modeling the specific head components such as hair and teeth.3D Morphable Face Models.
The 3D Morphable Model (3DMM) [3] is a statistical model thatprovides 3D face representation for rendering face images by parameter modulation.
Recent methods[40, 16, 4, 5, 6] extend 3DMM to more precise modeling of shape, expression, or pose on the basisof the original synthesis.
Among these models, FLAME [31] additionally models the head pose,joint pose, and geometric expression information, offering more realistic and expressive 3D faces.
Benefitting from 3DMM, the face reconstruction methods [11, 14, 1, 54] achieve significant progress.
The representative method DECA [13] introduces an encoder to extract the parameters of the face for FLAME and employs another encoder to attain detailed reconstruction.
In this paper, we exploit apre-trained encoder of DECA to extract the parameters required by FLAME and then utilize FLAMEto reconstruct a reliable 3D face representation from 2D images.3D Volume Rendering.
3D volume rendering is used for rendering 2D projections of 3D objects andscenes.
NeRF [33] is a breakthrough in view synthesis which can produce impressive rendered resultsbased on the input positional information.
Its core idea lies in optimizing a continuous volumetricscene function using a sparse set of input views.
Thanks to its excellent performance, many works[43, 36, 37, 38, 8, 18, 59, 22] integrate NeRF with GANs to directly control the pose of synthesizedresult and generate impressive multi-view images.
GRAF [43] introduces conditional GANs on thebasis of NeRF essentially, which improves the quality and 3D consistency of the rendering results.
GIRAFFE [38] can complete the rendering of a real-world scene and freely add multiple objectsto the same scene.
Differently, we introduce a lightweight and efficient adaptive volume renderingmethod, which only requires a single view to enhance the quality of the rendered 2D image.
Method
We formulate the face animation task as a talking-head generation with an editable pose.
For a givensource image S and a driving video { D 1 ,D 2 , · · · ,D N } , where D i denotes the i -th frame in thevideo and N is the total frame number, the objective of our FNeVR is to generate a photo-realistictalking-head video with the same face identity as S and the motion and facial expressions derivedfrom { D 1 ,D 2 , ··· ,D N } .
As shown in Fig.
2, the whole framework mainly consists of four modules toachieve this goal: (1) 2D motion estimation, (2) 3D face reconstruction, (3) FVR, and (4) LPE.
With
S and D i as input, we first utilize the 2D motion estimation module to estimate 2D facial keypointsand generate a motion field to transform the features.
We also employ the 3D reconstruction moduleto build 3D face representations to provide the rendering network with precise and reliable 3D shapeinformation.
Then a novel FVR module is introduced to fully explore the capacity of the 2D motionmodule on the challenging expression transformation and generate images with high fidelity.
Inaddition, the LPE module is added to extend our model to edit the facial pose efficiently.
3.1 2D Motion Estimation
Despite the trend that most existing methods [51, 21, 12] focus on studying various estimationmethods of motion fields, extensive experimental results in [46, 48] show that the strategy of warping2D features achieves a consistent motion transfer of facial details.
Based on this observation, weutilize the 2D motion estimation module proposed in FOMM [46] to complete the warping process.
As shown in Fig. 3(a), we use an Hourglass Network [35] as the keypoint detector to extract K
= 10keypoints { p S,k , p D,k ∈ R 2 } and their Jacobians { J S,k , J D,k ∈ R 2 × 2 } of both source and drivingimages.
For each keypoint, we utilize the first-order Taylor expansion to build an affine approximationmotion filed according to [46]:
[Equation]
where z ∈ R 2 indicates a location in the image.
Apart from K sparse motion fields, an extra motionfield is further introduced to preserve the static background information.
To generate the final densemotion field, we aggregates these K + 1 motion fields with related weight masks { M 0 ,M 1 , · · · ,M K }estimated by a U-Net used in FOMM.
Furthermore, an additional occlusion map O indicating regionsto be inpainted is also predicted.
The process of obtaining the dense motion field and the warpedfeature F w is:
[Equation]
2) where f w ( · , · ) is the warping function, F S denotes the source feature and ⊙ is the Hadamard product.
3.2 3D Face Reconstruction
As shown in Fig. 3(b), we exploit the capability of a pre-trained encoder [13] to extract the parametersrequired by FLAME [31] and use the prior face knowledge of FLAME to reconstruct a 3D facerepresentation from 2D images.
The reconstruction module is a typical encoder-decoder structure,where FLAME serves as the decoder, using linear blend skinning (LBS) and additional expressionblend shapes to generate a detailed mesh v ∈ R 3 N with N = 5023 vertices.
Mathematically, giventhe parameters of shape β ∈ R | β | , pose θ ∈ R 3( K +1)
(a global rotation vector and K joints’ rotationvectors), and expression ψ ∈ R | ψ | , FLAME is represented by a blend skinning function W as:
[Equation]
where T p ∈ R 3 N depicts a head mesh in zero pose, J ∈ R 3 K denotes the locations of the K joints,and W ∈ R K × N acts as blend weights.
The camera parameter c is further estimated to scale andtranslate the reconstructed vertices to the camera view.
Meanwhile, the shape parameter β of thesource image is assigned to the driving image as the reconstruction target.
3.3 Face Volume Rendering (FVR)
We design a new neural rendering framework, Face Volume Rendering (FVR), for face animation asillustrated in the red box in Fig.
In this framework, we innovatively employ our designed CNNsto effectively extract the 3D color and shape features from the warped feature.
We also introduce anew loss function to optimize the 3D shape feature with the 3D reconstruction result, which is onlyutilized during the training process without any overhead for inference.
Moreover, we develop a neworthogonal adaptive ray-sampling module to estimate the sampled color field and voxel probabilityfor image rendering in an efficient way.
With these novel designs, our FVR module is capable ofcapturing more realistic facial details than 2D rendering.
We also provide more details about ourmethod in the supplementary materials. [Figure] Estimation &
Pose Editor [Figure] 3D Feature Extraction. Based on the warped feature F w , we first need to extract the high dimensional 3D shape feature F σ and 3D color feature F color for subsequent face volume rendering.
To thisend, we respectively design two CNNs P σ and P color (see the supplementary materials), by whichricher 3D shape and color features can be achieved than [33].
On the one hand, P σ calculates F σ = P σ (
F w ) ∈
R H
× W × D
×
N σ , where D represents the spatialdepth information and N σ is the number of channels representing the 3D shape information, whichis set to 16 in this paper.
Observing that the spatial mesh v can be well reconstructed by the 3Dface reconstruction module, it is thus reliable to utilize the 3D mesh feature F m derived from v tosupervise P σ to achieve a reasonable result.
We maximize the inner product between vectorized F mand F σ , which leads to a matching loss function L σ :
[Equation]
4) where α 1 and α 2 are parameters to control the shape of the loss function.
In practice, we empiricallyset α 1 = 10 and α 2 = 0 .
Here, we elaborate on computing F m below.
Firstly, we conduct adown-sampling operation on the 3D vertices v to get N down sampled vertices v down , which helps toreduce the calculation cost and eliminate redundant vertex information.
Additionally, we leverage the
Gaussian function to process the i -th sampled vertex v down,i , forming a Gaussian heatmap F m,i withthe size of H × W × D × 1 :
[Equation]
where σ is the variance and we set σ = 0 .
Afterward, the 3D mesh feature of the reconstructionresult F m with the size of H × W × D × 1 is calculated by a weighted sum of all the Gaussianheatmaps F m,i as:
[Equation]
where w i is the weight for F m,i .
Notably, w i is predicted adaptively by an additional CNN.
It isworth noting that the 3D reconstruction vertices are not needed during inference, and we can thereforecomplete the image synthesis in an efficient way.
On the other hand, we need to extract the 3D color feature from the 2D warped feature F w .
To thisend, we use P color to estimate the 3D color feature F color with the size of H ×
W × D × N color :
[Equation]
where N color is the number of channels representing the color information.
Orthogonal Adaptive Ray-Sampling.
With the extracted 3D features, we further calculate thesampled color field and the voxel probability, which are required for rendering.
Different fromconventional 3D volume rendering tasks [18, 43, 38], face animation can be considered as a singleview task, which casts rays from the camera into the scene in the direction orthogonal to the imageplane.
Accordingly, we design an orthogonal ray-sampling module to adaptively estimate the sampledcolor field p color and the voxel probability p σ , which is achieved by a single MLP-based one-stageprocess f θ as follows:
[Equation]
where M color is the number of channels representing the color information of p color , and the intervalsampling number D is the same as the previous spatial channel number D .
Note that p σ is theintegral of the volume density with an adaptive interval size along the orthogonal ray. In particular,the selection of the interval size is actually involved in the estimation of the voxel probability p σ andadaptively adjusted by the MLP. It is worth noting that unlike the hierarchical volume sampling as in [33], which requires a two-stagenetwork to process multiple views according to the rays of different directions, our orthogonaladaptive ray-sampling only needs one MLP to deal with the assumed ray at the angle orthogonal tothe image plane. Furthermore, instead of decoupling the facial attributes as geometric-prior guidedsampling methods [22], we directly process the 3D related features to predict the sampled color fieldand the voxel probability more efficiently.
In short, by simplifying the ray-sampling with an adaptiveapproach, our one-stage method can significantly increase the rendering speed compared with thecommon two-stage process.
More details about the sampling module can refer to the supplementarymaterials.
Image Rendering.
Specifically, we project the sampled color field p color and the estimated voxelprobability p σ onto 2D to generate the rendering feature map F r .
Our rendering method is a modifiedversion of that in [32] according to orthogonal adaptive ray-sampling.
Also let i be a pixel point inthe feature map, and j be a sample interval in each pixel i .
The rendering method calculates the finalcolor result F r,i for each pixel i according to the corresponding sampled color field p color,i,j andvoxel probability p σ,i,j as:
[Equation]
where p σ,i,j and p color,i,j represent the voxel probability and the sampled color field of the j -thinterval of the i -th pixel respectively, and τ j = exp( (cid:80)
j − 1 k =1 − p σ,i,k ) is the transmittance.
Moreover, to ensure the effectiveness of the rendering feature F r , we introduce a two-layer CNN P tand the VGG perceptual loss [24, 50] in the training process.
Specifically, we directly input F r intothe CNN to generate an intermediate image I m , and then compute the perceptual loss L R between
I m and the driving frame D i as:
[Equation]
Afterward, we concatenate the volume rendering feature map F r and the warped feature F w , and feedthem into our designed rendering decoder which introduces the SPADE layer [39] to obtain the finalgenerated result.
This decoder (see the supplementary materials) can enhance the rendering qualitywhile preserving facial details of the input.
3.4 Lightweight Pose Editing (LPE)
Existing face animation methods, which adopt a 2D warping motion network [46, 48, 21], workwell in the feature warping but fail on pose editing.
As illustrated in Fig. 3(a), we introduce alightweight network (LPE) to add a new pose editing function for our framework.
Taking the rotationangle information { yaw ∈ R 1 , pitch ∈ R 1 , roll ∈ R 1 } provided by the 3D face reconstructionmodule and the keypoint information of the source image as input, LPE ( f editor ) estimates the targetkeypoints δ value and their Jacobians δ Jacobian .
In practice, we first formulate yaw , pitch and roll asa 3D rotation matrix r rotate ∈ R 3 × 3 , and then introduce an MLP as f editor to compute δ value andδ Jacobian under a specific rotation angle θ rotate :
[Equation]
Moreover, we design a loss L editor to supervise f editor by aligning δ value and δ Jacobian with thekeypoints and the Jacobians of the driving frame, respectively:
[Equation]
where λ 1 and λ
[Table]
3.5 Training
Our framework is implemented based on self-supervised learning or self-reenactment, where a pairof the source image S and the driving image D i from the same speaker in a video is used as theinput and D i also acts as the ground truth for supervision.
We train our FNeVR by minimizing thefollowing loss L total :
[Equation]
where x generated is the output of the whole network FNeVR.
L total consists of four parts: (1) L Pand L G are used to maintain the quality of the generated image; (2) L E is used to adjust the 2Dmotion estimation module to predict reasonable motion fields; (3) L R and L σ are used to optimizethe 3D volume rendering module to generate better 3D rendering features; (4) L editor is used tooptimize the face pose editor.
For (1) and (2), we employ the same loss functions as in [46], includingthe perceptual loss L P , GAN loss L G and equivalence constraint loss L E .
For (3) and (4), the lossesmainly provide the quality guarantee of the 3D rendering feature F r .
Experiments
In addition to the extensive experiments described in this section, we also provide more results in thesupplementary materials.
4.1 Implementation Details
Datasets.
Our evaluation is performed on VoxCeleb [34], which contains more than 100,000 videoscovering 1,251 speakers of different identities, and VoxCeleb2 [9], which contains about 1M videosof different celebrities.
We adopt the same preprocessing strategy of cropping faces from the videosand resizing them to 256×256 as in [46].
Training Details.
We train FNeVR with self reconstruction on the training set, in which a pair ofsource and driving images from the same speaker in a video are used as input, and the driving imagealso serves as ground truth for supervision.
FNeVR is trained for 100 epochs, repeating the videoimage set 75 times per epoch.
We adopt Adam [29] optimizer with learning rate η = 2 × 10 − 4 ,β 1 = 0 .
5 and β 2 = 0 .
9 for each module.
Since our model is lightweight, we only use 2 24GB
NVIDIA 3090 GPUs during training.
Evaluation Metrics.
The metrics include (1) reconstruction faithfulness using L 1 , PSNR, SSIM [53],and LPIPS [58], (2) output visual quality using FID [20], (3) Average Keypoint Distance (AKD) [46],(4) Average Euclidean Distance (AED) [46], and (5) identity preservation cosine similarity CSIM[42, 23].
4.2 Comparison with State-of-the-Art Methods
Methods.
We compare our FNeVR with five state-of-the-art (SOTA) models: FOMM [46], Facevid2vid [51], Face vid2vid-S [51], Bilayer [56], DaGAN [21], and PIRenderer [41].
We use theofficial pre-trained models for Bilayer, FOMM, DaGAN and PIRenderer, and a widely recognizedunofficial model for Face vid2vid due to the absence of the official code. Face vid2vid-S is obtained
[Table]
[Table]
[Figure]
by replacing the decoder of Face vid2vid with a SPADE [39] layer-based decoder.
All of these modelsare trained on VoxCeleb.
Same-Identity Reconstruction.
We conduct a quantitative comparison with SOTA methods on thetesting dataset of VoxCeleb when the source image and the driving image belong to the same person.
As shown in Table 1, it is evident that our FNeVR outperforms other SOTA methods on all metrics.
Especially, FNeVR achieves pronounced improvements on several metrics, such as image quality(PNSR), semantic consistency (AKD), and image authenticity (FID).
Fig. 4(a) shows that FNeVRcan generate the most realistic images that are most similar to the driving faces.
Cross-Identity Reenactment.
We compare the performance of our FNeVR with SOTA methods onthe VoxCeleb and VoxCeleb2 testing datasets when the source image and driving video come fromdifferent persons.
Concretely, we randomly select 10 source images and 14 driving videos from thetesting datasets.
Table 2 illustrates that FNeVR produces the best overall performance.
Although
Face vid2vid-S has a better result in terms of CSIM, it consumes several times more computation andmemory cost (FLOPs and Parameters) than FNeVR, as indicated in Table 3, demonstrating FNeVR’soutstanding efficiency in compact computation.
Moreover, Fig. 4(b) visualizes some synthesis resultsfrom different methods of reenactment and shows that FNeVR can generate more realistic faceswhose poses and expressions are closest to the driving faces.
[Figure]
Pitch
Roll
Configurations
[Table]
4.3 Ablation Study
We conduct comprehensive experiments on the same-identity reconstruction task to further demonstrate the effectiveness of each module in the proposed FNeVR.
Specifically, we employ FOMM[46] as the baseline
[Table]
and construct four comparison models: (1) replacing FOMM’s decoder with ourdesigned decoder, (2) inserting FVR into FOMM, (3) FNeVR without F w inputted into the decoder,and (4) FNeVR without L σ .
The results are shown in Table 4.
Both FVR and our designed decoderhave significant effectiveness.
In particular, FOMM with FVR inserted produces much better resultsthan the baseline. Furthermore, FNeVR without F w performs worse than the full FNeVR, indicatingthat not inputting F w into the decoder will affect the model’s ability to transfer the facial poses andexpressions, and lead to the loss of facial details in the generated results.
And the results show thatif L σ is not introduced, all metrics are worse than the full FNeVR, indicating that it is necessaryto introduce reliable 3D information. Meanwhile, even without the 3D information, FNeVR stillperforms well, demonstrating the great effectiveness of the proposed framework.
Note that our fullmodel produces the best overall results and surpasses other models a lot in several metrics, fullyverifying the efficacy of FNeVR.
4.4 Lightweight Pose Editing
With the pose parameters, we conduct a qualitative experiment with pose editing visualization in
Fig.
5 to further evaluate the effectiveness of the proposed LPE.
Specifically, according to the Eulerangle ( yaw , pitch , and roll ) provided by the 3D face reconstruction result, we separately adjust therotation effect of the generated results.
Fig.
5 shows that LPE can generate reliable face rotationsaccording to a given Euler angle.
Conclusion
In this paper, we propose a Face Neural Volume Rendering (FNeVR) network for face animation,which unifies the 2D motion warping and 3D volume rendering in one framework.
We follow theline of 2D motion warping and introduce the 3D face reconstruction module to provide reliable 3Dshape information for the rendering process.
We innovatively develop a Face Volume Rendering(FVR) module to enhance the facial details of the warped feature and generate high-quality faces.
Moreover, we design a Lightweight Pose Editing (LPE) module, which can directly implement poseediting with rotation angles.
Extensive experiments illustrate that our FNeVR achieves state-of-the-artperformance.
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