Generative Neural Articulated Radiance Fields
Authors: Alexander William Bergman
Petr Kellnhofer
Wang Yifan
Eric Ryan Chan
David B. Lindell
Gordon Wetzstein
Venue: NeurIPS 2022 Accept (2022)
Keywords: unsupervised learning
GAN
neural rendering
neural scene representation

Abstract: Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress. These 3D GANs, however, have not been demonstrated for human bodies and the generated radiance fields of existing frameworks are not directly editable, limiting their applicability in downstream tasks. We propose a solution to these challenges by developing a 3D GAN framework that learns to generate radiance fields of human bodies or faces in a canonical pose and warp them using an explicit deformation field into a desired body pose or facial expression. Using our framework, we demonstrate the first high-quality radiance field generation results for human bodies. Moreover, we show that our deformation-aware training procedure significantly improves the quality of generated bodies or faces when editing their poses or facial expressions compared to a 3D GAN that is not trained with explicit deformations.

Generative Neural Articulated Radiance Fields
Alexander W. Bergman" Stanford University
Petr Kellnhofer TU Delft p. kellnhhferrtuuelff..n
Wang Yifan"
Stanford University
twbestanford.c edu
yifan. wang@stant ord. edu
Eric R. Chan" Stanford University
David B. Lindell
Gordon Wetzstein
University Toronto
Stanford University
erchan@stanford. edu
Vector Institute lindell0cs toronto.ceo.. edu
gordonwz@stanfordd edu
/
Abstract
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress. These 3D GANs, however, have not been demonstrated for human bodies and the generated radiance fields of existing frameworks are not directly editable, limiting their applicability in downstream tasks. We propose a solution to these challenges by developing 3D GAN framework that learns to generate radiance fields of human bodies faces in canonical pose and warp them using explicit deformation field into a desired body pose or facial expression. Using our framework, we demonstrate the first high-quality radiance field generation results for human bodies. Moreover, we show that our deformation-awee training procedure significantly improves the quality of generated bodies or faces when editing their poses or facial expressions compared to 3D GAN that is not trained with explicit deformations.
Introduction
Unsupervised learning of 3W-aware generative adversarial networks (GANs) using large-scale datasets of unstructured single-view images is an emerging research area. Such 3D GANs have recently been demonstrated to enable photorealistic and multi-view consistent generation of radiance fields representing human faces [177] These approaches, however, have not been shown to work with human bodies, partly because learning the body pose distribution much more challenging given the significantly higher diversity in articulations compared to facial expressions.
Yet, generative 3D models of photorealistic humans have significant utility in wide range of applications, including visual effects, computer vision, and virtual or augmented reality. In these scenarios, it is critical that the generated people are editable support interactive applications, which is not necessarily the case for existing 3D GANs. While variations of linear blend skinning g have been adopted to articulate radiance fields for single-scene scenarios 21 it unclear how to efficiently apply such deformation methods to generative models.
With work, dubbed generative neural articulated radiance fields GNARE, propose solutions to both of these challenges. Firstly, we demonstrate generation of high-quality 3D (i.e., multi-view consistent and geometry aware) human bodies using GAN that is trained in unsupervised manner on datasets containing single-view images. To this end, we adopt the recently proposed tri-plane
Equal contribution
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

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3D GAN Canonical Pose
Radiance Field in
Animated Poses and Novel View Synthesis
Our method, GNARE, maps a latent space to radiance fields representing human identities. These generated humans can then be animated and rendered from novel views. Qualitative results for our method trained on the AIST+ dataset 23 are shown here.
Figure 1:
feature representation [. which is extremely efficient for training and rendering radiance fields, while being compatible with conventional 2D CNN-based generators, such as StyleGAN [22]. While this framework has been successfully demonstrated for faces in prior work, we the first to adapt to generating radiance fields of full human bodics. Secondly, we tackle the editability of the generated radiance fields by introducing procedure. This step ensures that the generator synthesizes radiance fields of people a canonical body pose, which is then explicitly warped according the body pose distribution of the training data. We show that this new approach generates high-quality, editable, multi- multi-view-come view-consistent human also be applied to editing faces, increasing the controllability of
explicit radiance field deformation step part of our GAN training
bodies and that approach existing generative models for this task (see Fig. 1
To summarize, the contributions of our approach are:
We present a 3D-aware GAN framework for the generation of editable radiance fields of human bodies. To our knowledge, this is the first approach of its kind. Our framework introduces an efficient neural representation for articulated objects, including bodies and heads, that combines the recently proposed tri-plane feature volume representation with an explicit feature volume deformation that guided by template shape.
We demonstrate high-quality results for unconditional generation and animation of human bodies using the SURREAL and AIST+ datasets and faces using the FFHQ dataset.
2 Related Work
Parametric shape templates are one of the most common types of articulated 3D representations adopted in recent neural scene representation and rendering approaches. These templates, including faces [4 25 bodies [26]. hands [27]. or a combination of these parts 28
Articulated 3D Representations.
and even animals [29], have been widely utilized for pose estimation and reconstruction, e.g. B0
Volume deformation is also commonly used in computer graphics, for example using mean va lue
coordinates (MVC) (35) or biharmonic coordinates (36) for shape deformation and editing [37,4]
GNARF combines parametric template shapes, such FLAME (25) for heads and SMPL 226 for bodies, with an intuitive surface-driven volume deformation approach that is both computationally efficient and qualitatively comparable to or better than both skinning and MVC-based deformation. This provides intuitive editing control for articulated radiance field deformation.
Neural Radiance Fields. Coordinate networks, also known neural fields 42
have emerged as
a powerful tool that enable differentiable representations of 3D scenes 4k 56 and learning view- dependent neural radiance fields 57-83) While initial proposals have focused on static scenarios,

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recent work has demonstrated successful representations of dynamic scenes [4] Articulated neural radiance fields further extend these approaches by providing editability for neural represen-
tations of human heads 192 |92-97 97 and bodies 19 98 " or animals [00], often by deforming
the underlying radiance fields using traditional 3D morphable models or skeleton-based parameter- izations, or alternatively conditioning the radiance field decoder with pose-related parameters. A more detailed survey of static, dynamic, and articulated neural radiance fields can be found in the recent state-of-the-a thrt report by Tewari et al. [101]. Note that all of these techniques are supervised with scene-specific multi-image data and focus on representing, i.e., "overfitting", a single scene. Therefore, it is not easily possible train these models using unstructured 2D image data and then apply them to generate and edit new and unseen objects or humans.
Generative 3D-aware Radiance Fields. Building the success of 2D image-based GANs [2 [024104], recent efforts have focused on training 3D-aware multi-view consistent GANs from collections of single-view 2D images in an unsupervised manner. Achieving this challenging goal requires a combination of a neural scene representation and differentiable rendering algorithm. Recent work in this domain builds representations using meshes [I05] 106), dense [107 or sparse [133] voxel grids, multiple planes (2). fully implicit networks 7 or a combination of low- resolution voxel grids combined with 2D CNN-based image upsampling layers [144115 Our 3D) GAN architecture is most closely related to the recent work by Chan et al. D. which uses an efficient tri-plane-based volume representation combined with neural volume rendering. We extend this work by including an explicit deformation field in our GAN architecture to model diverse articulations, which allows the generator synthesize radiance fields of human bodies or heads in canonical pose while being supervised by 2D image collections that contain arbitrary pose distributions. Explicitly disentangling radiance field generation and deformation enables us drastically improve the quality of generated human bodies and faces when their poses or facial expressions are edited.
HeadNeRF [166] is related to our approach in that they generate 3D heads conditioned various attributes. Both HeadNeRF and GNARF condition on identity and facial expression independently, with the former also conditioning on illumination and albedo. However, to disentangle individual attributes they need to acquire training images of the same person performing various expressions in different lighting conditions. In contrast, and similar to other 3D GANs, our approach only requires single-view images of different people and can therefore work with readily available 2D image collections. The recent work by Grigorev et al. [117] also generates human bodies. However, their work proposes a 2D GAN that generates textures which are used in combination with a standard articulated template mesh whereas we aim at generating and editing radiance fields using a 3D GAN. The concurrent work of Noguchi et al. [118] is closest to ours as the method also includes a radiance field deformation step in tri- tri-plane-basee based 3D GAN, Our evaluation shows superior generation quality and, more importantly, while their approach ties the network architecture to the specific choice of skeleton, our surface-driven volume deformation is agnostic to the particular choice of template and can be used with human bodies, faces, or other object types.
3 Generative Articulated Neural Radiance Fields
GNARF is a novel general framework to train 3D-aware GANs for deformable objects that have parametric template mesh c.g. human bodies and faces. It builds on the efficient tri- tri-plane feature representation [T] for the generated neural radiance field, but additionally applies an explicit deformation which alleviates the requirement for the generator learn complicated distribution of articulations. As a result, the generator automatically learns to generate radiance fields of objects in the canonical pose, which are then warped explicitly to produce target body poses and facial expressions in a fully controllable and interpretable manner.
3.1 Modeling Articulated Radiance Fields
We first discuss our approach to modeling and rendering articulated radiance fields, before describing how this is integrated into the 3D GAN in Sec. 3.2
Scene representation. To represent an object, we leverage the recently proposed tri-plane feature representation ①]. This representation uses three axis-aligned 2D feature planes, each with resolution N x N x C, where N and C' denote the spatial resolution and number of channels. The feature of any
3

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522 saue
Intermechuta letant
Marping Network
Final Image
StyleGANZ Discriminator
Deformation
Neural Rendering
Foutiures deddeG2
Tr-pann
Volume
Superres.
of
Raw Ihape
Module
StyleGAN2 Generator
Body pooes
Carrena peramts
Figure 2: Illustration of the GNARE pipeline, including the StyleGAN2 generator, the tri-plane feature represen- tation, feature volume deformation, neural volume rendering, image super-resolution as well as camera view and body-pose conditioned dual discrimination. The resolution of intermediate data and the final image is indicated for experiments with the AIST+ dataset.
3D point x E I is queried by projecting onto the planes, retrieving three feature vectors via bilinear interpolation, and aggregating the vectors by summation, i.c., F (x) Fay (x) + Fys (x) + Fes (x) where Fi IR + RC is function mapping 3D coordinates to features on the ij plane via projection and interpolation.
Articulated deformation. We use the deformation function D R33 + R (detailed later) to warp coordinate x from the target (deformed) space into the canonical space. Using small multilayer perceptron MLP : IR C + R we convert the deformed 3D feature volume into a neural field of spatially varying RGB colors and volumetric densities
(c (x) o (x)) = ML. ((Fay (x) + (Fyz o (x) + (Faz D) (x)).
There are many possible choices for how to specify the deformation field in an intuitive manner. For example, linear blend skinning can be used to deform the entire volume "rigged" by a skeleton (see
e.g. ()). While skinning popular for human body articulations, it cannot explain subtle deformation due to varying facial expressions. Another option is to use the object-specific template mesh as a cage and apply cage-based deformation for the entire volume using mean value coordinates (MVC) [35]. However, the high computational cost of evaluating MVCs on the full-resolution grid (see Tab. is prohibitive for GAN training and, more critically, this approach generally leads to severe artifacts when the template mesh (accidentally) includes self-intersections (see supplement).
To alleviate these problems. we use an intuitive surface-driven deformation method, which we label as the Surface Field (SF) method. This method only requires canonical and target template meshes with correspondences, which are readily available for faces (25 and bodies 26). These template shapes, in turn, can be driven using skeletons, manual editing, that could be detected in and transfered from videos of other people. Therefore, the SF method is generally sufficient to apply to different body parts and it can be intuitively edited in number of ways, resulting in accurate volume deformation for our class volumetric models.
using keypoints or landmarks
The SF approach assigns each 3D coordinate x to its nearest triangle (4 [Vo, V1, Vel E 3x3 on the target (deformed) mesh. We compute the barycentric coordinates [u, v, w/ of the coordinate projected onto this triangle and find the corresponding triangle on the canonical mesh & and its normal n.: The deformed coordinate can then be computed as
D) (x) = [u, v, w]" + +(x 12. woj .n. n
(2)
The SF approach is very fast to compute and mitigates artifacts from seff-intersections of the template shape, thereby combining the benefits of linear blend skinning and MVC-based approaches for the task of radiance field deformation.
Rendering deformed radiance fields. We render the radiance field using (neural) volume render- ing 62 [19.. For this purpose, the aggregated feature F(r) of ray is computed by integrating the volumetric features f and density a as
F(r) l" (()) dt, (t) exp (- 1 (r()))
(3)

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where t, and y indicate near and far bounds along the ray r(t) = =o + td pointing from its origin into direction d. The volume rendering equation (eq. 3 typically approximated using numerical methods, such as the quadrature rule [119]
3.2 3D GAN Framework
An overview of our 3D pipeline is shown in Fig.2 Several components, including the StyleGAN generator, the tri-plane representation, the volume rendering, the CNN-based image super-resolution module, and (dual) discrimination are directly adopted from the EG3D framework [T]
Instead of directly generating the radiance field with the target body pose or facial expression, however, GNARF is unique in generating the radiance field in a canonical pose and then applying the deformation field discussed in the previous section to warp the feature volume. We additionally remove the pose conditioning on the generator, and only use camera pose and body pose conditioning in the discriminator. This removes the ability for the generator to incorporate any knowledge about the final view or pose in the canonical radiance field generation, ensuring that the generated results will be robustly animatable beyond just the image rendered at training time. Thus, the generator depends only on the latent code controlling identity, which is input into a StyleGAN2 generator. This architectural choice takes advantage of the state-of-the-arr 2D generative model architectures by using them to generate the tri-plane 3D representation. The discriminator having access to the camera and body poses ensure that the GAN learns to generate warping accurate to a target pose rather than just being in the correct distribution. Finally, adopt a radiance field rendering strategy which samples along each ray inside of an expanded template mesh. This ensures that the integration samples are taken in regions of the radiance field with the most detail and not taken in empty space, simultaneously improving the quality of the generated results and speeding up training.
Additional implementation details, source code, and pre-trained models can be found in the supple- ment or on our website.
4 Experiments
We first evaluate the proposed deformation field by overfitting a single representation on a single dynamic full body scene. Then we apply this deformation method in GAN training pipeline for both bodies (AIST++ 23 and SURREAL (100) and faces (FFHQ) [104. Training details and
hyper-parameters are discussed in the supplement.
AIST++ is a large dataset consisting of 10. IM images capturing 30 performers in dance motion. Each frame is annotated with ground truth camera and fitted SMPL body model. SURREAL contains 6M images of synthetic humans created using SMPL body models in various poses rendered in indoor scenes. FFHQ large dataset of high-resolution images of human faces collected from Flickr. All images have licenses that allow free use, redistribution, and adaptation for non-commencial use.
4.1 Single-scene Overfitting
We compare the proposed surface-driven deformation method, SF, with two alternative methods, MVC and skinning, in a single-scene overfitting task. MVCs require set of weights (called the mean value coordinates) to be computed w.r.t. every vertex of the target mesh M for every sample point. The sample point is then deformed into the canonical pose by linearly combining the vertices of the canonical mesh M with these computed weights. In skinning, the sampling points are deformed to the canonical pose by the rigid transformation of the closest bone as measured by point to line-segment distance. We find this simplified definition of skinning effective in avoiding blending between two topologically distant body parts (c.g., hand and pelvis) the starting pose brings them to geometric proximity.
We select a multi-view video sequence from the AIST+ dataset (33 and optimize tri tri-plane features in the canonical pose using a subset of the views and frames for supervision. We then evaluate the quality of the estimated radiance field warped into these training views and poses but also into held-out test views and poses. We apply several modifications to the tri-plane architecture to reduce overfitting: details regarding these changes as well as the selection of training and evaluation views provided in the supplemental material.
5

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Training images
Test images
Run time [ms] +
PSNR
SSIM 1
LPIPS +
PSNR 1
SSIM t
LPIPS
Skinning 18.8
0.942
0.060
17.9
0.940
0.067
95.6
MVC B5
18.1
0,937
0.067
17.2
0.934
0.074
0.2 (3782.1)
Surface Field
19.0
0.943
0.058
18.0
0.940
0.065
31.6
Table 1: Single-scene overfitting. We evaluate three deformation approaches for the task of estimating single radiance field in canonical body pose supervised by video sequence showing person from different views different poses. The approach achieves the best quality for both training and unseen test images while being the fastest. Note that the MVC method is only faster than SF when using precomputed grid at the cost of significantly lower deformation accuracy (the runtime without such approximation is reported in parentheses). The timings measured to deform single feature volume RTX3000 graphics processing unit.
EG3D Generated Estimated Canonical Pose
Rendered Target Pose Images
Canonical Pase
Figure 3: Qualitative comparison of generated target poses using our model vs. warping pre- pre-trained EG3D model on the AIST++ dataset.
To speed up MVC and SF computation, we decimate the source and deformed SMPL mesh using Quadric Error Metric Decimation [121] in the Open3D library [12 from the original 13,776 faces to 1,376 faces, while tracking the correspondence between the source and deformed meshes. Nonetheless computing MVC for each deformed pose still prohibitively expensive for online training (3.7 s per example). We thus precompute the deformation for training and test body poses a fixed 16 grid and retrieve the deformation for arbitrary sampling points using trilinear interpolation.
As shown in Tab. M our SF method outperforms the others for both training and test images. MVC performs worst, partially due the grid approximation, which essential practice. The skinning method is comparable to SF in terms of image quality but it is 3x slower. Moreover, skinning cannot sufficiently deform subtle facial expressions. Therefore, the SF approach the most flexible among these deformation methods by being compatible with different human body parts while also offering computational and memory efficiency.
4.2 Human Body Generation and Animation
We now our SF approach as the deformation method for feature volumes generated by GNARE. Our method is trained and evaluated on the captured AIST++ (23 and the synthetic SURREAL 120 datasets. For both datasets, method generates high-quality multi-view consistent human bodies in diverse poses that closely match the target pose.
Baselines Evaluation. Because GNARF the first method to learn a generative model of radiance fields representing bodies, we propose baseline where we use the original EG3D trained without deformation to generate feature volume (not the canonical pose) then warp it into various target poses during inference time using the proposed SF deformation method. Without the feature

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AIST++ @256
SURREAL @ 1288
FID (50k)
PCKH@0.5
FID (10K) 4
FID (50k) &
PCKh@0.5 f
ENARE-VAE T
63.0
ENARF-GAN [18
21.3
0.966
EG3D (no warping)
8.3
14.3
13.3
EG3D (+ pose est. & re-warping)
66.5
0.855
163.9
162.2
0.348
GNARF
7.9
0.980
4.7
5.7
0.999
Table 2: Quantitative evaluation of GAN trained SURREAL 120] and AIST+ (23)
Our method only
considers foreground images (backgrounds masked). "Metries provided by authors of [18 after standardizing the evaluation protocol, which may differ from initial values in their original report.
volume deformation, the generator is forced to learn to model both identity and pose in its latent space. As result, the tri-plane features no longer represent human body in a consistent canonical pose, but rather match the distribution of poses in the dataset. The animation of generated bodies is similar to our proposed approach, except that the generated (arbitrarily posed) human body is used as the canonical pose, for which we obtain a SMPL mesh by applying the human shape reconstruction Additionally, we included the concurrent work ENARF- LENAR--AAN and its variant DNARF-VAE 118 in the evaluation whenever a fair and standardized comparison is possible.
method SPIN [1
We compare FID scores to evaluate the quality and diversity of generated images well as the Percentage of Correct Keypoints (PCK) metric to evaluate the quality of the animation. PCK computes the percentage of 2D keypoints detected on generated rendered image that within error threshold (half the size of the head in the case of PCKh@0.5) of keypoints on ground truth image in the same pose and view. We use an off-the-shelf body keypoint estimator [[23] trained on MPII [124 publically available on the MMPose Project [25] to estimate keypoints from a corresponding ground truth and generated image.
AIST++ AIST+ is challenging dataset as the body poses are extremely diverse. We collect 30 frames per video as our training data after filtering out frames whose camera distance is above a threshold or the human bounding box is partially outside the image. Then we extract the human body by cropping a 600 x 600 patch centered at the pelvis joint, and resize these frames to 256 x 256. Since this dataset does not provide ground truth segmentation masks, we use a pre-trained segmentation model [166] to remove backgrounds to stabilize the GAN training. To speed up training, we use GAN transfer learning [127]. Rather than initializing our network weights randomly, we begin training from a pre-trained EG3D (I] model. Fine-tuning allows for quicker convergence and saves computational resources during training.
As shown in Tab. 2 we can see that our method outperforms the naive reanimation of EG3D by a large margin (see the 4th row vs. the 5th row). Furthermore, our animated method also generates better images than the EG3D baseline that does not support animations. This is likely due to GNARF allowing the generator to focus on generating specific identity in a canonical pose instead of learning both identity and complex pose distribution in a combined latent space. Similarly, our model enables high-guality re-animation, as demonstrated by the PCKh@0.5 005 metric. In Fig. our method produces significantly better qualitative results than those generated by the baseline. The baseline results using re- re-warped EG3D are significantly degraded, since it is difficult to accurately estimate the SMPL mesh from the generated images, which we use as the canonical pose in the deformation function. Additionally, floating artifacts which exist the radiance field outside of camera views and make no difference in the conventionally rendered images become visible after being warped can cause false occlusions. In Fig. [] we show that our method generates bodies in a canonical pose with diverse identities. Additionally, we show that by changing the SMPL parameters, we can drive each radiance field to desired target pose and render at an arbitrary novel view.
Figure 4: Example of gen- erated humans in canonical pose and in target pose us- ing model trained on SUR- REAL dataset.
SURREAL.
We also test our method on the SURREAL dataset. The training data extracted from the first frame of each video in SURREAL's training split. Each frame is cropped based
7

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FID (500)
FID (50K) +
AED 4
APD 4
ID-Consistency 1
EG3D (+3DMM est, & re-warping)
22.9
11.6
0.29
0.028
0.81
PIRenderer 10
64.4
0.28
0.040
0.70
3D GAN inversion 150
31.2
0.36
0.039
0.73
GNARP
17.9
6.6
0.23
0.025
0.80
Table 3: Quantitative comparison on FFHQ dataset [04]
Iarget face
EG3Dewup AED=0.36 36
PiReaderer 189 AED=043
3D GAN
3
100
ABD=0.25
close-ups EG3D (lef)) and (right)
Figure 5: Deformation fidelity on FFIQ dataset. We plot the target and detected facial landmarks in blue and green, respectively, visualize the expression fidelity. PIRender [129 and 3D GAN inversion [30] both show large discrepancy to the target face, while the baseline EG3D has strong artifacts from warping (right). Our
generated results show high visual quality and they align accurately with the driving deformation.
on the provided segmentation mask from head-to-toe, and resized to 128 x 128. We filter out the backgrounds and set them to be black. The SURREAL dataset provides ground truth SMPL parameters and camera intrinsics and extrinsics for each frame. Similarly to AIST++, we use transfer learning from pre-trained EG3D model at the appropriate resolution.
In Tab. 2 we see that our method trained with deformation produces improved FID scores as the version trained without SF feature volume deformation (5th vs 3rd row). Attempting deform generated radiance fields results in an immense degradation in quality, shown by both the FID and PCKh@0.5 metrics (4th row).
Additional qualitative results and evaluations are included on the website.
4.3 Human Face Generation and Editing
Thanks to the surface-driven deformation method, SF, our method can directly utilize expressive parametric face models to drive subtle deformations. We use the FLAME [25] head model and apply the state-of-the-art facial reconstruction method DECA 128] to estimate the flame parameters (100D identity vectors, 6D joint position vectors and 50D expression vectors) from the training dataset. Since DECA does not account for eyeball movement, we remove the eyeballs from the original FLAME template. Additionally, we add triangles to close the holes the neck and mouth area in the FLAME template, which improves the consistency of the SF deformation. These preprocessing steps produce mesh with 3,741 vertices and 7,478 faces. Finally apply the same decimation method as in the body experiments to obtain a coarse mesh (2,500 triangles), which we use during the GAN training for faster SF deformation.
To train GNARF, we start with conventional EG3D model pre-trained using the FFHQ dataset (see (1 for details on the training details and data processing). We then fine-tune this model using the proposed framework, which includes the SF deformation module. Note that we apply global scaling and translation to the FLAME template mesh to roughly align it to the faces generated by the pre-trained EG3D model.
For evaluation, our first baseline is the original EG3D model with re-warping at inference time, as described in Sec. (4.2 We also include two state-of-the- ohetrr- facial reenactment methods, PIRen- derer [129] and 3D GAN inversion [130]. Several metrics are used by our quantitative evaluation. FID (500) follows the evaluation protocol of Lin al. [30,, where the ground truth dataset consists of 500 randomly sampled identities and the test dataset is constructed by animating the ground truth using randomly sampled target poses. FID (50k) follows the protocol EG3D, where the entire FFHQ dataset is treated as the ground truth and the test dataset includes 50k generated images using randomly sampled latent vectors, camera poses and FLAME facial parameters. Following [330, we evaluate the fidelity of the animation with the Average Expression Distance (AED) and the Average
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Figure 6: Qualitative results FFI dataset. We show each identity in their canonical (left) and a different
(right) expression from two different camera poses. Our method shows excellent multi- nult consistency.
Pose Distance (APD) computed using DECA estimation, as well as the identity consistency based on a face recognition model [131]
As shown in Tab. our method is superior to the baseline methods in both FIDs and in the edited fidelity, and comparable to the baseline EG3D in terms of identity consistency. The advantage of our method in editing ability clearly demonstrated in Fig. 5 our method reproduces the target expression more accurately when compared to the face reenactment methods and mitigates the warping artifacts present in the baseline EG3D. In Fig. we further demonstrate the quality of our results. Notice that even though the FLAME model does not include teeth, the SF deformation guides the neural radiance field construct teeth consistently the correct location.
5 Discussion
Limitations and future work. Our work is not without limitations. The level of detail in the generated bodies, for example, is relatively low. This is partly due to the limited resolution of the training data in the SURREAL and AIST++ datasets, but also due to the limited resolution that the tri-plane representation offers for any one body part, such the face. An interesting avenue of future work could include the exploration of adaptive radiance field resolution for human bodies, allocating more resolution to salient parts (see e.g. (I32]). The image quality of the generated bodies is currently on par with simpler approaches that only need to generate an RGB texture on the SMPL mesh [17]. Yet, details in faces and hair cannot be handled by texture-generating approach and expect the quality of generative radiance fields to surpass that of simpler alternatives with increasing and perhaps adaptive radiance field resolutions. Another bottleneck of our current framework is the challenge of being able to work with large-scale datasets showing a diversity of visible humans. In- ln-lhe-wild datasets, such as MSCOCO (333. do contain this diversity but also contain significant amount of occlusion and detailed backgrounds, requiring the generator to spend capacity on modeling the distribution of backgrounds and occlusions themselves. An interesting avenue of future work consists of explicitly modeling occlusion and background in the GAN training pipeline independently from identity and pose. Currently, the background not modeled in the generative framework and requires pre- rre-processing of the dataset to separate foreground from background. Additionally, pose estimation from in-the-wild wild images still not very accurate, degrading the quality of our deformation function. On the other hand, large-scale custom curated datasets such as the one used in InsetGAN [132] are not publicly available. Finally, the deformation we use as part of the 3D GAN training is limiting in several ways: it does not allow for topology changes, it does not prevent solid parts like teeth or eyeglasses from being stretched, and the deformation quality degrades for points far from the surface. In general, using the surface of parametric mesh to guide volume may not be the optimal choice for more complex volumetric scenes. More advanced deformation methods, perhaps including kinematic constraints, could be an interesting direction of future research.
Ethical considerations. GANs could misused for generating edited imagery of real people. Such misuse of image synthesis techniques poses a societal threat, and we do not condone using our work with the intent of spreading disinformation. We also recognize potential lack of diversity in results, stemming from implicit biases of the datasets process.

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Conclusion. Our work takes important steps towards photorealistic 3D-awar -wwar image synthesis of articulated human bodies and faces with applications in visual effects, virtual or augmented reality, and teleconferencing among others.
Acknowledgments and Disclosure of Funding
Alexander W. Bergman was supported by Stanford Graduate Fellowship. Gordon Wetzstein was supported by NSF Award 1839974, Samsung, Stanford HAL, and PECASE from the ARO. We thank Connor Lin for helping standardize comparisons to [I30]. We thank Atsuhiro Noguchi and the authors of T18 for helping in standardization of the comparisons of our methods.
References
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[132] Anna Frühstück, Krishna Kumar Singh, Eli Shechtman, Niloy Mitra, Peter Wonka, and Jingwan Lu. Insetgan for full-body image generation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[133] Tsung- Yi Lin, Michael Maire. Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft COCO: Common objects in context. In European Conference on Computer Vision (ECCV), 2014.
Checklist
1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper's
contributions and scope? [Yes]
(b) Did you describe the limitations of your work? [Yes] See Section
(c) Did you discuss any potential negative societal impacts of your work? [Yes] See Section 5
(d) Have you read the ethics review guidelines and ensured that your paper conforms to them?
[Yes]
2. you are including theoretical results...
(a) Did you state the full set of assumptions of all theoretical results? [N/A]
Did you include complete proofs of all theoretical results? [N/A]
3. If you ran experiments..
(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] We plan to release the code completely, but have not yet with submission. Details on the architecture, training, and data are available in the supplemental material.
(b) Did you specify all the training details (e.g., data splits, hyperparameters. how they were chosen)? [Yes] See supplemental document and sections and and
(c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? [No] GNARF models require a significant amount of computational resources (see supplemental document), and thus averaging training over many seeds would be computationally infeasible. However, we evaluate our trained models with large number of generated images, demonstrating the robustness of our method.
(d) Did you include the total amount of compute and the type of resources used (e.g.. type of GPUs, internal cluster, cloud provider)? [Yes] See supplemental document.
4. If you are using existing assets (c.g., code, data, models) curating/releasing new assets...
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(a) If your work uses existing assets, did you cite the creators? [Yes]
(b) Did you mention the license of the assets? [Yes] See Section
Did you include any assets either in the supplemental material URL? [No]
(d) Did you discuss whether and how consent was obtained from people whose data you're using/curating? [N/A]
(c) Did you discuss whether the data you using/curating contains personally identifiable information or offensive content? [N/A]
you used crowdsoureing or conducted research with human subjects...
(a) Did you include the full text of instructions given participants and screenshots, if applicable? [N/A]
(b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A]
(c) Did you include the estimated hourly wage paid to participants and the total amount spent participant compensation? [N/A]
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Reviews:
[{"review_id": "COSGoRk_ohr", "reviewer": "Reviewer_qMy8", "summary": "This paper proposes Generative Neural Articulated Radiance Fields, named GNARF, serving as the 3D representation of 3D-aware generator to handle the datasets with more deformation, such as human-body datasets. The non-rigid motion is represented by the surface deformation derived from the source and target meshes, which helps the tri-plane focus on learning the canonical feature volume. The experiments give a comprehensive study of GNARF on several human datasets, including ATISS++, SURREAL, as well as FFHQ. The experimental results demonstrate the GNARF can achieve better image quality and editability compared with several baselines.", "questions": "1. As shown in this paper, SF is derived from the target and canonical template meshes. Does it mean that each batch also needs to sample the target SMPL models besides the latent code? If I am right, it is not fair to compare with the EG-3D baseline w.o. any 3D mesh information. \n2. It is not convincing that the authors remove the background to stabilize the training process. Why not use a separate background model? StyleNeRF has demonstrated that it can work even on the CompCars dataset with large background variations.\n3. Since the tri-plane is required to learn the canonical feature volume, why is the latent code still injected into the tri-plane generator network? Why not only add the stochastics to the shallow MLP because the canonical pose across different instances is similar. ", "limitations": "My concerns about this paper is the synthesis quality. Although it has improved on the baseline, it still has a large gap to real-world applications like avatar animation. \nBesides, the mouth of the synthesized faces in Fig.6  and the demo video are not realistic. I guess that the Flame model does not model the teeth. Does it mean that the GNARF is very dependent on the template meshes? When transferred to a new dataset like LSUN cats which does not have any ground truth template meshes, how does GNARF perform? \nI think this paper is interesting, and I tend to accept this paper (raise the scores) if the authors can address my concerns.", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "This paper aims to generate 3D humans from a posed 2D image collection, which is rarely explored in the literature. Compared to prior arts of 3D-GANs, the paper pays more attention to the non-rigid human bodies instead of the rigid objects like faces and cars. It is valuable to solve this challenging problem. Although this task is very difficult, the synthesized results are not very good. The details of the human body, such as face and clothing,  are of a low quality. Besides, I also have some confusion about the proposed techniques, which will be presented in the following section. ", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ["I don’t know"]}, "extra_text": {}}, {"review_id": "2H-r48VbKV_", "reviewer": "Reviewer_H6JG", "summary": "This paper proposes a technique called GNARF for controllable 3D aware generation of human bodies and faces with different articulations and expressions. The authors build on the prior EG3D generative model and extend it along two dimensions -- to entire 3D bodies and for more explicit control and disentangling of base identity faces and their expressions. The authors' main idea is to introduce an explicit surface deformation module into the generative model, which deforms a canonical triplane representation. The authors parametrize this surface deformation via parametric mesh SMPL or FLAME models. They compare their surface deformation approach against MVC and blend-skinning and also their generative models for body and face against SOTA existing generative models and show improvements in quality.", "questions": "From Table 2, it is evident that the baseline EG3D (no warping) approach has FID scores nearly as good as or better than GNARF, meaning that it is able to generate highly realistic posed images. However in line 244 the authors claim that for EG3D (no warping) poor results for reposed images are produced \"since it is difficult to accurately estimate the SMPL mesh from the generated images\". If the image quality of the posed images is good as per the FID scores, what limits good SMPL mesh fitting, and reposing of the EG3D generated images?\n\nHow do the authors propose to handle cases such as subject wearing long skirts or dresses, which are likely to not fit well to the two-legged tight-fit model of SMPL?", "limitations": "The authors have adequately and honestly discussed the limitations of their proposed method.", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "- Originality: The work is original along many dimensions. It extends 3D-aware generative models to body poses, which has not been considered in prior work (other than in [115], which is concurrent work). It is also novel in introducing the explicit surface deformation model  into the EG3D framework, making it more controllable. However, it builds heavily on EG3D. Nevertheless, in my opinion this work introduces sufficient novelty over the existing works to warrant publication.\n\n- Quality: The work is technically sound; the method appropriate and its steps logical; and most claims (a few exceptions noted below) are well supported via experiments. To the authors' credit they have also gone above and beyond the submission requirements and compared against several unpublished concurrent works [115, 127] and shown the superior performance of their approach. The authors have discussed the limitations of their method honestly and in much detail.\n\n- Clarity: The material is clearly presented and easily understandable. Many implementation details are included in the supplementary material and the authors have promised to release their code upon acceptance.\n\n- Significance: \n- The dominant approach for modeling humans (bodies and faces) and animals has been via parametric mesh models for several decades now. The advent of 3D aware GANS is heralding in a new era of GAN-based generative models, which have the promise to provide much higher images quality and photo-realism, while not being limited only to the surfaces (face and body) modeled by the mesh models. 3D aware-GAN models can also model hair, eyeglasses and clothing details beyond meshes. However, the question of how best to model body articulation/surface deformation caused by expressions/body articulation into the context of 3D-aware GANS is an important open one. This work provides an important step towards addressing this latter problem. That said the proposed solution's falling back on parametric mesh models to model the surface deformation is perhaps not the most elegant solution, as the authors acknowledge themselves as well.\n- The authors' comparisons of the different types of 3D mesh-based deformations (MVC, blend-skinning and surface-based) is also quite interesting and insightful.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "-e5OmiEMLHf", "reviewer": "Reviewer_UgRe", "summary": "This paper proposes a method for generative neural articulated radiance fields. The main strategy is to generate radiance fields in a canonical pose and warp them using an explicit deformation field into a desired body pose or facial expression. The main technical contribution of this work is to combine the recently proposed tri-plane feature volume representation with an explicit feature volume deformation which is guided by a template shape. High-quality results on human body and faces are demonstrated in the paper.", "questions": "I don't have questions.", "limitations": "As mentioned in the related work, HeadNeRF is related to this work, and the authors point out that HeadNeRF needs to acquire training images of the same person performing various expressions in different lighting conditions. However, HeadNeRF can explicitly control properties like identity and expressions. To my knowledge, the proposed method can not achieve disentangled representation.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "This paper proposes a practical 3D-aware GAN framework for the generation of editable radiance fields of human bodies. The main strength of this paper is to combine several existing strategies like tri-plane feature volume representation, StyleGAN2 generator, feature volume deformation, neural volume rendering, image super-resolution, etc. Although each component has been proposed before, integration them together to generate satisfying results is not easy and could be used for related applications.\n\nOn the other side, the weakness is the lack of technical contribution as each component is borrowed from other papers. Meanwhile, some component can be replaced with more suitable strategy. For example, based on our implementation and the experimental results reported in the paper, tri-plane representation still has limited representation ability, which can be replaced by other representations to achieve better performance.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Accept