AG3D: Learning to Generate 3D Avatars from 2D Image Collections
Zijian Dong1,2∗

Xu Chen1,3∗ Jinlong Yang3 Michael J. Black3 Otmar Hilliges1 Andreas Geiger2
1
ETH Zürich, Department of Computer Science 2 University of Tübingen
3
Max Planck Institute for Intelligent Systems, Tübingen

Figure 1: Sampled 3D Human Appearance and Shape. Our generative model is learned from an unstructured 2D image
collection, yet synthesizes novel 3D humans with high-quality appearance and geometry, different identities and clothingstyles including loose clothing such as dresses and skirts. Moreover, our generated 3D humans can be easily animated.

Abstract
While progress in 2D generative models of human appearance has been rapid, many applications require 3D
avatars that can be animated and rendered. Unfortunately,
most existing methods for learning generative models of
3D humans with diverse shape and appearance require 3D
training data, which is limited and expensive to acquire.
The key to progress is hence to learn generative models
of 3D avatars from abundant unstructured 2D image collections. However, learning realistic and complete 3D appearance and geometry in this under-constrained setting remains challenging, especially in the presence of loose clothing such as dresses. In this paper, we propose a new adversarial generative model of realistic 3D people from 2D
images. Our method captures shape and deformation of the
body and loose clothing by adopting a holistic 3D generator
and integrating an efficient and flexible articulation module. To improve realism, we train our model using multiple
discriminators while also integrating geometric cues in theform of predicted 2D normal maps. We experimentally findthat our method outperforms previous 3D- and articulationaware methods in terms of geometry and appearance. We
validate the effectiveness of our model and the importanceof each component via systematic ablation studies.
* Equal contribution

1. Introduction
Generative models, like GANs [19], can be trained fromlarge image collections, to produce photo-realistic imagesof objects [5, 29–31] and even clothed humans [2, 18, 20,
33, 34, 55]. The output, however, is only a 2D image andmany applications require diverse, high-quality, virtual 3D
avatars, with the ability to control poses and camera viewpoints, while ensuring 3D consistency. To enable the generation of 3D avatars, the research community has been studying generative models that can automatically produce 3D
shapes of humans and/or clothing based on input parameters such as body pose and shape [9, 11, 38, 50]. Despite
rapid progress, most existing methods do not yet consider
texture and require accurate and clean 3D scans of humans
for training, which are expensive to acquire and hence limited in quantity and diversity. In this paper, we develop amethod that learns a generative model of 3D humans with
texture from only a set of unstructured 2D images of various people in different poses wearing diverse clothing; thatis, we learn a generative 3D human model from data that is
ubiquitous on the Internet.
Learning to generate 3D shapes and textures of articulated humans from such unstructured image data is a highly
under-constrained problem, as each training instance has adifferent shape and appearance and is observed only oncefrom a particular viewpoint and in a particular pose. Recent

progress in 3D-aware GANs [6, 22, 48] shows impressive
results in learning 3D geometry and appearance of rigid objects from 2D image collections. However, since humans
are highly articulated and have more degrees of freedom tomodel, such methods struggle to generate realistic humans.
By modeling articulation, recent work [4, 47] demonstrates
the feasibility of learning articulated humans from image
collections, allowing the generation of human shapes andimages in desired poses, but only in limited quality and resolution. Recently, EVA3D [23] achieves higher resolution
by representing humans as a composition of multiple parts,
each of which are generated by a small network. However,there is still a noticeable gap between the generated and realhumans in terms of appearance and, in particular, geometry.
Additionally, the compositional design precludes modelingloose clothing that is not associated with a single body part,such as dresses shown in Fig. 5c.
In this paper, we contribute a new method for learning
3D human generation from 2D image collections, whichyields state-of-the-art image and geometry quality and naturally models loose clothing. Instead of representing humans with separate body parts as in EVA3D [23], we adopt
a simple monolithic approach that is able to model the human body as well as loose clothing, while adding multiple
discriminators that increase the fidelity of perceptually important regions and improve geometric details.
Holistic 3D Generation and Deformation: To achieve thegoal of high image quality while flexibly handling loose
clothing, we propose a novel generator design. We model
3D humans holistically in a canonical space using a monolithic 3D generator and an efficient tri-plane representation [6]. An important aspect in attaining high-quality images is to enable fast volume rendering. To this end, we
adapt the efficient articulation module, Fast-SNARF [8], toour generative setting and further accelerate rendering via
empty-space skipping, informed by a coarse human body
prior. Our articulation module is more flexible than priormethods that base deformations of the clothed body on
SMPL [38], enabling it to faithfully model deformations forpoints that are far away from the body.
Modular 2D Discriminators: We further propose multiple
discriminators to improve geometric detail as well as theperceptually-important face region as we found that a single
adversarial loss on rendered images is insufficient to recover
meaningful 3D geometry in such a highly under-constrained
setting. Motivated by the recent success of methods [25, 69]
that exploit monocular normal cues [54, 65] for the task of
3D reconstruction, we explore the utility of normal information for guiding 3D geometry in the generative setting.
More specifically, we discriminate normal maps renderedfrom our generative 3D model against 2D normal maps obtained from off-the-shelf monocular estimators [54] applied

to 2D images of human subjects. We demonstrate that thisadditional normal supervision serves as useful and complementary guidance, significantly improving the quality of thegenerated 3D shapes. Furthermore, we apply separate facediscriminators on both the image and normal branch to encourage more realistic face generation.
We experimentally find that our method outperforms previous 3D- and articulation-aware methods by a large margin
in terms of both geometry and texture quality, quantitatively(Table 1), qualitatively (Fig. 5) and through a perceptual
study (Fig. 4). In summary, we contribute (i) a generativemodel of articulated 3D humans with SotA appearance andgeometry, (ii) a new generator that is efficient and can generate and deform loose clothing, and (iii) several, specialized discriminators that significantly improve visual and geometric fidelity. We will release code and models.

2. Related Work
3D-aware Generative Adversarial Networks: Generative
adversarial networks (GANs) [19] achieve photorealistic
image generation [5, 29–31] and show impressive results on
the task of 2D human image synthesis [2, 18, 20, 33, 34, 55].
However, these 2D methods cannot guarantee 3D consistency [10, 33, 39] and do not provide 3D geometry. Several
methods extend 2D GANs to 3D by combining them with
3D representations, including 3D voxels [44, 64], meshes[36, 58] and point clouds [1, 35]. Recently, many methods represent 3D objects as neural implicit functions [42,46, 51, 61, 68]. Such representations are also used for 3Daware generative image synthesis [6, 7, 22, 45, 48, 56, 57].
StyleSDF [48] replaces density with an SDF for better geometry generation and SotA methods like EG3D [6] introduce a tri-plane representation to improve rendering efficiency. Nevertheless, it is not straightforward to extend
these methods to non-rigid articulated objects such as humans. In this paper, we propose a 3D- and articulationaware generative model for clothed humans.
3D Human Models: Parametric 3D human body models [3, 28, 38, 49, 66] are able to synthesize minimallyclothed human shapes by deforming a template mesh. Extending these mesh models to generate 3D clothing or
clothed humans is challenging [40]. In the case of meshes,
the geometry is restricted to a fixed mesh topology and large
deviations from the template mesh are hard to model. To
overcome this limitation, methods such as SMPLicit [11]and gDNA [9] propose to build a 3D generative model ofclothed humans based on implicit surface representations,either by adding an implicit garment to the SMPL bodyor by learning a multi-subject implicit representation withcorresponding skinning weights. The main problem of all
aforementioned approaches, however, is their reliance on
3D ground truth: their training requires a large number ofcomplete and registered 3D scans of clothed humans in different poses, which are typically acquired using expensive
3D body scanners. Several methods [13, 17, 27, 32, 53, 62,
63, 72] combine NeRF with human priors to enable 3D human reconstruction from multi-view data or even monocular
videos. Nevertheless, their proposed human representations
can only be utilized to represent human avatars for a single
subject, wearing a specific garment.
Recently, some methods have been proposed to learn
generative models of 3D human appearance from a collection of 2D images. ENARF-GAN [47] and GNARF [4]leverage 3D human models to learn a 3D human GAN, butthey still fail to produce high-quality human images. The
concurrent work EVA3D [23] achieves high-resolution human image generation by introducing a compositional partbased human representation. However, none of these methods including other concurrent arXiv papers [26, 67, 71]
are able to generate and deform loose clothing, and theirgeometry typically suffers from noisy artifacts. In contrast,
our method generates both high-quality geometry and appearance of diverse 3D-clothed humans even wearing loose
clothing, with full control over the pose and appearance. We
empirically demonstrate the benefits of our method over the
more complex EVA3D model [23] in Section 4.2. A comparison to the recent arXiv papers [26, 67, 71] is not possible since the models and code have not been released.
3D Shape from 2D Normals: Several methods predict normals from a single image, for general objects [14–16, 24] orclothed humans [54, 65]. These predicted 2D normal cues
can be exploited to guide 3D reconstruction using neuralfield representations. For instance, MonoSDF [69] leverages predicted normals to improve 3D object reconstruction
from sparse views. Similarly, SelfRecon [25] uses a normal reconstruction loss to reconstruct a human avatar froma monocular video. PIFuHD [54] and ICON [65] predict
normal maps as additional input to support single-view 3D
human reconstruction. In this work, we demonstrate thatmonocular 2D normal cues are useful for learning a generative 3D model of articulated objects.

3. Method
Given a large 2D image collection, our goal is to learn agenerative model of diverse 3D human avatars with realistic
appearance and geometry, while enabling control over poseand identity. An overview of our method is shown in Fig. 2.
In this section, we first introduce an efficient andarticulation-aware 3D human generator (Section 3.1) which
generates the appearance and shape in canonical space anduses a deformation module to warp into posed space via
a learned continuous deformation field. Next, we describe
our rendering module that is accelerated by an empty spaceskipping strategy which leverages the SMPL body prior. To

enable fast training, we use a super-resolution module to liftfeature maps to high-resolution images.
We optimize the generator using a combination of adversarial losses (Section 3.2) and an Eikonal loss [21]. Whileprior work uses a single discriminator formulation, we showthat employing several, specialized discriminators improves
visual fidelity. To this end, we define discriminators thatreason at the level of the whole body and locally at the faceregion, respectively. We additionally introduce an adversarial normal loss, which significantly improves the quality of
the generated geometry.

3.1. Holistic 3D Avatar Generator
Canonical Generator: Given a latent vector z ∈ Rnz
and pose parameters p ∈ Rnp , our method first generates
3D human appearance and shape in canonical space (seeFig. 2). Here, we leverage pose conditioning to model posedependent effects. For efficient rendering, the canonicalgenerator builds on the tri-plane representation proposed in
ConvONet [52] and EG3D [6] to model 3D features. These
are then decoded by an MLP to predict the canonical shapeand appearance in 3D space.
We represent geometry using a signed distance field(SDF). Since existing fashion datasets are imbalanced andcontain mostly frontal views, learning correct 3D geometry
from such datasets is difficult. Following [23], we exploit
a human shape prior in the form of canonical SMPL [38].
Specifically, for every query point x in canonical space, we
predict an SDF offset ∆d(x, z, p) from the base shape tomodel details such as hair and clothing. The SDF valued(x, z, p) is then calculated as
d = d(x, z, p) = dSMPL (x) + ∆d(x, z, p),(1)

where dSMPL (x) is the signed distance to the canonical
SMPL surface. Unlike [23], to compute dSMPL (x) efficiently, we represent the SDF as a low-resolution voxel grid(128 × 128 × 32), where the value of every grid point is the(pre-computed) distance to the SMPL mesh. We then querydSMPL (x) by trilinear interpolating the SDF voxel grid.
We also compute normals in canonical space. The normal n at a certain canonical point x is computed as the spatial gradient of the signed distance function at that point:n = ∇x d(x, z, p).

(2)

The canonical appearance is represented by a 3D texture
field c. We also predict features f that are used to guide thesuper-resolution module (described later). We denote theentire mapping from 3D point x, latent vector z and posecondition p to SDF d, normal n, color c and color features
f in canonical space as follows:g : R3 × Rnz × Rnp → R × R3 × R3 × Rnf(x, z, p) 7→ (d, n, c, f ).

(3)

Canonical
Generator

Deformer+
Volume Renderer

Pretrained
NormalPredictor
SR

Canonical
Shape Color Normal

Figure 2: Method Overview. Holistic 3D Human Generation: Given a latent vector z, our method generates human shape dand appearance c in canonical space. In addition, we compute surface normals n via the spatial derivatives of the canonicalshape which is represented as an SDF. These canonical representations are then posed into the target body pose p via a
flexible deformer and then rendered from the target viewpoint. The rendered images are further super-resolved by 2×.
Adversarial Training: We optimize the generator and the super-resolution module using multiple discriminators. In addition
to an image discriminator operating on the images, we improve geometry by introducing a normal discriminator that compares
our rendered normal maps with the normals of real images predicted by an off-the-shelf normal estimator. To further improve
the quality of the perceptually important face region, we add normal and image discriminators for the face region.
Deformer: To enable animation and to learn from posed
images, we require the appearance and 3D shape in posed
space. In the following we denote quantities in posed spaceas (·)′ . Given the bone transformation matrix Bi for jointi ∈ {1, ..., nb }, a canonical point x is transformed into its
deformed version x′ via
′

x =

nb
X

wi Bi x(4)

posed space x′i = o′ + ti v′ and predict their SDF values d′i ,colors c′i , color features fi′ and normals n′i . We convert SDF
values d′i to densities σi′ via the method of StyleSDF [48].
The color of each pixel in the rendered image I is computed via numerical integration [43]:I(r) =

M
X

αi

i=1

Y

(1 − αj )c′i

αi = 1 − exp(σi′ δi )

(6)

i<j

i=1

Here, the canonical LBS weight field w : R3 → Rnb , withx 7→ (w1 , ..., wnb ) and nb the number of joints, weights
the influence of each bone’s Bi transformation onto the deformed location x′ . This weight field is represented by a
low-resolution voxel grid. The normal at the deformed pointx′ is given by
Pnb
( i=1
wi Ri )−T n′
n = Pnb(5)∥( i=1 wi Ri )−T n∥
where Ri is the rotation component of Bi [60].
We leverage Fast-SNARF [8] to efficiently warp points
backwards from posed space x′ to canonical space x via efficient iterative root finding [8]. The SDF value d′ , color c′
and feature f ′ at the deformed point are obtained by evaluating the generator at the corresponding x. In contrast to [8],which focuses on reconstruction tasks and learns skinningweights on the fly, we constrain the notoriously difficult adversarial training by averaging the skinning weights of thenearest vertices on the canonical SMPL mesh.
Volume Renderer: To render a pixel, we follow [43] andcast ray r′ from the camera center o′ along its view direction
v′ . We use two-pass importance sampling of M points inwhere δi is the distance between samples. 3D normals N (r)and feature vectors F (r) are rendered accordingly.
To accelerate rendering and to reduce memory, we takeadvantage of the geometric prior of the SMPL model anddefine the region within a predefined distance threshold to
the SMPL surface as the occupied region. For points sampled outside of this region, we set the density to zero.
Super Resolution: Although the SMPL-guided volume
rendering is more efficient than previous approaches, it isstill slow and requires a large amount of memory to render at high resolution. Therefore, we perform volume rendering at a sufficient resolution (2562 pixels) to guarantee
good rendering of the normal image N and rely on a superresolution module [6] to upsample the image feature map F
and color I to the final image I + of size 5122 pixels.

3.2. Training
We train our model on a large dataset of 2D images using
adversarial training, leveraging a combination of multiple
discriminators and an Eikonal loss.
Image Discriminator: The first discriminator Dimage compares full images generated by our method to real images.

(a) View Control(b) Pose Control(c) Interpolation

Figure 3: Qualitative Results: 3D Human Generation. We generate 3D human appearance and shape, and render the
resulting 3D representations using different body poses and from different viewpoints. In addition, we show virtual people
generated by interpolating between latent codes. Overall, our synthesized humans exhibit reasonable appearance and geometric quality, remain consistent across different poses and views, and smoothly interpolate when varying the latent code z.
Following EG3D [6], we apply the discriminator at bothresolutions: We upsample our low resolution rendering I,
concatenate it with the super-resolved image I + , and feed¯
it to a StyleGANv2 [30] discriminator. For real images I,
we downsample and re-upsample them, and concatenate theresults with the original image as input to the discriminator.
Face Discriminator: We observe that the generated faceregion suffers from artifacts due to the low resolution of
faces within the full-body image. Motivated by 2D humanGANs [20], we add a small face discriminator Dface image .
Based on estimated SMPL head keypoints, we crop the headregions of our high resolution output I + and real data I¯ andfeed them into the discriminator for comparison.
Normal Discriminator: A central goal of our work is toattain geometrically correct 3D avatars. To achieve this,
we propose to use geometric cues present in 2D normal
maps to guide the adversarial learning towards meaningful 3D geometry. To this end, we use an additional normal
discriminator Dnormal . This normal discriminator compares
the predicted 2D normal maps N to 2D normal maps N̄ ofreal images I¯ predicted by the 2D normal estimator from
PIFuHD [54]. Analogously to the image branch, we usean additional discriminator Dface normal to further enhance
the geometric fidelity of the generated faces. We refer thereader to the Sup. Mat. for implementation details.
Eikonal Loss: To regularize the learned SDFs, we apply an
2Eikonal loss [21] Leik = Exi (∥∇(∆d(xi ))∥ − 1) to thecanonical correspondences {xi } of sampled points {x′i }.
Training:

We train our generator and discriminators

EVA3D
Shape
Image
0%

Ours

28.30%

71.70%

18.60%

81.40%
25%

50%

75%

Figure 4: User Preference. We conduct a perceptual study
with approximately 4000 samples and report how often
participants preferred shapes and images generated by our
method or those generated by EVA3D [23].
jointly using the non-saturating GAN objective with R1regularization [41] and an Eikonal loss. Please refer to theSup. Mat. for more training details.

4. Experiments
In our experiments, we first demonstrate the quality ofgenerated samples and then compare our method to other
SotA baselines. In addition, we provide an ablation study toinvestigate the importance of each component in our model.
Datasets:* DeepFashion [37] contains an unstructured collection of fashion images of different subjects wearing various types of clothing. We use the curated subset with 8k images from [23] as our training data. UBCFashion [70] contains 500 sequences of fashion videos with subjects wearing
loose clothing such as skirts. Following EVA3D [23], we
treat these videos as individual images without assuming* Disclaimer: Standard fashion datasets lack diversity, see Section 4.4.

Method
EG3D
StyleSDF
ENARF-GAN
EVA3D
EVA3D (public)
Ours

FID↓
26.38∗
92.40∗
77.03∗
15.91∗
20.45
10.93

DeepFashion
FIDnormal ↓ FIDface ↓
30.81
17.21
20.38
14.79

FID↓
23.95∗
18.52∗
12.61∗
19.81
11.04

UBCFashion
FIDnormal ↓ FIDface ↓
49.29
54.42
18.79
15.83

Table 1: Quantitative Comparison with SotA Methods. We evaluate FID of full images, cropped face images and normal
maps generated by our method and the SotA method EVA3D (public) [23] using their released trained models. For reference,
we also report quantitative results from the EVA3D paper above the separation line marked by *.
temporal information. Pre-processing details can be foundin the Sup. Mat.
Metrics: We measure the diversity and quality of generated
images using the Fréchet Inception Distance (FID) between
50k generated images and available real images, denoted by
FIDimage . To measure the generated face quality, we reportan additional FID specifically for the face region, denotedby FIDface . Furthermore, we evaluate the quality of the synthesized geometry by computing the FID between our rendered normals and pseudo-GT normal maps predicted by[54] (FIDnormal ). We use an inception network [59] pretrained on ImageNet [12] for all FID computation. In addition, we conduct a Perceptual User Study among 50 participants with 4000 samples and report how often participantspreferred a particular method over ours.
Baselines: We compare our method to four baseline methods: EG3D [6], StyleSDF [48], ENARF-GAN [47] andEVA3D [23]. EG3D and StyleSDF are SotA , 3D-aware,
generative models of rigid objects. For comparison, these
two methods are trained on the aforementioned humandatasets. Since these two methods do not model articulation, they have to learn it implicitly. ENARF-GAN and
EVA3D additionally model articulation for 3D human generation. The quantitative FID results of all baseline methods
are directly taken from the experiment in EVA3D [6]. In addition, we evaluate FIDface and FIDnormal on EVA3D basedon their released code and trained model weights.

4.1. Quality of 3D Human Generation
We show our qualitative results in Fig. 3. More results
can be found in the Sup. Mat. Overall, our method generates
realistic human images with faithful details such as clothing
patterns, face and hair, and meaningful 3D geometry evenwith fine structures such as hair and shoe heels. Our method
further enables control over the generation as follows.
View Control: As shown in Fig. 3a, by learning humans
in 3D space, our method can generate 3D-consistent highquality images and geometry from varied viewpoints.

Pose Control: The generated 3D humans can also be reposed into unseen poses as shown in Fig. 3b. The images
and geometry in different poses are consistent due to theexplicit model of human articulation.
Interpolation: Our method learns a smooth latent space of
3D human shape and appearance. As shown in Fig. 3c, ourmethod yields smooth transitions of appearance and shapeeven when interpolating the latent codes of two subjects
with different gender and clothing styles.

4.2. Comparison to SotA
Table 1 summarizes our quantitative comparisons on
both DeepFashion and UBCFashion datasets. Since the
SotA method EVA3D [23] outperforms other baselines bya significant margin, we focus our discussion on the comparison with EVA3D only. More comparisons with otherbaselines can be found in the Sup. Mat.
Image Quality: Our method achieves better quantitative
results than EVA3D in terms of FIDimage and FIDface on
both datasets. This improvement is confirmed by our user
study in Fig. 4. Notably, in 81.4% of the cases, participants consider our generated images to be more realistic
than EVA3D. A qualitative comparison is shown in Fig. 5a.
Our method generates overall sharper images with more details due to the use of a holistic 3D generator, and synthesizes more realistic faces due to our face discriminators.
Our improvements are particularly pronounced when
considering side views. As shown in Fig. 5b, our method
generates sharp and meaningful images also from the side
where EVA3D’s image quality significantly degrades. Thisis a consequence of EVA3D’s pose-guided sampling strategy. As discussed in their paper, EVA3D had to increase
the dataset’s frontal bias during training to achieve reasonable geometry and face quality. We hypothesize that thisrequirement is due to the limited capacity of the lightweightpart models. As a consequence, EVA3D overfits more to
frontal views and generalizes less well. In contrast, our efficient articulation and rendering modules allow us to exploit
a single holistic generator and our face and normal discrim-EVA3D

EVA3D

Ours

(a) Overall Quality

Ours(b) Novel Views

EVA3D

Ours

(c) Loose Clothing

Figure 5: Qualitative Comparison to EVA3D. We show random samples of our method and the SotA method EVA3D [23].
Our method achieves better image and shape quality, degrades more gracefully at side views, and better models loose clothing.

MethodFID ↓FIDnormal ↓FIDface ↓Ours
w/o normal GAN
w/o face GAN

10.93
11.15
11.71

20.38
32.17
23.96

14.79
14.35
20.88

Table 2: Ablation. We compare our method and ablatedbaselines in which we remove individual discriminators.

inators enable us to directly sample from the data distribution which leads to better generalization.
Interestingly, the (non-animatable) generative model
EG3D achieves reasonable FID despite not modeling articulation. This is because the FID evaluation only considers
training poses and views, see also Sup. Mat.
Geometry: Our method yields significantly better geometry compared to EVA3D, as evidenced by the improvementin FIDnormal in Table 1 and the perceptual study in Fig. 4.
Based on our qualitative results, our geometry is more realistic and detailed, in particular on faces. In contrast, noiseand holes can be observed around the shapes generated by
EVA3D, despite their surface representation and regularization. We attribute this improvement to our normal discriminators, which provide strong geometric cues.
Loose Clothing: As shown in Fig. 5c, our method outperforms EVA3D in modeling loose clothing. Due to its compositional nature, EVA3D is prone to generating artifacts
between the legs. In contrast, our holistic representation
generates loose clothing without discontinuity artifacts.

Ours w/o Normal GAN

Ours

Figure 6: Ablation of the Normal Discriminator. Our normal discriminator effectively improves the generated geometry while preserving appearance quality.

Efficiency: Our method is more efficient than EVA3D inrendering images and normal maps with the same image
and ray sampling resolution. For an image resolution of
2562 and with 28 sample points per ray, our method renders
normals and images together at 10.5 FPS while EVA3D runs
at 5.5 FPS. With a 2D super-resolution module, our method
is more than three times faster than EVA3D when rendering
images of 5122 (9.5FPS vs 3FPS), while achieving better
performance in terms of geometry and appearance. Moredetails can be found in Sup. Mat.

istic generation as shown in Fig. 7.

Ours w/o Face GAN

Ours

Figure 7: Ablation of the Face Discriminator. Our facediscriminator effectively improves generated face quality.

EVA3D

Ours w/ SMPL
(nn=1)

Ours w/ SMPL
(nn=4)

Ours(complete)Figure 8: Ablation Study of the Deformer. Results withloose clothing in novel poses, generated by EVA3D and our
method with different choices for the articulation module.

4.3. Ablation Study
Normal Discriminator: Our normal discriminator serves
an important role in improving the realism of the generated
geometry. Comparing our model to ablated versions where
we remove the normal discriminator (w/o normal GAN), we
observe a significant FIDnormal improvement (see Table 2).
As shown by the qualitative results in Fig. 6, the normal
GAN effectively removes holes and noise on the generated
surface, especially on faces, while preserving image quality.
Face Discriminator: As expected, when training without
face discriminators, we observe a large drop in FIDface (seeTable 2). Given that faces are low resolution and hard togenerate, our adversarial loss on faces forces the generator
to focus on this local region and thus achieves a more real-

Deformer: To test the importance of our Fast-SNARF
based deformation module, we compare our model to a
SMPL nearest-neighbor-based deformer (denoted by Ours
w/ SMPL), where points are deformed based on the skinningweights of their K nearest SMPL vertices in posed space.
As shown in Fig. 8, only our method can deform the skirts
without splitting them. This is due to our articulation module being able to derive meaningful deformations for points
far away from the SMPL surface. In contrast, our ablatedbaselines, with different choices of K, suffer from discontinuity artifacts as they only provide meaningful deformation at regions close to the SMPL surface. Similar artifacts
can be observed in EVA3D’s results, which we hypothesize
stem from their part-based model.

4.4. Limitations
Since each training instance is observed only in one pose,
the association of pixels to body parts cannot be uniquelydetermined. Hence, our model sometimes generates wrong
clothing patterns under arms, or at hands close to the torso.
Future work should investigate techniques to guide association, such as 2D correspondence predictions.
Moreover, samples from generative models reflect the biases present in their training data. The 2D image collections that we used for training focus on fashion images andlack diversity in skin tone, body shape, and age. Our work
should be viewed as a methodological proof of concept andcontains no mechanisms to combat these biases. To avoid
biases future research and deployable systems should i) be
trained on more diverse data or ii) use explicit de-biasing.
Further limitations are discussed in the Sup. Mat.

5. Conclusion
In this paper we contribute a new controllable generative
3D human model that is learned from unstructured 2D image collections alone and does not leverage any 3D supervision. Our model synthesizes high-quality 3D avatars withfine geometric details and models loose clothing more naturally than prior work. We achieve this through a new generator design that combines a holistic 3D generator with anefficient and flexible articulation module. Furthermore, weshow that employing several, specialized discriminators that
operate on the different branches (RGB and normals) andregions (fully body and facial region), leads to higher visual
fidelity. We experimentally demonstrate that our methodadvances state-of-the-art in learning 3D human generationfrom 2D image collections in terms of both appearance andgeometry and that it is the first generative model of 3D humans that can handle the deformations of free-flowing andloose garments and long hair.
Acknowledgements: Zijian Dong was supported by theBMWi in the project KI Delta Learning (project number 19A19013O) and the ERC Starting Grant LEGO-3D(850533). Andreas Geiger is a member of the Machine
Learning Cluster of Excellence, funded by the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC number
2064/1 – Project number 390727645. Xu Chen was supported by the Max Planck ETH Center for Learning Systems. This project was supported by the ERC Starting Grant
LEGO-3D (850533), the BMWi project KI Delta Learning(project number 19A19013O) and the DFG EXC number
2064/1 - project number 390727645. We thank Kashyap
Chitta, Katja Schwarz, Takeru Miyato and Seyedmorteza
Sadat for their feedback, and Tsvetelina Alexiadis for her
help with the user study.
Disclosure: MJB has received research gift funds from
Adobe, Intel, Nvidia, Meta/Facebook, and Amazon. MJB
has financial interests in Amazon, Datagen Technologies,
and Meshcapade GmbH. MJB’s research was performed
solely at, and funded solely by, the Max Planck.

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