CGS-GAN: 3D Consistent Gaussian Splatting GANs for High Resolution Human Head Synthesis
Authors: Florian Barthel
Wieland Morgenstern
Paul Hinzer
Anna Hilsmann
Peter Eisert
Venue: NeurIPS 2025 poster ()
Keywords: 3D GANs
3D Gaussian Splatting
CGS-GAN
3D Consistent GAN Synthesis
Generative Models
Primary area: deep_learning
TL;DR: We propose a new 3D Gaussian splatting GAN for 3D consistent rendering at high resolution

Abstract: Recently, 3D GANs based on 3D Gaussian splatting have been proposed for high quality synthesis of human heads. However, existing methods stabilize training and enhance rendering quality from steep viewpoints by conditioning the random latent vector on the current camera position. This compromises 3D consistency, as we observe significant identity changes when re-synthesizing the 3D head with each camera shift. Conversely, fixing the camera to a single viewpoint yields high-quality renderings for that perspective but results in poor performance for novel views. Removing view-conditioning typically destabilizes GAN training, often causing the training to collapse. In response to these challenges, we introduce CGS-GAN, a novel 3D Gaussian Splatting GAN framework that enables stable training and high-quality 3D-consistent synthesis of human heads without relying on view-conditioning. To ensure training stability, we introduce a multi-view regularization technique that enhances generator convergence with minimal computational overhead. Additionally, we adapt the conditional loss used in existing 3D Gaussian splatting GANs and propose a generator architecture designed to not only stabilize training but also facilitate efficient rendering and straightforward scaling, enabling output resolutions up to $2048^2$. To evaluate the capabilities of CGS-GAN, we curate a new dataset derived from FFHQ. This dataset enables very high resolutions, focuses on larger portions of the human head, reduces view-dependent artifacts for improved 3D consistency, and excludes images where subjects are obscured by hands or other objects. As a result, our approach achieves very high rendering quality, supported by competitive FID scores, while ensuring consistent 3D scene generation.

CGS-GAN: 3D Consistent Gaussian Splatting GANs for High Resolution Human Head Synthesis
Florian Barthel Fraunhofer HHI HU Berlin
Wieland Morgenstern Fraunhofer HHI
Paul Hinzer Fraunhofer HHI
Anna Hilsmann Fraunhofer HHI
Peter Eisert Fraunhofer HHI HU Berlin
Figure I: Example renderings 1024 resolution produced by our 3D Consistent Gaussian splatting GAN (CGS-GAN). Unlike prior methods that recompute the scene for each individual view to ensure high quality, our method capable of preserving quality while synthesizing fully 3D consistent scene that can be ported into explicit 3D settings like game engines or VR environments.
Abstract
Recently, 3D GANs based 3D Gaussian splatting have been proposed for high quality synthesis of human heads. However, existing methods stabilize training and enhance rendering quality from steep viewpoints by conditioning the random latent vector on the current camera position. This compromises 3D consistency, observe significant identity changes when re-synthesizing the 3D head with each camera shift. Conversely, fixing the camera to a single viewpoint yields high-quality renderings for that perspective but results in poor performance for novel views. Removing view-cone wow-condiii typically destabilizes GAN training, often causing the training to collapse. In response to these challenges, we intro- duce CGS-GAN
novel 3D Gaussian Splatting GAN framework that enables stable training and high-quality 3D-consistent synthesis of human heads with- out relying on niew-conditioning. To ensure training stability, we introduce a multi-vieu regularization technique that enhances generator convergence with minimal computational overhead. Additionally. we adapt the conditional loss used in existing 3D Gaussian splatting GANs and propose generator architecture designed to not only stabilize training but also facilitate efficient rendering and straightforward scaling, enabling output resolutions up to 2048². To evaluate the capabilities of CGS-GAN dataset enables very high resolutions, focuses on larger portions of the human head, reduces -ieeddeeeddent artifacts for improved 3D consistency, and excludes images where subjects are obscured by hands or other objects. As a result, our ap- proach achieves very high rendering quality, supported by competitive FID scores, while ensuring consistent 3D scene generation. Check our our project page here: https://Eraumoofrrhh. .eiiiigitttubi 10/ so//ggggann
curate new dataset derived from FFHQ. This
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Introduction
Synthesizing high-quality 3D human heads remains a major challenge in computer vision with wide applicability in film and gaming industries. However, this often requires expensive 3D capturing studios or high manual labor. For this reason, there is a high demand for automating and simplifying the synthesis process while maintaining high rendering quality. 3D GANs trained on 2D images with known camera poses have emerged as powerful framework, enabling both generation and manipulation of facial geometry and appearance. Earlier approaches such as 2.5D GANs encode camera viewpoints directly into the generator features, allowing for nuet synthesis but often lacking consistency across viewpoints. 32 6 28 3D-aware GANs based on Neural Radiance Fields 4 [6]] improve view disentanglement by rendering from an intermediate 3D volume, but
3 31 More recently, 3D Gaussian Splatting (3DGS) (1 has been integrated into generative models, providing explicit scene representations with real-time differentiable rendering capabilities. Methods like GSGAN [15] and GGHead (3 demonstrate that high-quality 3D Gaussian head synthesis is feasible within GAN frameworks. However, they rely on view con.condiiioning. where the camera parameters are fused with latent codes to guide synthesis. While this aids training convergence for 3DGS based GANs, it leads to strong view-dependent variations in geometry and identity.
suffer from slow inference and limited resolution scalability [38 7
To address this, we introduce CGS-GAN, novel 3DGS-based generative framework that achieves stable training and 3D-consistent synthesis of human heads without the need for nie-connditionin. Unlike existing methods, CGS-GAN achieves very high rendering quality from any camera pose without re-synthesizing the 3D scene for each view. This is made possible through a lightweight multi-view regularization strategy that stabilizes the training and encourages geometric consistency with minimal computational overhead. To support high-resolution synthesis and scalability, we propose an efficient generator architecture that minimizes GPU memory usage while enabling output resolutions up to 2048?, Additionally, we apply random background augmentation to mitigate hole artifacts. To underline the capabilities of our new GAN framework, curate high-quality dataset derived from FFHQ, including entire heads by recropping from 4K sources, excluding occluded faces, and reduce view-specific biases. Our key contributions are:
We propose a novel 3DGS- GAN framework that synthesizes consistent 3D human heads.
We introduce multi-view regularization that stabilizes training and promotes 3D consistency.
We propose a memory--fficiint generator design supporting scalable, high-resolution synthesis.
We apply random background augmentation method that reduces common artifacts.
We curate high-quality FFHQ-base based dataset for high-fidelity, 3D consistent head modeling.
2
Related Work
Generative adversarial networks (GANs) were first introduced by Ian Goodfellow in 2014 13] Ever since, GANs have received high attention for their real-time photorealistic synthesis capabilities. In its core, GANs are constructed as a non-cooperative game between a generator that synthesizes images from random latent vectors and a discriminator that differentiates between fake images and real images from a training dataset. The training pipeline is differentiable so that the generator can be optimized to produce outputs that the discriminator confuses with real images. As both networks improve during training, the generator converges towards the training data distribution. As synthesizing from random latent vectors creates only limited control, conditional GANs (cGANs) [27] have been introduced as extension to GANs, allowing annotations to be incorporated into the GAN training framework.
Although in recent years diffusion- based approaches have surpassed the rendering quality of GANs for 2D image synthesis or video generation, GANs still stand out for their efficient real-time editing and rendering capabilities and their clear advantage not to require ground-truth data. The latter especially important for 3D synthesis, as good 3D ground-truth data very difficult to obtain. Consequently, current approaches that utilize diffusion based models for synthesizing 3D human heads, either take the 3D ground truth data from a pre-trained 3D GAN 25), or from 2D diffusion models [10), which are very slow to query.
2

---

2.1
3D GANs
While early 3D GANs were based on conditional 2.5D GANs B2 28 or voxel grids 12 36 there have been drastic improvements since the introduction to Neural Radiance Field (NeRF) 4 26 NeRFs provide a differentiable renderer that allows for high quality synthesis, by querying a small Multi-layer Perceptron MIP to decode color and opacity during ray tracing. By designing a generator that directly produces the weights of NeRF (MIP). PI-GAN ( and GRAF [31] were the first works to include NeRF renderer in a 3D GAN framework. Although the rendering quality was promising. these approaches were slow to render and limited in output resolution. Addressing this, EG3D 7 proposes an efficient 3D GAN architecture that introduces lightweight tri-plane representation, which allows using 2D features to describe 3D volume that can be rendered using a very small NeRF MLP. This architecture considerably sped up the training and inference. Nevertheless, EG3D still relied on 2D resolution to render in high resolutions after the 3D rendering, which harms the 3D consistency. To achieve better 3D consistency for 3D GANs, several works [34] [ (37 proposed extensions to EG3D that allow for removing the super-resolution part after the NeRF rendering. This was mainly achieved by moving the super-resolution in front of the NeRF rendering. While this slows down the rendering, we observe considerably better 3D consistency in the rendered 3D heads. Nevertheless, since these methods use NeRF rendering, the generator model always outputs 2D image of the implicit 3D model, making it difficult to use the results in explicit 3D environments.
2.2 3DGS GANs
With the introduction of 3D Gaussian splatting (3DGS) 21], obtained fully explicit method which allows for fast rendering that is qualitatively similar to NeRFs. Ever since, many works have demonstrated very good rendering quality for synthesizing 3D human heads [33 30 11 39]. Often, however, these methods rely on multi-view input data to overfit a single human head. This data is difficult come by, as it either requires expensive multi-camera setup 24 or video of person remaining perfectly still. As this is not required when training a 3D GAN, which learns 3D geometries from 2D data, there is a high demand for 3DGS based GANs. The first method to achieve 3DGS human head synthesis from a latent space is the GSGAN Decoder (5) It directly converts tri-plane features from pre-trained EG3D model into Gaussian splatting scenes, enabling all advantages of GANs (i.e. synthesizing fakes, inverting or editing) to be employed in an explicit 3DGS setting, On the downside, however, the GSGAN Decoder adds computational overhead, loses some detail and always requires pre-trained EG3D model. The second 3DGS GAN method, GGHead [23], was proposed as the first method to train 3D human heads in a GAN training framework. Similarly to EG3D, GGHead first synthesizes 2D features using StyleGAN2 backbone. However, instead of aligning the features in tri- plane and sampling the volume with a NeRF decoder, the output feature maps are used as UV-maps on a fixed template head mesh. Such UV-map is constructed for each of the 3DGS attributes (color, position, scale, opacity, and rotation), where the position UV-map denotes an offset to fixed position on the template mesh. As result, GGHead's s generator produces a 2D grid of Gaussian primitives that are used to model the surface of head model. This method has demonstrated strong performance. producing competitive Fréchet Inception Distance (FID) results while rendering with explicit 3D Gaussians. The third method to achieve Gaussian splatting GAN training is GSGAN [15.. GSGAN follows a different approach to GGHead, as it does not model the surface on a head mesh, but directly predicts the Gaussian splatting scene using transformer models 35 that are specifically designed for point clouds [14 40 29). Similarly to a StyleGAN architecture, GSGAN produces coarse Gaussian primitives in earlier layers, and adds fine-detail Gaussians in subsequent layers. Finally, all Gaussian primitives from all layers are concatenated into a single 3DGS scene that can be rendered and forwarded to the discriminator: With this configuration, GSGAN finds a good balance between large Gaussians that model smooth regions like skin regions, and small Gaussians that model fine details like hair. To create a relationship between the layers, GSGAN builds hierarchical data structure, where smaller Gaussian primitives are attached to larger ones from previous layers. As a result, GSGAN produces good quality 3D heads with competitive FID results, For our method, we will use GSGAN as a basis.
3

---

TranStree: Black
Altigtie Eouan
(312)
5
Inadormet
Es Aragane
ToCaum
BoAaden
Maping
Adatooe Taastane Blork
ToClus
TrAnder
makes hackgaaa
Salf Aguriee
Adatize Trasderner Bick
Inloo
Vije
Tanifatsss Blnds
ToCle
Mdptiie Tranderrur Blldk
ToCaes
TrAadaor
Adaptiee
SlyleGANZ Discrininator
win
rel Skx
Datavol
(00) 9011
Figure 2: Overview of the proposed CGS-GAN framework. The generator is built on the generator of GSGAN [15] with key modifications for improved 3D consistency and scalability. contrast to view-conditioned approaches, our method omits camera labels in the mapping network and instead stabilizes training through efficient multi-view rendering the 3DGS head during each training step. Additionally, we render with random backgrounds to reduce hole artifacts.
3 Method
In this section, we present the core components of CGS-GAN, our novel framework for high- fidelity, 3DDcconisteett synthesis of human heads using 3D Gaussian Splatting. Our method is designed to achieve stable training without relying on view-conditioning, while enabling efficient and scalable rendering. We first introduce our tailored generator architecture, which supports efficient high quality and high resolution rendering. We then describe our multi-view regularization that enforces consistency across viewpoints during training. In addition, we propose a modified loss formulation adapted to our unconditional setting and introduce a background augmentation technique that mitigates hole artifacts and improves the geometric quality of the resulting 3D scenes.
3.1 Generator Architecture
Our generator architecture is designed to enable high-resolution, 3D-consistent head synthesis while maintaining efficiency and scalability. We use hierarchical generator structure similar to GSGAN [5]. We first create constant learnable feature point cloud of size (512 x 3) that is randomly initialized and forwarded to positional encoding function that adds higher frequeencies in NeRF
26]. resulting size (512 x 512). The encoded representation is then processed by a series of Adaptive Transformer Blocks from GSGAN, where each block is composed of an Adaptive Instance Normalization Layer (AdalN), a Self Attention Layer, and a small Multi-Layer Perceptron (MLP). As shown in Figure 2 (right), the AdaIN layer normalizes the input features w.r.t. mean and standard deviation and applies a learned offset and scale, derived from a mapped latent vector w E W. Intuitively, this operation applies the style of latent vector the learned feature point cloud representation. This idea originates from the well established StyleGAN 19 framework, known for its state-of-the-art art rendering capabilities. The subsequent Self Attention layer enables the model to establish meaningful correlation between the points. Finally, we apply small MLP to create additional capacity [35]. From the resulting feature points, we decode 3DGS scene by forwarding the points through fully connected layers with respective output channels for each attribute (position: 3, color: 3, scale: 3, rotation: 4 and opacity: 1) and apply tanh clip the output to reasonable range, while keeping differentiabiliity This is done for Gaussians that are rendered and Gaussians that are used as anchors or offsets for subsequent layers. To create the next layer of Gaussians, we again apply adaptive transformer block to the feature point cloud of the previous layer and convert the result to a 3DGS scene that is offset and orientated by the anchors of the prior layer. This time, apply point-upsampling to create four times more Gaussians than in previous layer.

---

Notably, unlike GSGAN, we do not apply point upsampling within the backbone network. Instead, we increase the upsampling ratio for each subsequent layer. This is very important aspect when it comes to scaling the generator for larger resolutions. Since, with our generator the number of points in the backbone fixed, we can flexibly add additional layers to our generator, while scaling linearly in complexity. This is not the case for GSGAN, which performs point upsampling withing the backbone. By doing so, GSGAN cannot apply a transformer block to the second (or higher) layer, as the memory consumption would be too high. Instead, GSGAN applies graph convolution layers between neighboring Gaussians, which are very inefficient, as they require sorting distances between all points. As a result, our proposed generator requires substantially less GPU memory while being considerably faster. To scale our generator for higher resolutions, we add further transformer layers and double the spsamlig factor. With this configuration, we end up with the following number of Gaussian primitives for the corresponding training resolutions: (2562: 109k, 512?: 240k, 10242: 502k, 20482: IM).
3.2 Multi-view Regularization
Training instability is one of the main problems when working with GANs. Usually, the generator cannot keep up with the performance of the discriminator, which causes the training to collapse. For 3D synthesis, this instability is amplified, since the generators job of constructing 3D head is much more complex than the discriminators job of evaluating the realness of provided 2D images.
To encounter this training instability, we introduce multi-view regularization that renders the generated 3D heads from multiple views per training step and averages the resulting gradients before backpropagation. This brings two advantages. Firstly, as we average the gradient on larger sample size, the direction of the gradient is less affected by stochastic variation, improving general convergence. And secondly, we collect gradients that do not push the weights towards one specific view per latent vector, but instead evenly balance the optimization across multiple views the same time. This is especially important for 3DGS, given that the positional gradient is very sensitive to small changes [5] 23 15] Usually, when using NeRe- GANs, like EG3D 77, such an operation would be very inefficient, as the rendering with NeRF is a huge bottleneck compared to the feature synthesis part. With the efficient 3D Gaussian splatting rasterizer, we have the opposite case, where the rendering >200 FPS is considerably faster than the backbone synthesis. As a result, our multi-view regularization only creates neglectable overhead to the overall training time.
we render the same scene from multiple views per training step,
As the discriminator is equipped with a Minibatch-Stanndee Standart-Deviationn layer that collects statistics across the batch dimension, we have to be careful not alter the batch statistics in any way compared to the real batches. For instance, we cannot provide the discriminator with a batch showing four times the same person from different angles, given that no person appears even twice in the real training data, Instead, if we apply the multi-view regularizations four times, we create four new batches where each person is still only shown once per batch.
3.3 Conditional Loss
In order to learn 3D geometry from 2D data, the discriminator is provided with the annotated camera position to learn which camera position belongs to which appearance. Once the discriminator has learned the 3D concepts from real data, it provides meaningful feedback for the generator how to mimic the 3D geometry. However, the way in which this feedback is created can differ quite a lot. GSGAN for instance, applies contrastive loss function that pulls together matching camera / image pairs, while pushing non-matching pairs apart. Although this approach has proven to work, we observe that it destabilizes the training after several million training iterations. This caused by the large training set containing quite a lot of camera / image pairs that are very similar to each other. If we now sample a batch of images and cameras that have very similar camera positions, the discriminator will produce high correlation between those non-matching pairs. Although it seems to be not bad if the discriminator confuses two very similar camera labels, it will result in very high loss terms when applying the contrastive loss. Therefore, we argue that the contrastive loss formulation is not ideal for such an optimization problem and dataset. Instead, we use the conditional training mechanism from StyleGAN [19] Specifically, we do not directly apply loss on the camera parameters itself but embed the camera parameters into the image features of the discriminator, which are then used to predict whether the provided image is real or fake. This conditioning, without an
5

---

Side
Side Cuoditscing
Side
Conditoonng Frent Conditiniis
Cooditocing Side
Conlizioing Front
Canlitionig Conditioing Side
Coulitioing
Figure 3: Visualizations how GGHead (left) and GSGAN (right) rely on view-conditioning to achieve good quality renderings from any angle. As soon the view-conditioning does not align with the camera pose, we observe worse quality. Additionally, we use a fixed latent vector for all four images, underlining that view-conditioning also changes the identity and expression.
explicit conditional loss function, is well established for 2D GANs as well as 3D GANs 7 While removing the contrastive loss, we keep both regularizations from GSGAN, which penalizes Gaussian primitives that move too far from the center and enforces the Gaussians to cluster by calculating KNN distance between the anchors on the lowest level.
3.4 3D Consistency
While in theory, GSGAN [15 and GGHead (23) produce 3D consistent human heads, they both make use of camera conditioning, where they input the camera viewpoint to the mapping network of the generator. Specifically, during training they forward in 50% of the time the camera information the mapping network the generator. Although this was designed to give the generator the ability to deal observe
with view-dependent biases in the dataset and therefore improve training convergence, that the resulting generators heavily rely on this conditioning during inference as they produce poor quality renderings when conditioning the generator to frontal view, while rendering from the side Figure 3, Removing this view nrew-conditioning typically destabilizes the training, even causing it to cases. "The necessity for such fricks is one of the main disadvantages of 3D GANs
collapse in and shows that we still need more research to find better 3D generative modeling paradigms [ As our training is now much more stable with our improved generator architecture, the multi-view regularization and the removed contrastive loss, we are able to fully remove the camera conditioning from the training pipeline without harming training convergence. By doing so, the generator learns to synthesize a consistent 3D head that can be rendered from any viewpoint at high quality.
3.5 Random Background
Like GGHead, we remove the background in the training dataset using the background matting network, MODNet (20). GGHead then replaces the background with white background. As result, we observe that the generator sometimes exploits the white background color and uses it to produce white regions, such as reflections, by creating holes in the surface. To remove this artifact, the white background with a random colored background that changes for each new image sample. This way, the generator cannot rely on any color from the background, enforcing it to produce the correct color that is required for rendering the human head itself.
replace
4 Dataset
When training with in-the-wild images from FFHQ, we observe rendering artifacts that include large undefined floating objects in front of or next the 3D head. Most often, these objects look like human hands, microphones, or additional human heads. They originate from the training data that includes several people with their hand covering the face, while drinking, smoking holding a microphone. As the generator is enforced to replicate the same data distribution provided in the training data, it has to mimic the occluded training examples well. This is problem for several reasons: Firstly, these occluders are frequent enough in the training data to force the generator to reproduce them, however, they are not frequent enough to give the generator enough information to render them realistically. As a result, they appear in many synthetic images and in poor quality. Secondly, to avoid such artifacts during inference, often the truncation trick applied. The truncation trick moves

---

FFIQQ Images
Fittering -15k Images
Recropping
Background Masking
Smile Rebalence (12k Images)
Pose Rebelance Images)
with resutanding
Iotooaal decting 1eg3
Figure 4: Our data processing pipeline: 1. remove images with occluders enhance training, 2. recrop the entire head at 2048² resolution, 3. apply background masking, 4. rebalance the smiling bias, and finally 5. rebalance the camera positions.
the randomly sampled latent vector towards the mean vector, which typically synthesizes faces with higher quality and fewer occluders but trade image quality with image diversity. Thirdly, rendering the occluders in addition to the 3D head uses capacity of the generator that could be spend on better face details. This especially applies for 3DGS GANs that use fixed number of Gaussian primitives to construct the 3D scene. For all those reasons, we argue that the overall quality drastically benefits from removing these occluders from the training data. Even though this removes some variation, we argue that effectively more variation is kept, as we no longer have to apply a strong truncation to avoid unwanted artifacts. total we remove 15k images which is about 20% of the training data. To train VGG-19 19 on small training set curated
identify the images where occluders are present, with PicArrange [16), an application that allows querying large datasets with CLIP prompts, such as: a person drinking'. As a result, we propose new subset of FFHQ, FFHQ-Cleae (FFHQC) that only keeps training images without occluders.
We further observe that 3D consistency is slightly harmed when training our generator with the default distribution of FFHQ, since FFHQ has strong bias for people smiling when looking into the camera. Since our model has to replicate this effect without being equipped with view-dependent components, starts to align very thin Gaussian primitives in a way that a person smiles only when viewed from the front, harming the perceived 3D consistency. To avoid this, we rebalance the dataset by adding 12k images of persons viewed from the front who do not smile. Furthermore, we apply rebalancing for the camera view to improve the synthesis quality for side views, by duplicating 50k images from extreme poses. The full data processing pipeline in visualized in Figurge 4
5 Experiments
We compare our method with two existing Gaussian splatting based 3D GANs for human head synthesis, GGHead 23 and GSGAN [15]. For GSGAN, we remove the background synthesis. We first train all methods with the default FFHQ dataset with removed background, and then apply our new dataset, FFHQC. A detailed training configuration can be found in the appendix. As we specifically set our focus on explicit 3D human head synthesis, we do not compare our results to implicit NeRF-based 3D aware-GANs. Such comparisons can be found in 23 and [15]
To evaluate and compare our results with prior methods, we use the Fréchet Inception Distance (FID) that compares the feature statistics, created with the pre-trained Inception V3 network, of 50K rendered images to the feature statisties of the training data. Although the FID a well established method for calculating the image quality, it does not account for 3D consistency, as it only generates a single image per 3D head. This highly favors those methods that render very good quality for one specific view, but render poorly from any other view. In order to properly measure the quality of the synthesized 3D model, we propose a modified FID metric, FID that measures the quality of the 3D faces without exploiting fiew-conditioning Specifically, we condition prior methods on the frontal view, given that provides the best result for most views, but render from a randomly sampled view from the dataset. For our method without conditioning FID and FID equal.
7

---

Table I: FID (50kFull) results where the latent vector is conditioned on the current viewing angle (left table) and 3D results where we use fixed frontal conditioning for all images (right table).
FID
FFHQ
FFHQ Clean
FID
FFHQ
FFHQ Clean
512
512
1024
2048
512
512
1024
2048
GSGAN
5.02
5.17
GSGAN GGHead
10.50
7.68
GGHead
4.34
5.37
9.91
7.90
7.78
14.27
Ours
4.94
4.53
5.25
7.8
Ours
4.94
4.53
5.25
7.8
5.1
Quantitative Results
In Table compare the FID and FID results prior Gaussian Splatting based 3D GAN methods. For the standard FID, that allows for view-dependent rendering, our model achieves comparable scores to GGHead and GSGAN, with GGHeed achieving the best score of 4.34 for FFHQ. For our proposed dataset FFHQC, however, our model performs better than both prior methods. Here, GGHead performs worst, because of the template mesh that is optimized for frontal heads. As FFHQC includes full heads and also more views from the side, the provided template mesh might not be suitable anymore.
For the FIDaD results, we observe significantly worse quality for GGHead and GSGAN. This is because, the side views have poor quality, restrict the models to produce consistent scenes conditioned on the frontal view. For resolutions of 10242 and 2048² our model produces low values, indicating good scaling capabilities and robust training convergence.
Culltin Sale Caulian Curees
GGHead
Disiitin Carnet
GSGAN
Cleiition Sale
FEIR
Onrs
Figure 5: A comparison between GGHead, GSGAN and our proposed method. For prior methods we apply conditioning on the current view (first rows), resulting in good but inconsistent identities, and apply conditioning on a left side view (bottom rows), resulting in a consistent scene with poor quality for novel views. In contrast, our method creates high quality renderings for the whole rotation for FFHQ and FFHQC (last row).
5.2 Qualitative Results
shows example renderings of our method compared to prior methods. It illustrates how niw-coonitiooiing helps synthesizing good renderings for steep angles however the cost of change in person identity. For GGHead and GSGAN, observe changes in hair color, expression
Figure 5

---

Table 2: Performance comparison between 3DGS GANs. Synthesis speed is measured on a Nvidia
4090. Training speed and GPU memory is measured using Nvidia H100 with a batch size of
Synthesis Speed
Training GPU Memory
Training Speed
GSGAN 512
35.0 FPS
37 GB
43 sec kimg
GGHead @512
167.7 FPS
15 GB
16 sec / Kimg
GGHead 1024
151.7 FPS
36 GB
27 sec kimg
Ours @ 512
152.5 FPS
15 GB
25 sec kimg
Ours @ 1024
110.7 FPS
19 GB
64 sec / kimg
Ours @ 2048
61.3 FPS
38 GB
157 see kimg
and glasses when moving the camera without altering the latent vector. Alternatively, if we condition a fixed latent vector, this case the left most image highlighted in red, we observe good quality for that view, but worse quality the further we rotate. Both problems are no longer present with our proposed method that removes the new-conniiioniigg Our method produces high quality renderings from any viewpoint, producing single fixed 3DGS scene. Results for 1024² training are shown in Figure
We provide more qualitative results in the appendix.
5.3 Performance and Memory
In Table 2. we compare the synthesis speed, GPU memory usage and training speed among the 3DGS GAN methods. Instead of measuring the rendering speed of the resulting 3DGS scene, which is very fast for all methods (> 200 FPS) leveraging the efficient 3DSG rasterizer, time for synthesizing novel faces. Le., the time it takes between forwarding a random latent vector and rendering the resulting 3D scene. With GGHead being the overall fastest with 168 FPS, our method achieves comparable results with 153 FPS. GSGAN compares as the slowest, which is due to expensive graph convolution layers. Similar results are observed in training GPU memory, where method and GGHead require more than half of the memory of GSGAN. For higher resolutions, our model still requires comparably little GPU memory.
measure the
Although one training step of our method is slower than for GGHead, our method converges much faster. While GGHead trains for 25M iterations, taking about 4.5 days, our method already converges in less than 10M steps only taking less than 3 days.
5.4 Multi-view Regularization
Training our model without the multi-view regularization is significantly less stable, causing it to collapse after about 8M training steps. Further, as we do not generate new 3D scenes during multi- view regularization, the only computational overhead is the efficient 3DGS rendering and additional forward passes through the 2D discriminator. While generating 4x more images during training, the overall duration is only extended by 20%. We found rendering 4 views per training step good setting, as we do not improve using and perform worse using 2.
5.5 Random Background Augmentation
In Figure 6) (right), we demonstrate the effectiveness of our random background augmentation. Without random backgrounds, the model overfits the white background causing to create holes instead of creating white Gaussian primitives. As our method can no longer rely on a single background color, produces every color, required for rendering the 3D head, with Gaussian primitive.
5.6 Dataset Comparison
Rendering images with our proposed dataset, FFHQC, shows improve the overall quality. Not only are we able to synthesize the full head (Figure 5) bottom) at high resolutions, we also observe better quality when rendering steep angles as shown in Figure (top). This caused by our camera rebalancing strategy, enforcing steeper angles to be shown more frequently. Furthermore, as we filter 15k occluded face images, we observe almost exclusively good quality 3D human heads, without rendering artifacts. Only in rare cases, we still observe some floating objects next to the face. This is

---

likely due to the background matting network, which sometimes confuses background and foreground. Next removed occluders, we observe that the smiling rebalancing resolved a problem where some rendered faces were smiling when shown from frontal view, while looking neutral from a side view as shown in Figure (left).
FFHQ Cropping and w/o pose rebalancing
Our cropping and pose rebalancing
Trnined with white backgrounds
Trnined with random backgmunds
rebalancing
rebalancing
Figure 6: Top: Improved side views by training with entire heads and rebalancing the camera pose. Bottom left: Renderings w/ and w/o smiling bias. Bottom right: Training w/ and w/o random backgrounds.
Conclusion & Outlook
We propose novel Gaussian splatting GAN framework, CGS-GAN, that is capable of efficiently synthesizing 3D consistent human heads at high-quality and high resolutions of up to 2048², While prior work suffers from either rendering inconsistent persons or producing 3D heads that show poor quality for some viewpoints, our method achieves very good quality from any viewing angle, while producing fixed 3DGS scene that can be ported into explicit 3D environments. This allows for applications such as inverting 3D head from 2D photograph and directly importing it into a game engine. This potentially saves time and resources, as no high-end 3D capturing studio is required. straight forward extension to this work could be the inclusion of further training samples showing the back of the head, like in 3. allowing for full 360 head synthesis. Furthermore, even though our method already trains very fast, requiring less iterations while possessing an efficient forward pass, we could apply optimized attention implementations reduce the training time even further, making development of novel 3D GAN methods easier and more accessible. Finally, combining method with 3D morphable models could turn our static human heads into animatable talking 3D head avatars.
Acknowledgments and Disclosure of Funding
This work has partly been funded by the German Research Foundation (project 3DIL, grant no. 502864329), the European Commission (Horizon Europe project Luminous, grant no. 101 135724) as well as the Fraunhofer Society in the Max
collaboration project NeuroHum.
References
[1] R. Abdal, Yifan, 7. Shi, Y. Xu, R. Po, Z. Kuang, Q. Chen, D.-Y. Yeung, and G. Wetzstein. Gaussian shell maps for efficient 3d human generation. In Proceedings of the IEFF/CVF Conference on Computer Vision and Pattern Recognition, pages 9441-9451, 2024.
[2] R. Abdal, P. Zhu, N. J. Mitra, and P. Wonka. Styleflow: Atribute-conditione exploration of stylegan- generated images using conditional continuous normalizing flows. ACM Trans. Graph., 40(3), May 2021.
10

---

[3] S. An. H. Xu, Y. Shi, G. Song, U. Y. Ogras, and L. Luo. Panohead: Geometry-awe awwee 3d full-head synthesis in 360deg. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 20950-20959, 2023.
[4] J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. multiscale representation for anti anti-aliasing neural radiance fields. ICCV, 2021.
and P. P. Srinivasan. Mip-nerf:
[5] F. Barthel, A. Beckmann, W. Morgenstem, A. Hilsmann, and Eisert. Gaussian splatting decoder for 3d-aware generative adversarial networks. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 0972, 2024.
[6] S. Brehm. Barthel, and R. Lienhart. Controlling 3D objects in image synthesis. Comput. Sci..
4(1), Nov. 2022.
[7] E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. De Mello, O. Gallo, L. J. Guibas, J. Tremblay,
S. Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In Proceedings of the IEEE/CVF conference on compider vision and pattern recognition, pages 16123-16133 2022.
[8] E. R. Chan, M. Monteiro, P. Kellnhofer, J. Wu, and G. Wetzstein. pi-gam: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages
2021.
[9] X. Chen, Y. Deng, and B. Wang, Mimic3d: Thriving 3d-aware gans via 3d-to-2d imitation. In 2023 JEEE/CVF International Conference on Computer Vision (ICCV), pages 2338-2348. IEEE Computer Society, 2023.
[10] X. Chen, M. Mihajlovic, S. Wang. S. Prokudin, and S. Tang. Morphable ditfusion: 3d-consistent diffusion for single-image avatar creation. 2024.
[11] X. Chu and T. Harada. Generalizable and animatable gaussian head avatar. Advances in Neural Information Processing Systems, 37:57642-57670, 2024.
[12] M. Gadelha, S. Maji, and R. Wang. 3d shape induction from 2d views of multiple objects. In 2017 International Conference on 3D Vision (3DV), pages 402-411, 2017.
[13] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, Ozair, Courville, and
Y. Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014.
[14] M.-H. Guo, J.-X. Cai, Z.NN. Liu, T.-J. Mu, R. R. Martin, and S.-M. Hu. Pet: Point cloud transformer. Computational visual media, 7:187-199, 2021.
[15] S. Hyun and J.PP. Heo. GSGAN: Adversarial learning for hierarchical generation of 3d gaussian splats, In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.
[16] K. Jung, K. U. Barthel, N. Hezel, and Schall. Picarraneeviisually sort, search, and explore private images on mac computer. International Conference on Multimedia Modeling, pages 452-457. Springer, 2022.
[17] T. Karras, T. Aila, S. Laine, and Lehtinen. Progressive growing gans for improved quality, stability, and variation. arXiv preprint arXiv: 10196, 2017.
[18] T. Karras, S. Laine, and T. Aila. A style-hased generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401-4410, 2019.
[19] T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila. Analyzing and improving the image quality of StyleGAN. In Proc. CVPR, 2020.
[20] 7. Ke, J. Sun, K. Li, Q. Yan, and R. W. Lau. Modnet: Real-time trimap-free portrait matting via objective decomposition. In AAAI, 2022.
[21] B. Kerbl, G. Kopanas, T. Leimküthler, and G. Drettakis. 3d gaussian splatting for real-tine radiance field
rendering. ACM Transactions Graphics, 42(4), July 2023.
[22] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. CoRR, abs/ 412.6980, 2014.
[23] T. Kirschstein, S. Giebenhain, J. Tang. M. Georgopoulos, and M. NieBner. GGHead: Fast and Generalizable
3D Gaussian Heads. In SIGGRAPH Asia 2024 Conference Papers, SA '24, New York, NY, USA, 2024. Association for Computing Machinery.
11

---

[24] T. Kirschstein, S. Qian. S. Giebenhain, T. Walter, and M. Nießner. Nersemble: Multi-view radiance field reconstruction of human heads, ACM Trans. Graph., 42(4), jul 2023.
[25]
Y. Lan, F. Tan, D. Qiu, Q. Xu, K. Genova, Z. Huang. S. Fanello, R. Pandey, T. Funkhouser, C. C. Loy, and Y. Zhang. Gaufsian3diff: 3d gaussian diffusion for 3d full head synthesis and editing. In ECCV, 2024.
[26] B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng. Nerf: Representing scenes neural radiance fields for view synthesis. In ECCV, 2020.
[27] M. Mirza and S. Osindero. Conditional generative adversarial nets. CoRR, abs/141 1.1784, 2014.
[28] T. Nguyen-Phuoe, C. Li, L. Theis, C. Richardt, and Y.-.. Yang. Hologan: Unsupervised learning of 3d representations from natural images. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7588-7597, 2019.
[29] A. Nichol, H. Jun, P. Dhariwal, P. Mishkin, and M. Chen, Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint wrXiv22212.0875.1 2022.
[30] S. Qian, T. Kirschstein, I.. Schoneveld, D. Davoli, S. Giebenhain, and M. Nießner. Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pages 20299-20309, 2024.
[31] K. Schwarz, Y. Liao, M. Niemeyer, and A. Geiger. Graf:
Generative radiance fields for 3d-aware image synthesis. H. Larochelle, Ranzato, R. Hadsell, M. Balcan, and Lin, editors, Advances Neural Information Processing Systems, volume 33, pages 20154-20166. Curran Associates, Inc., 2020.
[32] A. Shoshan, N. Bhonker, Kviatkovsky, G. Medioni. Gan-control: Explicitly controllable gans. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 14083-14093,
October 2021.
[33] J. T.Tang, D. Davoli, T. Kirschstein, I.. Schoneveld, and M. Niessner. Gaf: Gaussian avatar reconstruction from monocular videos via multi-view diffusion. arXiv preprint arXiv:2412.10200 2024.
[34] A. Trevithick, M. Chan, T. Takikawa, U. Iqbal, S. De Mello, M. Chandraker, R. Ramamoorthi, and K. Nagano. What you see is what you gan: Rendering every pixel for high-fidelity geometry in 3d gans. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 22765 22775, 2024.
[35] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. Attention you need. Advances in neurul information processing systems, 30, 2017.
[36] J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum. Learning a probabilistic latent space of object shapes 3d aeversaive modeling. In Lee, M. Sugiyama, Luxburg, Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 29, Curran Associates, Inc., 2016.
[37] Xiang. J. Yang. Y. Deng, and X. Tong. Gram-hd 3d-consistent image generation at high resolution with generative radiance manifolds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages
2023.
[38] Y. Xue, Y. Li, K. K. Singh, and Y. Lec. Giraffe hd: A high-resolution 3d-aware generative model. In Proceedings the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18440- 18449, 2022.
[39] J. Zhang, Z. Wu, Z. Liang, Y. Gong, D. Hu, Y. Yao. X. Cao, and H. Zhu. Fate: Full-head gaussian avatar with textural editing from monocular video. arXiv preprint
2024.
[40] H. Zhao, L. Jiang. J. Jia, P. H. Torr, and V. Koltun. Point transformer. In Proceedings the IEEE/CVF international conference on computer vision, pages 16259-16268, 2021.
12

---

A Technical Appendices and Supplementary Material
In the following sections, we give a more training details of our GAN framework A.1 provide further information about the components of the generator [A.2 compare our generator architecture to GSGAN A..3 discuss the limitations and broader impacts of this work A.4 make further comparisons to prior NeRF-based approaches A.5 provide further ablations for the multi-view regularization A.6 render further results A.II and show some failure cases with possible solutions for future work A.7 Additionally, we give more details about the contrastive loss function used in GSGAN and show how it destabilizes the training give more details about the dataset filtering process A.9 and, finally, show that the resulting scenes can be used in explicit environments such as Unity A.10 More interactive results can be viewed on the project page that we include in the supplementary materials.
A.1 Training Details
Given that GAN training can be very difficult to set up, lot of works, including this one, base their implementation on an already well functioning GAN frameworks. While there are often significant changes for the generator architecture, we observer very similar hyperparameter settings, adversarial loss functions and regularization. Chronologically, there has been the Progressive Growing of GANs [17, then StyleGAN [18] and StyleGAN2 [19], and specifically for 3D rendering EG3D M.. Those works all build upon the prior one, thus inheriting most training setting. The same goes for GSGAN [1]. which we use as a starting point for our work, that applies most settings from EG3D.
As result, our training settings are closely orientated to all prior methods. Specifically, this includes an Adam optimizer [22) with a learning rate of 0.0025 for the generator and 0.002 for the discriminator and a RI regularization of 1.0, which penalizes the discriminator if it creates too large gradients for real images. At the beginning of the training, we blur the real and fake images for a period of 200k training iterations, to enable smooth training convergence. We set the batch size to 32, which is collated of batches of size for each of the GPUs. During training, images to identify the best checkpoint. Further, we average the generator weights in an Exponential Moving Average EMA generator, which produces better results than the training generator. For our 2D discriminator, we use the StyleGAN2 discriminator from 19
track the FID with 20k fake
Different to GSGAN, which represents the 3DGS scene in a 3D cube with side length of 2 (-1 to 1), we go back to the EG3D representation, using cube with side legth of (-0.5 to 0.5). GSGAN, also modifies the field of view of the rendering camera from 12 to 22", given that it uses much larger scene. We, however, also revert this setting to the original 12" used in EG3D.
A.2 GAN Architecture
In Figure 7
we provide an overview of the point upsampling layer and the toGauss / toAnchor layer that we use in our generator architecture. The point upsampling is constructed of repeat branch that simply duplicates the points four times and a learnable branch that produces four times more features and then reshapes the features into the point dimension. Intuitively, each point is the offspring to four
ToGauss / ToAnchor
iC: 512,
xyz
Point Upsample
PC (as ou1,
> color
(a. 512)
repeat x
(4a,
migg 513,
scale
FC (in-c, oul-4e)
reshapp
(40. c)
PC ti 512, c01 4)
rotation
(4n.
IC Be 111
opacity
Figure 7: Overview of the point upsampling layer (left) and the toGauss/ toAnchor layer (right) used in our generator.
13

---

(Qurs)
GSGAN
Pooiicos Esocity
Rochooal
512,300
can
2
Breain
(26,
Mapping
Magping
Mapiin Inasiornc Block
ArAdgtire
Treaifmne Black
Silf teardis
TrClass
HL
Adgtir
Hor Sparch
Ada Guglh Coes
TeAnchan
012.312
Pee Uppempl
Tixadder
Trasforee Booc
Adgetire
Tistnderr
912.912
Gugh Oaey
Inaafornr Block
uld
Teclam
Heee Uppemple VI
Tikacher
Adgeiie Trandhmec Block
Tivadder
Guph
(40 1301
TrClass
Figure 8: Comparison between our generator (left) and the generator of GSGAN (right).
new points with slightly changed features each. The toGauss / toAnchor is simply constructed of fully connected layers that map each high dimensional 512D point into 3DGS feature. Additionally also apply a weak tanh activation between -20 and 20. Otherwise, features such as rotations infinitely rise values of 1000. For the rendered scene, this makes no difference since the magnitude of the quaternion is neglected, however, for achieving stable gradients it can make a big difference.
A.3
Comparison GSGAN
compare generator architecture (left) to the architecture of GSGAN (right). The core differences are the removed upsampling layers in the backbone network, the removed graph convolution components, and the removed background generator.
In Figure 7
Instead of upsampling the feature points in the backbone network, which causes quadratic memory consumption for each supsequent layer, our backbone maintains fixed number of feature points and performs scale our network for very large number of Gaussians, without requiring lot of GPU memory.
Upsampling:
upsampling for each output layer individually. This way, we can
Since the memory consumption is too high using attention layers with large inputs, GSGAN applies graph convolution layers after the first block in the backbone. This however, slows down the forward pass, as graph convolution layers require expensive neighborhood search algorithms like KNN. As we do not upsample the points in the backbone, we can instead use efficient attention layers for all following layers.
Graph Convolution:
To render the appearance of the background, GSGAN uses a scaled down version of the main generator, produce points sphere, which are added in the background. As we remove the background in our training data, we also remove the background generator for our architecture.
Background Generator:
A.4 Limitations & Broader Impacts
Although our proposed model is capable of synthesizing high-fidelity renderings showing the full head, we still cannot render the back of the head, as such images are not provided in FFHQ. Including
14

---

Table 3: Further comparisons to EG3D, GSM and Mimic3D (F1)@512).
Table 4: Comparison between different num-
ber of views during multi-view regularization.
Method
FID
Num Views
FID
EG3D 77
3.28
w/o Milti-view
6,44
Mimic3D 9
4.27
Multi-view
6,13
GSM 1
28.19
Multi-view 4
4,90
Ours
4.94
Multi-view 4,96
additional images showing the back of the head like in [3. would likely achieve even better results. Further, even after removing 15k images showing occluders, we still observe some artifacts being present in some images. This needs further attention, either by filtering more images improving the background matting network that sometimes fails to differentiate hats from backgrounds masks multiple persons in an image. Additionally, our high resolution methods are still slow compute, making further development in this field not accessible for many smaller research groups with fewer computational capacity. Therefore, we are aiming to apply more efficient CUDA implementations to reduce the training time even further. Further, we provide our training checkpoints for all resolutions, together with the optimizer momentum values, allowing to finetune our models without the necessity of re-training the full pipeline from scratch.
Building automatic tools for synthesizing realistic human heads is both scientific advancement with many applications in the industry while also a dangerous tool that can be misused for producing fraud information. This an ongoing and complex problem that needs more attention. Furthermore, FFHQ has a strong ethnic bias, which is very problematic when applying such methods in production. Finally, using such generative models to produce art, can be problematic as the training data might be used against the consent of the respective artist.
A.5 Further Comparisons
In Table we provide further comparisons NeRF-based approaches, such as EG3D and Mimic3D, while also another Gaussian splatting based method, Gaussian Shell Maps (GSM), which was originally designed for full body 3D human avatars. We observe that EG3D produces the overall best results. Nevertheless, all except our method takes advantage of the view conditioning method. Further, EG3D also applies 2D super-resolution layers after NeRF rendering, making even less 3D consistent.
A.6 Multi-view Regularization Ablation
In Table we provide ablation for different number of views shown during the multi-view regularization. We observe better quality with increasing number of views. After four views, however, the performance does not improve further. Instead we observe slightly worse FID results. Therefore, we have selected four views as our default setting in all experiments. Notably, the training does not fully collapse, when removing the multi-view regularization from our model. We, however, observe sigmificantly worse results and worse training convergence, shown in FigFurre 9 Here, the output of the discriminator considerably lower, when removing the multi-view regularization. This means that the discriminator easily detects the fakes and produces very high gradients that harm the training.
15

---

Discriminator Output
Multiview
Multi-view 2
Multi-view
Multi-view
-0.5
Falon Scores
-1.0
-1.
-2.
-2.
Million training Steps
Figure Comparison of the discriminators output signal for fake images when using different settings for the multi-view regularization. Higher scores denote better training convergence.
A.7 Failure Cases
While the vast majority of rendered images look very good, in some cases we still observe occluders be present in the rendered output images. This is shown in the top Figure 10 We believe that this is due to the masking network that sometimes masks multiple persons or confuses the background with hat shown in Figure 11 Additionally, we notice that some heads have hole in the hair region when rotating them very far to the side. Such effect can likely be avoided by using more training samples with steeper viewing angles.
Figure 10: Top row shows some rare cases where the face is still obscured by floating objects. This likely fixed by using better masking network. And the bottom row shows holes that become visible when turning > Such viewing angles are never shown during training.
16

---

Figure 11: Real training examples with respective masks, where the masking network masks multiple persons, or where the background is confused with hat.
A.8 Contrastive Loss
In the method section (Section 3) of our main paper, argue that the contrastive loss function of GSGAN [15] not ideal for 3D GAN training with FFHQ. This is because lot camera view are very similar in the dataset, and the network is highly penalized if it confuses two almost identical camera views. Specifically, the contrastive loss if formalized as [[5]
exp(sim(pr. Pt)/
Gpose log
(1)
b= exp(sim(pr.PF))
Here, sim denotes the cosine similarity, pr an embedding the image, P the camera embedding of the matching camera, P the camera embedding of all non matching cameras in batch B, and temperature scaling parameter. If we now have a P that very similar another camera embedding p within the training batch, we receive a very high loss that harm the training convergence. This can be seen in |Figure 12] where the contrastive loss decreases for the first few million training steps and then suddenly increases again after about 3.8 million steps. At this point the training completely collapses and does not recover again.
Contrastive Loss over Training Iterations
3.5
3.0
Contrastive Loss
2.5
2.0
1.5
1.0
0.5
Million Training Steps
1e6
Figure 12: Contrastive loss tracked over the course of the training. After 3.8 million steps, the loss increases again, causing the training to collapse.
17

---

A.9 Dataset Filtering
In Figure 13 we give an overview of the categories that we applied identify bad training images. While mainly focusing on images where the face is obscured by an object, we also found images that generally harm the training convergence. Examples for such images are shown in the 'depiction' class and the 'blurry" class in Figure 13 Consequently, we removed those images along with the other images, with occluders.
niCS?
Microphone
Hand
>1 Face
Behind Obj.
Food
Instrument
Depiction
Blurry
Figure 13: An overview of the main categories for images that we identify as bad training data. Categories like 'Hand', '>1 Face' and 'Microphone' were the most frequent ones.
To identify, which images should be removed we use PicArrange [6]. which an application that allows for querying large datasets with text prompts. A screenshot of the application is shown in Figure 14] where we query the dataset with the prompt 'food" and receive matching images.
ausoewdin)
Food
Weriichil dtfee
FFHQ LPF meu.oooooooooo
sinbezihen
Sorthrang
Meu
Figure 14: Screenshot PicArrange [6]. which was used to identify bad bad training images, by writing text prompts like "food" in the upper left corner. PicArrange then returns the images with the highest CLIP correlation to the respective prompt.
18

---

A.10 Explicit 3D Environments
In Figure 15] we demonstrate that our 3D heads can be imported into explicit 3D environments. such as Unity. Using the 3DGS plugin by Aras Pranckevičius / UnityGaussi anSplatiin)). the 3D faces can simply be placed into 3D scenes and interact with other 3D objects. Combining this with the inversion capabilities of our models, we can now create realistic 3D assets just from single 2D image.
com/ aras p/
Figure 15: Our faces imported into unity using the Unity Gaussian splatting plugin by Aras
Pranckevičius: https://githul. http cwwccoom/aaaaaa hat//aras-aa arraaa//unnityduusssaaslpss.
A.11 Rendering Results
In Figure 16 and Figure 17 we demonstrate uncurated renderings of our proposed method, trained on FFHQC at 512 resolution. Apart from some outliers, we observe very high quality with few occluders being present. Further, in Fifuue 18 and Figure 19 we demonstrate the difference between the original FFHQ dataset and our adapted FFHQC dataset. We observe fewer occluders and render the full head instead of clipping just above the hair line.
19

---

Figure 16: 180 example renderings of method trained FFHQC 512 resolution (vé 0.8).
20

---

Figure 17: Example renderings of method trained FFHQC at 512 resolution (w 0.8).
21

---

Figure 18: Rendering examples of our model with FFHQ ( 1.0).
Figure 19: Rendering examples of our model with FFHQC showing the full head with fewer occluders (v 1.0).
22

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: We give brief overview of our work in the abstract and list our main contributions again in the introduction.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: We discuss the limitations for both our method and our curated dataset in more detail in the appendix, additionally showing failure cases.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g.. independence assumptions, noiseless settings, model sell-speciiiatiinn asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or speech-to-tex, system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and faimesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
23

---

Answer: [NA]
Justification:
No theoretical results or proofs are made in our paper.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proof's can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided not)?
Question:
Answer: [Yes]
Justification: Next to the details in our main paper, provide more detailed information about specific training settings in the appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, descríbing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e-g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path to reproducing verifying the results.
5. Open access to data and code
24

---

Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Question:
Answer: [Yes]
Justification: The training data that is publicly available and the code will be published along with detailed instructions how to reproduce our results. We also provide the code to the reviewers.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.com cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).
The instructions should contain exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https
y /nips. /tips.cc///pp/ http:/ccccccccguueeeeeeeeeeeeeee wwaccc/guuisss/ee httppplliiiiiiiieeeeeeeeeeaaaaaa
for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question:
Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary understand the results?
Answer: [Yes]
Justification:
We report our training details in the appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented the core the paper to level detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [No]
Justification: Training our models and the models of related work takes up lot of time and resources, not allowing us to run them multiple times.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emoo bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
25

---

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (e.g. negative еπτог rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
We report memory consumption and training time in
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate type of compute workers CPU or GPU, internal cluster, cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported the paper (e.g., preliminary or failed experiments that didn't make it into the paper).
9. Code of ethics
Question: Does the research conducted the paper conform, in every respect, with the NeurlPS Code of Ethics httpp::///eurip::: wc/ppubllc/tthicssideeee.....eie
Answer: [Yes]
Justification: We review the NeurIPS code of Ethics and ensure that there no conflict with our work.
Guidelines:
The answer NA means that the authors have not reviewed the NeurlPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: We provide a short section about the broader impact of our work in the appendix.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
26

---

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: Our paper does not pose such risks as we do not use any additional data other than publicly available image datasets.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model. for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
recognize that providing effective safeguards challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: We only use FFHQ which was published along with the StyleGAN paper which cited. FFHQ has CC BY-NC-SA 4.0 license.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset.
27

---

For scraped data from a particular source (e.g., website). the copyright and terms of service of that souree should be provided.
assets are released, the license, copyright information, and terms use in the package should be provided. For popular datasets, paperssi thood cww datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is not available online, the authors encouraged to reach out to the asset's 's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer:
[NA].
Justification: We do not release new assets. We only change the pre-processing of existing public datasets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/model part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset is used.
At submission time, remember anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question:
For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: Our paper does not involve crowdsoureing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According to the NeurlPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: Our paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
28

---

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if' applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Question:
Answer: [No]
Justification:
We exclusively use LLM for writing, editing, or formatting.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy for what should or should not be described.
29


Reviews:
[{"review_id": "O5BfroiZ6m", "reviewer": "Reviewer_ymYL", "summary": "This paper proposes CGS-GAN, a framework for generating human heads based on the 3DGS GAN framework. This framework achieves stable training and the synthesis of high-quality, 3D-consistent human heads without relying on viewing conditions. In order to ensure training stability, this paper introduces a multi-view regularization that promotes 3D consistency and improves training stability. Furthermore, this work presents a memory-saving generator design. In order to improve the quality and realism of the generative model, a random background augmentation method is employed. Additionally, this paper has created a high-quality dataset based on FFHQ. This dataset supports very high resolutions, focuses on a wider area of the human head, and reduces view-dependent artifacts to enhance 3D consistency.", "questions": "1. The paper proposes averaging gradients from multiple rendered views per training step to improve stability, but it's unclear how this averaging is implemented. Are the gradients from different views treated equally? \n1. Is it possible to add a user study for comparative analysis?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. **Multi-view regularization**：The model renders the same generated 3D head from multiple random viewpoints. The gradients from all these views are then averaged. This more stable and comprehensive averaged gradient is used to update the model, thereby improving training stability.\n1. **Random background augmentation**：This method enhances the quality and realism of the generative model, helping to reduce artifacts.\n1. **Self-created new dataset**：A high-quality dataset based on FFHQ was created for high-fidelity, 3D-consistent head modeling.\n\nWeaknesses\n\n1. The claim that removing camera conditioning typically destabilizes training—even leading to collapse—may be overstated. In my experience, 3D GANs can still be trained stably without camera conditioning. However, doing so often leads to identity inconsistency during inference: when rendering from different viewpoints with a fixed latent code w, the identity of the generated face may shift. One benefit of camera conditioning is that by fixing it (e.g., to a frontal view) during inference, the identity can remain consistent across viewpoints. I may be missing some insight here, and I would welcome further discussion or clarification on this point.\n2. The authors claim that their generator can be trained without camera conditioning while still producing consistent 3D heads from any viewpoint. However, it is unclear how identity consistency is maintained during inference when the camera viewpoint changes. In previous works, removing camera conditioning often led to identity drift across views. Could the authors clarify which component(s) of their approach—such as the improved 3DGS generator architecture or multi-view regularization—specifically contribute to preserving identity consistency in the absence of camera conditioning?", "was_revised": true, "final_justification": "The authors’ rebuttal adequately addresses my concerns regarding identity consistency and training stability. The explanation of the method’s behavior in 3DGS is convincing. Therefore, I recommend the paper for acceptance.", "rebuttal": "Dear Reviewer ymYL,\n\nThank you for your very positive review. In the following, we will address your questions and concerns wrt potential weaknesses.\n\n_**Weakness 1:** The claim that removing camera conditioning typically destabilizes training—even leading to collapse—may be overstated. In my experience, 3D GANs can still be trained stably without camera conditioning. However, doing so often leads to identity inconsistency during inference: when rendering from different viewpoints with a fixed latent code w, the identity of the generated face may shift. One benefit of camera conditioning is that by fixing it (e.g., to a frontal view) during inference, the identity can remain consistent across viewpoints. I may be missing some insight here, and I would welcome further discussion or clarification on this point._\n\nIn our own experiments, removing view conditioning from GSGAN and GGHead led to very poor performance. Especially in early training stages, the generator fails to  keep up with the discriminator, leading to either (i) full collapse (GSGAN) or to (ii) significantly slower and less stable convergence (GGHead). Regarding the remark on identity inconsistency, we did not observe identity changes in any of our results, with and without view-conditioning. We assume that  identity inconsistency is more pronounced in NeRF based 3D GANs, given they are less constrained to produce discrete 3D scenes.\n\n---\n\n_**Weakness 2:** The authors claim that their generator can be trained without camera conditioning while still producing consistent 3D heads from any viewpoint. However, it is unclear how identity consistency is maintained during inference when the camera viewpoint changes. In previous works, removing camera conditioning often led to identity drift across views. Could the authors clarify which component(s) of their approach—such as the improved 3DGS generator architecture or multi-view regularization—specifically contribute to preserving identity consistency in the absence of camera conditioning?_\n\nWe did not observe identity changes when disabling the view-conditioning for GSGAN and GGHead or our approach. This is likely due to the nature of 3DGS, which might make it very difficult to model inconsistent identities with discrete point clouds.\n\n---\n\n_**Question 1:** The paper proposes averaging gradients from multiple rendered views per training step to improve stability, but it's unclear how this averaging is implemented. Are the gradients from different views treated equally?_\n\nThe implementation of gradient averaging is very simple. In pytorch, we compute a loss and then apply the gradient with the backward() function:\n\n```py\nloss = -softplus(-D(G(latent, view)))\nloss.backward()\noptimizer.step()\n```\n\nIf we now accumulate the loss across a larger sample size before calling the backward function, we can average the gradients.\n\n```py\nloss  = softplus(-D(G(latent, view_1)))\nloss += softplus(-D(G(latent, view_2)))\nloss += softplus(-D(G(latent, view_3)))\nloss += softplus(-D(G(latent, view_4)))\nloss /= 4\nloss.backward()\noptimizer.step()\n```\n\nThis way, each angle is treated equally.\n\n---\n\n_**Question2:** Is it possible to add a user study for comparative analysis?_\n\nWe agree that a user study is the best way to validate and compare the performance of 3D face generators. Due to limited time and resources, we had to rely on the well established FID metric and our proposed FID_3D metric. While purely computational, these correlate strongly with human preference. However, for future work, such a user study will probably be very interesting and could give valuable insights on how to improve such models even further."}, {"review_id": "HvC2cBy9JO", "reviewer": "Reviewer_SFiD", "summary": "CGS-GAN introduces a new framework for generating high-quality, 3D-consistent human head images. It eliminates the need for view-conditioning, which often compromises 3D consistency in previous methods, by using multi-view regularization during training to ensure stable and consistent 3D geometry. The authors also developed a scalable generator capable of producing images at up to 2048x2048 resolution and created a new dataset, FFHQC, derived from FFHQ, with enhancements like larger head crops and filtered occluded images to improve side views. The framework achieves competitive FID scores, indicating high rendering quality, and addresses issues like hole artifacts through background augmentation.", "questions": "Q1: **Generalizability**: How well does the CGS-GAN framework perform when applied to 3D synthesis of objects other than human heads? Are there plans to extend this method to more general 3D object generation?\n\nQ2: **Training Stability**: The authors mention that without multi-view regularization, the model tend to collapse after 8M training steps, but GSGAN seem not to have this kind of regularization. Does this unstability introduced by the difference of upsampling operations? \n\nA minor question: In the supplementary material, the authors mentioned that they use a cube with side legth of 1(like EG3D) instead of 2, does this choice significantly influence the training process or the final performance?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The core idea of explicitly disentangling geometry from semantics is highly intuitive and powerful. The architectural design is clean, and the potential applications are substantial.\n\n**Strengths**:\n\n**High Conceptual Novelty**: The primary strength is the explicit and architecturally enforced disentanglement of geometry and semantics. This is a more principled approach to controllable and 3D consistent generation and addresses a clear limitation of previous models. And this method manage to generate images at 2048 $\\times$ 2028 resolution. \n\n**Engineering Contributions**: The paper makes significant engineering contributions to address common GAN training challenges:\n\n**Dataset Bias**: It proposes a simple and effective filtering method to rebalance the FFHQC dataset, reducing view-dependent effects and improving side view quality. This approach is not only effective for this work but also provides valuable techniques for other GAN research.\n\n**Hole Artifacts**: It introduces random background augmentation to mitigate \"hole artifacts\" in generated images, enhancing rendering quality and training stability, which can benefit other GAN applications.\n\n**Weaknesses**:\n\n**Scope**: A potential limitation is the focus on human head synthesis, which may restrict the method's applicability to other 3D objects, for example AFHQ, where the pre-proecess procedure might not be available. \n\n**Unclear Presentation**:The distinction between the proposed generator architecture and that of GSGAN is not clearly articulated in the paper. While the authors do describe some detailed differences in the text, such as the variation in the upsample operation, I would suggest including a dedicated figure to visually highlight the core differences between the two. Furthermore, the textual description should also be revised to more prominently emphasize these key distinctions.", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer SFiD,\n\nThank you for your thorough review and the overall positive rating. We aim to improve the clarity of the paper by addressing the following questions and weaknesses:\n\n_**Q1: Generalizability:**\n“How well does the CGS-GAN framework perform when applied to 3D synthesis of objects other than human heads?“_\n\nIn prior works, GSGAN or EG3D showed that 3D GANs generalize well for other domains such as cats (AFHQv2 Dataset) or cars (ShapeNet Cars Dataset). Therefore, we expect  CGS GAN to handle these datasets successfully, potentially with less difficulty as cat and car images exhibit fewer view-dependent biases and dynamic expressions. By contrast, human datasets are challenging as subjects often gaze directly towards the camera lens and are more likely to smile, creating strong pose- and expression-related correlations.\nFor the present work, we focused on human heads, as this application is widely relevant, while it also aligns well with our future research ideas.\n\n\n_“Are there plans to extend this method to more general 3D object generation?”_\n\nExtending the method for more general object generation would be a very interesting addition to the method, but is currently not our focus. However, we will publish the code so that this can be done in future work.\n\n---\n\n_**Q2: Training Stability:** The authors mention that without multi-view regularization, the model tend to collapse after 8M training steps, but GSGAN seem not to have this kind of regularization. Does this unstability introduced by the difference of upsampling operations?_\n\nThe instability is introduced by removing the view-conditioning. In GSGAN, the generator always receives the viewing direction, allowing it to ‘cheat’: it can synthesize heads that look good only from that defined direction but poorly from any other direction. This is no longer the case with our method, which learns to synthesize 3D consistent heads whose appearance is plausible and consistent from any viewpoint. This training objective with multi-view consistency is much more challenging and causes the training to be less stable.\n\n---\n\n_**A minor question:** “In the supplementary material, the authors mentioned that they use a cube with side legth of 1(like EG3D) instead of 2, does this choice significantly influence the training process or the final performance?”_\n\nUsing the same settings as in EG3D (side length of 1m, a radius of 2.7m and a FoV of 12°) makes sense as it aligns well with the average camera settings from the real images in the training dataset. However, as these values are very rough estimations, it does not make a big difference for the training performance when using different settings as in GSGAN (side length of 2m, a radius of 2.7m and a FoV of ~20°). It is, however, important to scale all Gaussian primitives accordingly and adapt the regularizations from GSGAN that enforce clustering and penalize if Gaussian primitives move too far from the center.\n\n---\n\n_**Weakness 1:** “Scope: A potential limitation is the focus on human head synthesis, which may restrict the method's applicability to other 3D objects, for example AFHQ, where the pre-proecess procedure might not be available.”_\n\nThe pre-processing steps that were done to create FFHQC include a semi-manual filtering of bad images, re-cropping, background masking, smile rebalancing and pose rebalancing. All of those steps, apart from the smile rebalancing, can be applied to new datasets (like AFHQ or ShapeNet Cars) as well. If the new dataset introduces new view-dependent biases it might cause some inconsistencies. Those can either be addressed by resampling or ignored, as the model will still produce good results.\n\n_**Weakness 2** “Unclear Presentation: The distinction between the proposed generator architecture and that of GSGAN is not clearly articulated in the paper. While the authors do describe some detailed differences in the text, such as the variation in the upsample operation, I would suggest including a dedicated figure to visually highlight the core differences between the two. Furthermore, the textual description should also be revised to more prominently emphasize these key distinctions.”_\n\nThank you for this valuable comment.  Due to limited space in the main paper, we will add such a comparison figure to the supplementary materials."}, {"review_id": "iVt8EJVCem", "reviewer": "Reviewer_QMop", "summary": "CGS-GAN is a 3D Gaussian-splatting GAN framework that enables stable training and high-quality 3D-consistent synthesis of human heads without relying on view-conditioning, it can synthesizes high-resolution (up to 2048²) human heads that remain identity-consistent through multi-view regularization and conditional loss. In addition, the work introduces FFHQ-Clean, a re-cropped, de-occluded, pose- and expression-balanced extension of FFHQ designed to mitigate floating artifacts, occlusions, and smile bias. Experiments show that CGS-GAN matches or surpasses GGHead and GSGAN across FID, FID-3D, and inference speed, while substantially lowering GPU memory usage and training time.", "questions": "1.\tPlease refer to Strengths and Weaknesses\n2.\tIn the Figure 5 visualization, why do you use different identities for different methods? The same source images should be used for testing different methods.\n3.\tThe comparative qualitative results for the 2048 resolution are missing.", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: \n1.\tThe paper is well-written and easy to understand.\n2.\tImproves over GAN-based state-of-the-art (SOTA) methods.\n\nWeaknesses:\n1.\tThe motivation of the article is not clearly stated. In particular, don’t have a clear explanation of view-conditioning and camera pose, which makes the central claim—namely, that “without relying on view-conditioning”—difficult to follow. For example, if the camera view not included as training input, when rendering, how to indicate camera direction; And the rationale behind the experimental design in Figure 3 is likewise unclear: in what situation when utilizing GSGAN and GGHead will has the view-conditioning does not align with the camera pose, if this is a common phenomenon, or manually situation, and why don’t have results of CGS-GAN itself in Figure 3.\n2.\tAll comparative evaluations are confined to GAN-based Gaussian-splatting methods (e.g., GSGAN, GGHead). Although earlier GAN-based methods demonstrate that 3D Gaussian-splatting can be realized within a GAN framework, but the paper does not conduct a systematic comparison with state-of-the-art diffusion approaches (e.g., Morphable Diffusion, Gaussian3Diff, etc.). Consequently, it leaves unanswered (i) how CGS-GAN fares in image fidelity, 3D consistency, and computational efficiency relative to diffusion-based generators, and (ii) why a GAN architecture is still preferable—or at least competitive—in an era where diffusion models dominate high-quality generative synthesis.\n3.\tLimited ablation analysis. The paper does not disentangle the contributions of the architectural choices, multi-view regularization, and modified conditional loss from the baseline with clear ablation. A more granular ablation would clarify which components are responsible for the observed performance gains.\n4.\tGenerally, the paper is incremental work based on GSGAN, cannot extract clear motivation and sufficient novelty, and also cannot see obvious improvement in experiments.", "was_revised": true, "final_justification": "I agree with Reviewer VAzj and the authors’ observation that 3D-GANs have the advantage of not requiring multi-view image observations of the same object. However, it is worth noting that there now exist several sizeable 3D head datasets—both real (e.g., RenderMe360) and synthetic (e.g., Head360)—that can be leveraged for training diffusion-based models. With appropriate priors, datasets containing hundreds or thousands of identities can be sufficient to achieve strong performance.\n\nBecause diffusion-based models require multi-view supervision, they can offer fine control over generated content using text prompts or single-image guidance. Recent works such as Portrait3D and DiffPortrait360 (both with publicly available code), as well as Morphable Diffusion (which should perform well when trained on RenderMe360), demonstrate this capability. In the literature, diffusion-based methods often compare with 3D-GANs on the image-to-3D task through GAN inversion techniques. This provides a straightforward and balanced way to make comparisons—by evaluating both approaches on the same input images (potentially with varied poses) and checking whether identity is preserved. Such an approach would also allow a fairer comparison with other 3D-GAN methods, avoiding selective emphasis on only ‘the strengths and weaknesses for each model’.\n\nI would not suggest that unconditional generation from a random latent vector is less useful; maintaining an open mind about diverse generation paradigms is important. Nevertheless, given the availability of these datasets and methods, a controlled comparison—starting with the simplest case of single-image-to-3D generation—would be highly informative. This evaluation protocol has been adopted by other 3D-GAN works such as EG3D, PanoHead, and GSGAN, and it would be important to see how performs under the same setting against a diffusion-based baseline.", "rebuttal": "Dear Reviewer QMop,\n\nThank you for your review. In the following we will address the stated weaknesses and questions:\n\n_**Question 1:** “Please refer to Strengths and Weaknesses”_\n\n_**Weakness 1.** “The motivation of the article is not clearly stated. In particular, don’t have a clear explanation of view-conditioning and camera pose, which makes the central claim—namely, that “without relying on view-conditioning”—difficult to follow.”_\n\nWe appreciate the reviewer’s feedback that our discussion of view-conditioning and camera pose was not sufficiently explicit. This is a very central component of the paper and understanding it is essential for understanding our main contribution. We will improve the text in the paper for better clarity. \nBelow we offer a more intuitive explanation of view-conditioning to make sure that the concept is clear during the rebuttal dialog:\n\nThe general goal of generative 3D models is to produce a 3D scene (in our case a 3D head) that can be rendered from arbitrary views and used in 3D environments such as game engines or VR environments. This requires a 3D representation that looks realistically from any viewpoint without re-synthesizing it for each view. With current 3DGS GANs (GGHead and GSGAN) this is not possible due to the used view-conditioning. \nSince the introduction of the StyleGAN architecture, most GANs (including all GANs discussed in the paper) are equipped with a mapping network that projects the random latent vector Z, sampled from a normal distribution, to a learned latent space W before forwarding it to the synthesis component of the generator. When using view-conditioning, the camera information is provided to this mapping network as well. If we now tell the generator to produce a 3D head, viewed from the left side, it can simply map the random latent vector towards a region in the learned latent space that renders the left side of the head especially well. While this produces good renderings for that specific view, the resulting 3DGS scene looks very poor when viewed in a 3D environment from novel views. \nEssentially, with view conditioning, the camera information is forwarded to the generator twice. First when mapping the latent vector and second  when rendering the 3DGS scene. If these cameras match, the quality is high. If they are different, quality drops as  shown in Figure 3. In our paper, we remove the first camera information that is forwarded to the mapping network but keep the camera information when rendering the 3DGS scene. Intuitively, the generator now does not know from which direction the scene will be rendered. Therefore, it has to create a head that looks realistically from any viewpoint.\n\nIn summary: Using view-conditioning allows the generator to ‘cheat’ as it already knows before the synthesis, from which angle the head will be rendered. This causes the generator to produce renderings that exclusively look good from that specific view. To produce good quality for any view, we would have to re-synthesize the scene each time we change the camera view. While this does not allow us to export the head as a single consistent 3D scene with high quality for any view, it also causes the identity to shift each time we re-synthesize the scene.\n\n_“For example, if the camera view not included as training input, when rendering, how to indicate camera direction”_\n\nCamera direction is still provided to the renderer during both training and inference.  What changes is that it is not used as an explicit conditioning signal inside the generator or its mapping network. This is precisely what we mean by training “without relying on view-conditioning.” We will incorporate this explanation into the paper for greater clarity.\n\n_“And the rationale behind the experimental design in Figure 3 is likewise unclear: in what situation when utilizing GSGAN and GGHead will has the view-conditioning does not align with the camera pose, if this is a common phenomenon, or manually situation”,_ \n\nWhen using the synthesized 3DGS scenes of the generator and displaying it in a web browser, a game engine or a VR setting, we cannot re-synthesize the head each time so that the view-conditioning matches the current rendering view. Instead we require a fixed and consistent 3D scene that looks good from any viewpoint.\n\n_“and why don’t have results of CGS-GAN itself in Figure 3.”_\n\nWe did not include our result in Figure 3 as it was intended to highlight the limitation of existing methods rather than providing a comparison. Such a comparison can be found in Figure 5, which essentially demonstrates the same observation with more examples. Nevertheless, if including our results in Figure 3 improves the overall clarity, we are happy to include it as well.\n\n---\n\n_**Weakness 2.** “All comparative evaluations are confined to GAN-based Gaussian-splatting methods (e.g., GSGAN, GGHead). Although earlier GAN-based methods demonstrate that 3D Gaussian-splatting can be realized within a GAN framework, but the paper does not conduct a systematic comparison with state-of-the-art diffusion approaches (e.g., Morphable Diffusion, Gaussian3Diff, etc.). Consequently, it leaves unanswered (i) how CGS-GAN fares in image fidelity, 3D consistency, and computational efficiency relative to diffusion-based generators, and (ii) why a GAN architecture is still preferable—or at least competitive—in an era where diffusion models dominate high-quality generative synthesis.”_\n\nAlthough in recent years diffusion-based approaches have surpassed the rendering quality of GANs for some domains (2D synthesis or video generation), GANs still stand out for their efficient real-time editing and rendering capabilities and their clear advantage to not require 3D ground-truth data. The latter is especially important for 3D synthesis, as real 3D ground truth data is very difficult to obtain. This also goes for the two mentioned models GAUSSIAN3DIFF and Morphable Diffusion. GAUSSIAN3DIFF uses a pre-trained 3D-aware GAN (PanoHead) to create 3D ground truth data, and Morphable Diffusion creates pseudo 3D ground truth data using Zero-1-to-3 and SyncDreamer. \nIn contrast, 3D GANs can be trained on a large collection of 2D images, without requiring multiview information. Additionally, instead of overfitting the 3D scene to a single given person like in Morphable Diffusion or Diffusion Avatars, which can be very time consuming and computationally expensive using a diffusion model, GANs create a latent space with unlimited identities that each can be synthesized in less than 0.01 seconds.\nFor all those reasons, we treat 3D GANs and diffusion methods as two separate classes of models and do not make comparisons between them. \n\n---\n\n_**Weakness 3.** “Limited ablation analysis. The paper does not disentangle the contributions of the architectural choices, multi-view regularization, and modified conditional loss from the baseline with clear ablation. A more granular ablation would clarify which components are responsible for the observed performance gains.“_\n\nIn the paper, we provide some ablations for the most important components. This includes the visual comparisons in Figure 6, where we deactivate the smile rebalancing, compare the cropping strategies and the random background augmentation. Additionally, we provide a small ablation in the text (starting from line 309) for the multiview-regularization, where we argue that rendering four times is ideal. This was not supported with numbers in the submitted version, but we are going to add the numbers in the supplementary material.\n\n---\n\n_**Weakness 4.**. “Generally, the paper is incremental work based on GSGAN, cannot extract clear motivation and sufficient novelty, and also cannot see obvious improvement in experiments.”_\n\nIn the following, we list our contributions, which we believe are novel and clearly improve state-of-the-art methods:\nProper 3D consistent output scenes without relying on view-conditioning as prior methods\nSignificantly higher output resolutions compared to prior methods of up to 2048\n- A lightweight generator architecture with reduced memory footprint and faster synthesis speed\n- A new metric (FID_3D) for evaluating the quality of 3D scenes rather than 2D generated images\n- Improved FID and FID_3D results compared to prior methods\n- A random background augmentation for reducing the hole artifacts\n- A multi-view regularization that stabilizes the training convergence\n- Revised conditional loss function for better training stability\n- A high quality dataset FFHQC with:\n     - resolutions of up to 2048x2048\n     - removed training samples that harm the quality\n     - a re-cropping strategy for synthesizing the entire head\n     - a smile rebalancing for reducing view-dependent biases\n\n---\n\n_**Question 2.** “In the Figure 5 visualization, why do you use different identities for different methods? The same source images should be used for testing different methods.”_\n\nWe understand the reviewer’s concern and would like to explain why showing exactly the same face across methods is not feasible. The identity of the rendered faces of 3D GANs is not controllable. This is because each model learns its own latent space that can be completely different. A latent vector that encodes one identity in CGS-GAN has no semantic meaning in GSGAN or GGHead. In Figure 5, we aimed to choose fair comparisons that best represented the strengths and weaknesses for each model. The main takeaway message from Figure 5 is that both prior methods either change the identity when re-synthesizing the heads with the current view conditioning, or produce very poor renderings when fixing the view-conditioning.\n\n---\n\n_**Question 3:** “The comparative qualitative results for the 2048 resolution are missing.”_\n\nWe  provided renderings at 2048 resolution in the supplementary materials, located under /cgs_gan_supplementary/2048_renderings/."}, {"review_id": "L1GaWKOBmj", "reviewer": "Reviewer_VAzj", "summary": "The paper improves 3D GAN frameworks, which are trained on large 2D datasets, with a special focus on 3D consistent generations and high-resolution results.\n\nThe paper claims that one of the main drawbacks of 3D GANs is the camera conditioning of the Generator, which is necessary in previous frameworks in order to keep a healthy balance between generator and discriminator. In order to remove this conditioning the paper introduces a multi-view regularization technique, where one generated 3D scene is rendered multiple times and gradients are averaged. After carefully accounting for batch statistics the authors show that this technique results in more 3d consistent generations. Furthermore, that authors improve the architecture from GSGAN, and make it more scalable to obtain more Gaussians. Using random background augmentation, the authors deny the Generator to exploit white background for modeling specular highlights, which results in holes in the predicted geometry.\n\nNext to improving the network architecture and training scheme, the authors propose a careful data cleaning scheme and demonstrate that less, but more curated data results in better generations. Specifically, occluders are removed, frontal non-smiling images are added for better balance, and extreme view-point images are duplicated to balance the camera distribution. The new dataset is qualitatively shown to result in more consistent generations", "questions": "As the experimental section already presents thorough comparisons, there are not many important remaining questions that I can think of.\n\n- The paper is focused on generating 3D heads, which is so far also the most important application of 3D GANs. Have the authors experiment with different datasets? Ultimately, one of the main things that is holding (3D) GANs back is the balance during training, and the authors claim to improve training stability. Ultimately, demonstrating more results on other datasets would solidify this claim and show the public that 3D GAN training is more accessible now. \n- Similarly, I am wondering about the scalability w.r.t. to training data. It sounds like well balanced training data is especially important for GANs. How much effort would it be train the proposed model on large-scale dataset such as the LAION-Face subset? How much effort would go into data cleaning? Is there a clear recipe for the cleaning process, or is it a trial-and-error process that alternates between cleaning and training?\n- Fig. 6 (left) shows that without smiling rebalancing, the model can still generate a 3DGS point cloud, which appears to be 3D inconsistent. How can this be explained? Simply through volumetric cheating?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**:\n- Multi-view regularization allows for removal of camera conditioning in generator which finally makes 3D-aware GANS to actual 3D GANs. This is an important step for 3D generative that are trained merely on 2D image collections.\n- The carefully designed dataset curation strategy significantly helps with generation quality.\n- More smaller improvements such as generator architecture and random background colors lead to additional improvements.\n- The paper is well-written and easy to follow.\n- Thorough experiments validated the proposed ideas. The effects on training and rendering speed are also measured.\n\n**Weakness**:\n- Fig. 6 (left) shows that model can still predict 3d \"inconsistent\" results, by volumetric-cheating. While the smile rebalancing helped in this scenario. It raises questions about the scalability of the method., e.g. whenever going to a new dataset one might have to go through tedious dataset curation. \n- It remains to be seen whether the proposed improvements also carry over to different datasets, and whether it can be scaled to larger image collection of faces without extreme data curation overhead until a good balance can be obtained.\n\n\nOverall, the paper shows careful considerations of many existing issues in 3D GANs (specially 3DGS-based 3D GANs). While some ideas might sound incremental, overall they accumulate to significant progress. The removal of the camera conditioning in the generator is of special importance, and has to potential to make 3D GANs more popular again in the future.", "was_revised": true, "final_justification": "I stand by my original judgment, and vote for acceptance. \nThe paper proposes several improvements for an already elaborate 3D-GAN system, especially the removal of camera conditioning in the generator is an important step for 3D-GANs.\n\n3D-GANs, while it is not clear yet how well they can scale, are still a highly relevant research topic, due to their ability to learn 3D data distributions merely from a collection of 2D images with associated camera information.", "rebuttal": "Dear Reviewer VAzj,\n\nThank you for your positive review. In the following we will go through the weaknesses and questions.\n\n_**Weakness 1:** Fig. 6 (left) shows that model can still predict 3d \"inconsistent\" results, by volumetric-cheating. While the smile rebalancing helped in this scenario. It raises questions about the scalability of the method., e.g. whenever going to a new dataset one might have to go through tedious dataset curation._\n\nYes, that is true. Every time we deal with view-dependent biases like smiling or eye gaze, it is difficult to model those biases with a single static 3DGS scene. In such a case the model will likely apply volumetric-cheating to some degree. While re-balancing is one simple solution, we could alternatively forward the gaze or smiling information as a separate conditioning into the training pipeline. This way, the volumetric-cheating should also be reduced, while we would also be able to control those attributes during inference. While this might not be as applicable for other domains like cars or cats, those datasets probably possess little to no view-dependent biases, compared to human datasets.\n\n---\n\n_**Weakness 2:** It remains to be seen whether the proposed improvements also carry over to different datasets, and whether it can be scaled to larger image collection of faces without extreme data curation overhead until a good balance can be obtained._\n\nThat is definitely a direction that we want to go into. In our experiments, we see that the generator starts to outperform the discriminator at some point during the training. This possibly suggests that we can increase the complexity of the discriminator or the complexity of the training data even further. And although the data curation for FFHQ was very complex and contained a lot of manual filtering, we can now utilize the resulting dataset to automatically filter new images. \n\n---\n\n_**Question 1:** The paper is focused on generating 3D heads, which is so far also the most important application of 3D GANs. Have the authors experiment with different datasets? Ultimately, one of the main things that is holding (3D) GANs back is the balance during training, and the authors claim to improve training stability. Ultimately, demonstrating more results on other datasets would solidify this claim and show the public that 3D GAN training is more accessible now._\n\nFor now, we have only experimented with face datasets, as they align well with our research. However, benchmarking our model across other datasets would be very interesting. For AFHQ and ShapeNet Cars it should be a straight forward adaption. For other datasets, good camera poses might be difficult to obtain, which are currently essential for the training pipeline. \n\n---\n\n_**Question 2:* Similarly, I am wondering about the scalability w.r.t. to training data. It sounds like well balanced training data is especially important for GANs. How much effort would it be train the proposed model on large-scale dataset such as the LAION-Face subset? How much effort would go into data cleaning? Is there a clear recipe for the cleaning process, or is it a trial-and-error process that alternates between cleaning and training?_\n\nWe really like the idea to scale the model to larger datasets like LAION-Face. Given that the model also performed very well on the original FFHQ dataset, which included a lot of bad training samples, it might not even be required to go through such careful data pre-processing to achieve good results. The main reason to filter the dataset was to achieve results of higher quality rather than improving the stability. Additionally, since we now defined a set of good and bad training samples, we should be able to automate the filtering process. And even if we filtered out 70% of the 20 million images, we would still end up with 120 times more images than in FFHQC.\n\n---\n\n_**Question 3:** Fig. 6 (left) shows that without smiling rebalancing, the model can still generate a 3DGS point cloud, which appears to be 3D inconsistent. How can this be explained? Simply through volumetric cheating?_\n\nYes, the same can sometimes be observed with the eye gaze. The model aligns the Gaussian primitives in a way so that they are only visible from certain viewing angles, similarly to lenticular printing."}]

Author rebuttal:

Meta-review: The paper received mixed but overall positive evaluations, with two accepts, one borderline accept, and one borderline reject. Reviewers highlighted several strengths, including the introduction of multi-view regularization, architectural improvements, and the curated FFHQC dataset (Reviewers #SFiD, #VAzj, #ymYL). The primary concerns were raised by Reviewer #QMop, who noted the absence of comparisons with diffusion-based approaches.

In the rebuttal, the authors clarified the instability issues arising from the removal of view-conditioning (#SFiD), explained identity consistency in 3DGS (#ymYL), outlined plans for clearer architectural comparisons (#SFiD), and defended the use of GANs as preferable for 3D generation under limited data availability (#VAzj, #ymYL). Both #VAzj and #SFiD were satisfied with these clarifications and upheld their support for acceptance. Reviewer #ymYL also endorsed acceptance after rebuttal, while Reviewer #QMop maintained that comparisons to diffusion models were necessary and therefore recommended borderline reject.

During the AC–reviewer discussion, Reviewers #VAzj and #ymYL emphasized that GANs are more appropriate under the current data constraints and argued that meaningful comparisons to diffusion-based models are not straightforward in this setting, countering #QMop’s concerns.

After weighing the reviews, rebuttal, and reviewer discussion, the AC aligns with the majority view. While Reviewer #QMop’s reservations are acknowledged, the paper received strong and consistent support from the other three reviewers. Accordingly, the AC recommends acceptance.
Decision: Accept (poster)