Condition Matters in Full-head 3D GANs
Authors: Heyuan Li
Huimin Zhang
Yuda Qiu
Zhengwentai Sun
Keru Zheng
Lingteng Qiu
Peihao Li
Qi Zuo
Ce Chen
Yujian Zheng
Yuming Gu
Zilong Dong
Xiaoguang Han
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Head Synthesis
3D Avatar
3D-aware GANs
Primary area: generative models
TL;DR: We propose a semantic-conditional full-head 3D-aware GAN that uses view-invariant semantic features instead of camera views for conditioning, eliminating directional bias and improving generation quality, diversity, and consistency across all views.

Abstract: Conditioning is crucial for stable training of full-head 3D-aware GANs. Without any conditioning signal, the model suffers from severe mode collapse, making it impractical to training (\cref{fig:intro}(a,b)). However, a series of previous full-head 3D-aware GANs conventionally choose the view angle as the conditioning input, which leads to a bias in the learned 3D full-head space along the conditional view direction. This is evident in the significant differences in generation quality and diversity between the conditional view and non-conditional views of the generated 3D heads, resulting in global incoherence across different head regions (\cref{fig:intro}(d-i)). In this work, we propose to use \textit{view-invariant semantic feature} as the conditioning input, thereby decoupling the generative capability of 3D heads from the viewing direction. To construct a view-invariant semantic condition for each training image, we create a novel synthesized head image dataset. We leverage FLUX.1 Kontext to extend existing high-quality frontal face datasets to a wide range of view angles. The image clip feature extracted from the frontal view is then used as a shared semantic condition across all views in the extended images, ensuring semantic alignment while eliminating directional bias. This also allows supervision from different views of the same subject to be consolidated under a shared semantic condition, which accelerates training (\cref{fig:intro}(c)) and enhances the global coherence of the generated 3D heads (\cref{fig:teaser}). Moreover, as GANs often experience slower improvements in diversity once the generator learns a few modes that successfully fool the discriminator, our semantic conditioning encourages the generator to follow the true semantic distribution, thereby promoting continuous learning and diverse generation. Extensive experiments on full-head synthesis and single-view GAN inversion demonstrate that our method achieves significantly higher fidelity, diversity, and generalizability.

Published as a conference paper at ICLR 2026
CONDITION MATTERS IN FULL-HEAD 3D GANS
Heyuan Li
Huimin Zhangl.
Yuda Qiu
Zhengwentai Sun Yujian Zheng
Keru Zheng' Yuming Gu 6
Lingteng Qiu
Peihao Li²
Qi Zuo?
Ce Chen'
Zilong Dong2. Xiaoguang Ha.
Schooo of Science and Engineering, The Chinese University of Hong Kong, Shenzhen Tongyi Lab, Alibaba Group *Shenzhen Future Network of Intelligence Institute
*Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese USC
University of Hong Kong, Shenzhen MBZUAA
Figure 1: Our full-head model, HalanceHead, novelly conditioned on view-invariant semantic features to generate and diverse 3D heads. From top bottom, rows 1-7 show random-vier renderings; rows 8-10 display multi-view renderings; rows 11-12 visualize the geometries of the corresponding 3D heads.
"ntersship Tongyi Lab.
'Equal contribution.
Team lead.
M Corresponding authors.

---

Published as a conference paper at ICLR 2026
ABSTRACT
Conditioning is crucial for stable training of full-head 3D-aware GANs. Without any conditioning signal, the model suffers from severe mode collapse, making it impractical to training (fig. aa..).. However, series of previous full-head 3D-aware GANs conventionally choose the view angle as the conditioning in- put, which leads to bias in the learned 3D full-head space along the conditional view direction. This is evident in the significant differences in generation qual- ity and diversity between the conditional view and non-conditional views of the generated 3D heads, resulting in global incoherence across different head regions (lig. dd-)). In this work, we propose use fiew-invariant semantic feature as the conditioning input, thereby decoupling the generative capability of 3D heads from the viewing direction. To construct view-invariant semantic condition for each training image, we create a novel synthesized head image dataset. We leverage FLUX.1 I Kontext to extend existing high-quality frontal face datasets to a wide range of view angles. The image clip feature extracted from the frontal view is then used as a shared semantic condition across all views in the extended images, ensuring semantic alignment while eliminating directional bias. This also allows supervision from different views of the same subject to be consolidated under shared semantic condition, which accelerates training (fig. [()) and enhances the global coherence of the generated 3D heads (fig. D Moreover, as GANs often experience slower improvements in diversity once the generator learns a few modes that successfully fool the discriminator, our semantic conditioning encourages the generator follow the true semantic distribution, thereby pro- moting continuous learning and diverse generation. Extensive experiments on full-head synthesis and single-view GAN inversion demonstrate that our method achieves significantly higher fidelity, diversity, and generalizability. Project page: /ettps://Ihyee git.uuthhh.. httbbalancehead//
INTRODUCTION
Generalizable 3D head synthesis has long been central topic in computer vision and graphics, driven by its broad applications in digital entertainment, virtual avatars for augmented and virtual re- ality, and next-generation content creation pipelines. Traditional approaches, such as 3DMM 3lam & Vetter (1999) and FLAME Li et al. (017)), rely on large-scale, expensive 3D data sources like depth scans or multi view image collection. These methods, however, are often limited in capturing photorealistic facial details and back-head region with diverse hairstyles.
3D-aware GANs have been widely applied to 3D head generation. Early approaches used uncondi- tional 3D-aware GANs Nguyen-Phuoe al. (2019); Chan et al. 1b221b) on near-front-view images (e.g., FFH),, which did not report significant issues, as the view is relatively fixed and thus easier to learn However, EG3D Chan al. (2022) demonstrated that introducing view condition helps dis- entangle view-dependent information from other features, facilitating the learning of 3D priors and leading to noticeable quantitative improvements. Nevertheless, it also identified that using view- conditional generation can lead to the 2D billboard artifacts. To address this, EG3D proposed a regularization method based on randomly swapping the conditioning view. While this approach provides some improvement in near-front views, it does not fully resolve the issue.
Subsequent full-head 3D-aware GANs An et al. (2023): Li al. (2024); Wu et al. (2024) ; He al. (2025) largely build upon EG3D: they adopt and improve the tri-plane-like representation, while inheriting the ciew-conditioning strategy and its associated regularization. However, when trained on data covering a much broader range of views, especially full 360 perspectives, these models exhibit numerous artifacts and strong conditional view dependency. Specifically, the generation quality and diversity at the conditional view are significantly higher than at other views, resulting in clear global inconsistencies (Fig. 2(d.e)). To ensure that the front face appears natural, which is much more sensitive to human observation than other head regions, all these methods adopt common trick from EG3D. Specifically, they use conditioning signals for all view angles during training, but only employ a fixed frontal view at inference time. This approach prioritizes high- quality and diverse outputs in the front-facin region at the cost of limited representational capacity for rear views. As result, the back-head region often struggles with limited hairstyle variation and
2

---

Published as a conference paper at ICLR 2026
(8)
Loss Comperisan in Early Training
(b)
Disabling Condition at Mid-Training
(c)
FID-random Scores during Training
Gone Generatr ater Lestrrrssss (kg (ratl)
W/o Condition
Generatoo Laxs (10) scale)
10a
Diseble Condition
Condition
FID. roddammammmmmmm (icg scale)
10"
Semantic Conditio
Candition
Semarnic Conditioo
1.00
10P
201
10.
100
7000
5000 100001 bouo 2000 10000
Training (kima)
Trinnnn Progress (slmg)
Taining Progress (clmng)
(d)
(el
(h)
Yav
O
Cood
Figure 2: (a) No conditioning leads early mode collapse and unstable training. (b) Disabling view ditioning mid-training causes rapid collapse within 1000 kimg. (c) Semantic conditioning enables faster and more effective training. (d-i) View-conditioned models show strong directional bias and global incoherence; while conditional views are realistic, non-conditional views are distorted and inconsistent. (d,c), (f.g), and (h,i)
are results conditionally generated by random conditional views from PanoHead An et al. 2023 SphereHead Liet al. 2024), and HyPlaneHead Li et al. 22055
is prone to distortions and artifacts. This is one of the reasons why SphereHead Li et al. (2024) reports frequent artifacts in this area. Moreover, such an inference setting limits the overall diversity of the generated output, as it only utilizes small subset of the conditions available during training.
To avoid the limitations of view conditioning, we first consider removing it entirely. However, extensive experiments show that omitting the condition in the early training stage leads to rapid mode collapse and makes training infeasible. Alternatively, we increase the view-swap probability to during the middle phase of training, which is equivalent to disabling the condition. This setting typically leads to mode collapse within 1000k images, as shown in Fig. 2(a.b). Therefore, for full- head generation, conditioning is essential, but view-invariant conditioning strategy is required.
In this paper, we propose using front-view semantic feature as the conditioning input. Specifically, for person's multi-view images, we align the conditioning of all views to the semantic feature of the frontal view, since contains the most comprehensive information, including the essential facial features and the description of the hairstyle. By unifying all views under the same condi- tion, the dependency on specific viewing directions is effectively removed, making the conditioning view-invariant. However, such approach heavily relies on multi-view data, which are rare and expensive to scale up.
Instead, we leverage the strong generative capability of 2D image generation models, i.c. Flux.1 Kontext [Batifol et al. (2025), to expand high-quality real-world front head images into multi-view collections, thereby obtaining the required multi-view data. While 2D generation models cannot guarantee full 3D consistency, they excel at preserving the global semantic content, such as identity and clothing appearance. In our training process, these data are treated as single-view inputs, so incomplete 3D consistency does not affect learning; instead, the model learns to ensure view consis- tency through adversarial training and tri-plane-like like representation. Moreover, this data generation strategy significantly alleviates the limitations of in-the-wild datasets used in previous full-head models, which are difficult to collect and often suffer from imbalanced distributions of image qual- ity, quantity, and diversity across different views. Such imbalances lead to problematic supervision during training. In contrast, our approach guarantees that both data and semantic condition follow consistent distributions across all views, greatly benefiting the training of full-head models.
In addition, using semantic conditioning offers another benefit: it enables the use of large-scale data while maintaining diversity. The nature of adversarial learning often causes the generator to stall
3

---

Published as a conference paper at ICLR 2026
once it learns few modes or patterns that successfully fool the discriminator, rather than contin- uing to explore more diverse outputs. However, our semantic conditioning enforces the generator to produce images consistent with randomly sampled semantic conditions from the dataset, thereby strictly adhering to the real data distribution. Moreover, as the amount of data increases, the super- vision in the condition space becomes denser, leading to better model performance. As result, the model is able to continuously improve as the training data scale up.
Therefore, we construct 10-million-level 360 full-head image dataset using the aforementioned data generation method. We train our semantic-conditions 3D-aware GAN on this dataset, resulting in powerful 3D hull-hea model that achieves high diversity and fidelity with significantly reduced artifacts. Extensive experiments demonstrate its state-of ofo-offtthhh the-art performance.
In summary, our work makes the following major contributions:
We conduct the first in-depth analysis of the limitations of view-conditioning in full-head 3D- aware GANs and propose a novel semantic-conditional 3D-aware GAN address these issues.
To facilitate semantic-condition training, we construct a synthetic dataset with balanced image quality, quantity, and diversity across views, and with uniformly distributed semantic conditions.
Our method achieves state-of-the-art performance on both full-head synthesis and single-view 3D GAN inversion tasks, demonstrating the effectiveness of our proposed data and model design.
2
RELATED WORK
3D-Aware GANs 3D-awere Generative Adversarial Networks Szabó et al. (2019); Shi et al.
202T::
Liao et al. (2020): Gadelha et al. (2017): Nguyen-Phuoe et al. (2020 2019); Deng et al.
2022
Or-El et al. (2022); : Chan et al. (021a);; Schwarz et al. (2020) aims to synthesize view-
consistent images from diverse viewpoints by learning a scene's 's underlying 3D geometry and ap- pearance from collection of 2D images. Among them, Gram Deng et al. (2022) proposed to constrain the radiance field learning on 2D manifolds, embodied as a set of learnable implicit sur- faces. Eh3D al. (2022) introduced an efficient hybrid representation that projects a tri- tri-plane onto a 3D volume, technique that has since been widely adopted. GGHead Kirschstein et al. (2024) replaced the NeRF-based representation with 3D Gaussian Splatting (3DGS) by parameter- izing 3D Gaussian heads as UV maps, which enables efficient training on 2D convolutional neural networks (CNNs). While these methods achieve impressive visual quality, they still face significant challenges, particularly in synthesizing head images from large-angle views.
360 Full-Head Generation To address the limitations of narrow-view synthesis and enable full- head generation, researchers have focused extending existing architectures. PanoHead An et al. 2023) took a significant step by constructing a in-house training dataset with large view angles and expanding the tri-plane's capacity with parallel feature planes. SphereHead Li et al. (2024) improved upon this by introducing a spherical coordinate representation and novel view-image consistency loss, effectively mitigating artifacts such as mirroring and multiple faces. HyPlane- Head Li et al.] (2025) further introduces hybrid planar-spherical representation to reduce artifacts while maintaining high-quality detail rendering. More recently, 3DGH He et al. (2025) introduced a method that models the head and hair regions separately, reducing the large distribution divergence between front and back views. Alternatively, DiffPortrait360 Gu et al. (2025) and AvatarBack Xin) let al. (2025) directly trained back- head prior to synthesize the back image inference via 2D diffusion or 2D GAN, alleviating the burden of 3D generator.
Despite these architectural and methodological improvements, artifacts persist and diversity is lim- ited, especially in the back view. This is primarily due to the lack of high-quality back-view images and balanced data distribution in existing training sets. SOAP Liao al. (2025) attempted tackle this issue by collecting a large-scale 3D head dataset with 24k models and rendered training images from full range views. However, scaling up the number of unique identities with this approach re- mains significant challenge. ID-Sculpt Hao et al. (2024)and Portrait3D Wu et al. (2024) attempted to refine the 3D head inverted from 3D-aware GAN by Score Distillation Sampling, thereby avoid- ing dependency on training data, but this leads to color oversaturation and loss of fine details.
Human Head Portrait Datasets Generative models require large-scale, high-quality portrait datasets to ensure diverse and realistic outputs. While widely used datasets like FFHQ Karras et al.
4

---

Published as a conference paper at ICLR 2026
Front-iew CLI Image Feature
View-lavariaot Semaatic ( Condition
Imsgge quality
Serve
conditiona signal
"book"
Super-rosoutton
"bodesleff"
"oaderigh""
Freat-viww geseration
Flux.1 Kontext- - Multi-view
"left"
Head extimation
Inaages Generation
Near-front-view Facial Images
Crepping & Resize
Pane
Global Incoberent
Segmentattin
"rrnt-leff""
Frontul-sin Images
Are the genersted artifact- free and globally coherent?
"Frost right"
Selection
"Frone"
Image Filtering Agent
Multi-view Imaages
Figure 3: Overview of data generation pipeline. The first stage selects large number of near- near-front- front-view head and facial images, generates their corresponding front-view images using Flux. Kontext, and performs data preprocessing. In the second stage, similarly leverage Flux.1 Kontext with different fiew-angle prompts to extend the front-view images into multi-view collections. Finally, an image filtering agent based on Qwen2.5- VL is employed to remove images with artifacts global incoherence.
2018) and CelebA Liu et al. (2015) are available, they mainly contain near- frontal views and are insufficient for full-head generation. The LPFF dataset Wu et al. (2023a) offers broader view range but still lacks sufficient back-view images. The WildHead dataset Li et al. (2024) includes more back-view samples but suffers from uneven view distribution. This is largely due to natural variations in lighting, appearance, posture, and hairstyle. For example, certain hairstyles, such as Russian braids, are often only captured from the rear. Such imbalances inevitable in real-world data.
3 BALANCEHEAD360 DATASET
3.1 DATA GENERATION PIPELINE
As shown in fig. 3 our data generation pipeline consists of stages. First, we collect approxi- mately 350k high-quality near-front-view facial images from public datasets such as FFHQ Kazemi & Sullivan 2)14), CelebA [iu al. (2015). WildHead Li et al. (2024), and FaceCaption Dai et al. (2024). After preprocessing, we use Flux. Kontext Batifol et al. (2025) to generate a frontal-view counterpart for each image. the second stage, the same model is used to expand the frontal view into multiple perspectives using different view prompts. The CL.P feature extracted from the gen- crated front-view image serves as the shared conditional label for all extended multi-view images. For detailed technical operations, including the hyperparameters and specific prompts used for all models presented in this section, please refer to the supplementary material.
Front View Images Synthesis For each input near-front-view real image, first use Hyper- IQA Su et al. (2020) to estimate image quality. Images with a score above 60 are retained, while those scoring below 35 directly discarded. Images falling between these thresholds
fur- ther enhanced using a super-resolution model HYPIR Lin et al. (2025). We then employ Flux. Kontext Batifol et al. (025) generate multiple frontal-view images of the same subject while preserving their appearance and removing the background and non-central persons. We use VG- GHeads Kupynn al. (2024) to estimate head pose and crop the image to center-align the head. We then use DAVID Saleh et al. (2025) to obtain the mask of the foreground person. Next, we filter out images with an absolute yaw angle greater than 10". For the remaining ones, we extract identity features using ArcFace Deng al. (2019) and select the image with the smallest cosine similarity to the original as the synthesized front view image.
Multi-view Images Synthesis In this stage, leverage the strong viewpoint control and iden- tity preservation of Flux.1 Kontext. Given front-view image generated in the previous stage, we provide different view prompts to generate images from various perspectives while preserving the
5

---

Published as a conference paper at ICLR 2026
cond cam
VICICo Loss
Caam
Positive Real Samples
Negative Real Samples
Csem
sem
Team
cam
G
Gaem
D
View-invadian Canm
Taam
Fake
Semantic Condition
hy-plane
retddr cam
Generated Data
Figure 4: The Overview of our BalanceHead pipeline.
subject's appearance. However, due the inherent instability of 2D generative models, the out- put occasionally contains artifacts or fails to maintain consistent appearance across views. To filter such problematic images, we employ an image filtering agent based on Qwen2.5-VLL (202).. This agent effectively removes corrupted images and reduces cases where the subject's appearance changes significantly. Finally, we perform head pose estimation, cropping, and mask extraction on the images using the methods described above.
et al.
3.2 ROBUSTNESS OF 3D-AWARE GANS TO IMPERFECT MULTI-VIEW DATA
is important to note that although the generated multi-view data do not strictly guarantee 3D consistency, find this does not hinder training. First, during training, treat each view as in- dependent input without enforcing explicit multi-view supervision. Second, 3awaw GANs learn through adversarial training and result, most 2D artifacts become hard examples for the generator, as they are difficult to reproduce using geometric representations in the tri-planes. Consequently, the generator naturally avoids learn- ing these outlier samples. Therefore, 3D-aware GANs are well-suited for leveraging data from 2D models, benefiting from their high quality, controllability, and strong appearance consistency, while remaining robust to occasional artifacts and preserving 3D coherence.
tripplane-like representation encode 3D structures. As
3.3 BALANCBHEAD360 DATASET OVERVIEW
Via the aforementioned data generation process, we construct synthetic dataset containing 11.2 million 360 full-view head images with condition labels. The entire data generation and processing pipeline was executed on a computing cluster with 400 Nvidia A A10 GPUs over period of 26 days.
We name this dataset BalanceHead336) it addresses the imbalance in real-world data used by prior works. In our dataset, the distribution of image quantity, quality, and diversity is balanced across all viewing directions, significantly alleviating the supervision bias observed in SphereHead. Moreover, each subject shares a consistent semantic condition across views, ensuring that the distribution of each condition remains uniform across all perspectives. The inclusion of full-view images for every condition enables effective training with 3D-aware GANs.
Additionally, our data generation pipeline leverages large number of near-front-view real images and extends them to multiple views, resulting in generated images that follow natural distribution. Despite being synthetic, the dataset avoids imposing an artificial distribution, which enhances its generalization to real-world data. This design also enables significant scalability by making use of the abundant existing frontal head images.
SEMANTIC-CONDITIONE 3D- AWARE GANS
4.1 DEFINITION
Unlike previous unconditional 3D-aware GANs and those conditioned on view angles, propose a new class of 3D-aware GANs that use niew-invariant semantic features as conditions. This design eliminates view-dependent cues from the conditioning signal, thereby breaking the correlation be- tween generation capability and specific views and effectively addressing the prevalent directional bias in prior methods. Furthermore, it ensures that the generator learns produce diverse outputs

---

Published as a conference paper at ICLR 2026
by aligning with the true semantic distribution of the data. By consolidating supervision across different views under a shared semantic condition, the model enforces semantic consistency across all perspectives for each generated sample, which enhances global coherence and improves training efficiency. We refer to this class of models as semantic-condition 3D 3D-aware GANs.
4.2 VIEW-INVARIANT SEMANTIC FEATURE
As previously analyzed, using view as a condition can introduce directional bias in the 3D heads generated by 3D-aware GANs. Therefore, aim derive view-invariant semantic feature from the images to serve as a new conditioning signal. Ideally, one could align the semantic features of all views of subject, after removing their respective view-specific information, tion that is shared across all perspectives. However, in practice, it is highly challenging to eliminate view-specific content from each image, different views contain distinct visual information: for example, frontal views capture rich facial features but lack hair details, while rear views emphasize hair but miss facial features.
central representa-
Since it is infeasible to remove view-specific information from individual images, we instead anchor all views to a single reference image, allowing them share a common condition and thus decou- pling the condition from any specific viewpoint. Although no single view captures 100% of the full-head semantic information, the frontal view provides the most comprehensive signal among all viewpoints. It fully captures the facial region, which is most critical to human perception, and also conveys substantial global cues about hairstyle, hair color, clothing, and overall appearance. There- fore, we choose the CLIP image feature of the front view as the shared view-invariant semantic information across all views, thereby avoiding directional bias in the generation process.
4.3 VIEW-IMAGE AND CONDITION-IMAGE CONSISTENCY Loss (VICICO Loss)
Counterintuitively, despite our model being trained on view-balanced data, we occasionally observed multiple-face artifacts in the back head region, which are reported by SphereHead. However, unlike in SphereHead where such artifacts appear early in training, they typically emerge only at later stages in our experiments, suggesting different underlying cause. Through visualization analysis of checkpoints before and after the artifact occurrence, we found that these artifacts likely arise when the generator struggles to learn complex hairstyles, accessories, or hats due to their high diversity and large geometric and color variations. In this process, the generator may degenerate into generating meaningless floating color patches. As face-like patterns are easier for the discriminator accept, the model eventually generates facial features within these patches, leading to the multiple-face artifacts.
Fortunately. our model remains compatible with the ViCo loss proposed by SphereHead, which enforces consistency between image content and view information through the discriminator. Al- though this involves view-related signals, the used views are randomly rendered and independent of the semantic condition, thus avoiding directional bias. Building upon this idea, we construct negative pairs based on view-invariant semantic conditions, enabling the discriminator to jointly enforce consistency between image content and the semantic condition. This further strengthens the alignment between generated images and the true semantic distribution, enhancing both fidelity and diversity. We combine this with the ViCo loss to form a new loss function, View-image and Condition-iman image Consistency Loss (ViCiCo Loss), which is formulated as follows:
Lvicio. log(1 -D((+ + ,,,,,,,,1 1 (T'am 6ye)))
As illustrated in fig. 4 for batch of real data { I (ream Csem }. randomly shuffle either the camera label Feam. the semantic condition Csem. or both to form a shuffled pair (r'ams (sem) The resulting negative pairs {(T+, T, ITm), (r'am>> <gmm) } are then fed into the discriminator D.
4.4 BALANCEHEAD
We integrate the above technical innovations into semantic-conditiona full-head 3D-aware GAN pipeline, BalanceHead, as shown in fig. 4 Building upon recent state-of-th thte--of-hheann art Hy HyPlancHead model, generator employs StyleGAN2 the backbone and uses hy-plane representation to

---

Published as a conference paper at ICLR 2026
Yaw: 0"
45
90"
135'
180"
Yaw: 0
45'
90"
135'
180"
(a)
(h)
(b)
i
(c)
i
(d)
(k)
(e)
(L)
(f)
(m)
(g)
(n)
Figure 5: Qualitative comparison with state-of-the art methods. Conditioned on front-view: (a) EG3D
(b) GGHead (c) Panolead (d) SphereHead. (e) HyPlaneHeead conditioned on back-view. (f) view-
conditional baseline conditioned on back-view. (g) Our nle-semantic-condiin baseline conditioned on
side-view. (h-n) Our BalanceHeaa conditioned on view-invariant semantic condition.
encode 3D head geometry. The pipeline first renders low-resolution images / via volume render- ing and then applies super-resolution module generate high-resolution images I+ along with corresponding masks - In contrast to previous methods, we condition the generator using view-invariant semantic feature Cem instead of camera view information Ceam, and incorporate the proposed ViCiCo loss to suppress multiple-face artifacts and ensure consistency between the gener- ated output and the semantic condition. All these design choices collectively enable BalanceHead to achieve high-quality, diverse, and generalizable full-head generation with minimal artifacts.
5 EXPERIMENTS
The model was trained eight NVIDIA H20 GPUs using batch size of 32. Both the training and synthesized images have a resolution of 512 512. The training process, which lasted days, exposed the model total of 32 million images from our BalanceHead360 dataset.
5.1 360 FULL-HEAD SYNTHESIS
Qualitative Results We compare our HalanceHead with existing 3D-aware head GANs, including EG3D Chan al. (2022). GGHead Kirschstein al. (2024). PanoHead An al.] (2023) Sphere- Head Li et al. (202)), and HyPlaneHead Li et al. (2025). ) We also evaluate two ablation base- lines that share the same training settings as BalanceHead but differ in conditioning strategies: a view-conditional baseline that conventionally uses the view angle as condition, equivalent to Hy- PlaneHead trained on our dataset; and siew-semanti--codiiie feature of the current view instead of the corresponding front view as the conditioning signal. As shown in fig. 5 (a) EG3D and (b) GGHead fail to generate the back-head region, as they are trained near- front-view datasets and lack suitable 3D representations for non-frontal areas. (c) PanoHead frequently exhibits severe artifacts caused by feature penetration resulting from Cartesian coordinate projection. While (d) Seae addresses this issue with spherical tri-plane representation, its
baseline that uses the image clip
8

---

Published as a conference paper at ICLR 2026
Table Quantitative results
Condition
ViCiCo
FID-view + FID-random
FID-front
view
9.67
13.82
8.42
siensiemantic
8.63
46.24
5.90
semantic
4.45
4.11
semantic
3.67
3.51
suboptimal design limits the generator's capacity, resulting in blurry outputs with poor detail. (e) The recent SOTA method, HyPlaneHead, introduces a hybrid plane representation combining the strengths of tri-plane and spherical tri-plane structures, achieving high-quality results. However, as it still conditions on view angles like previous methods, it can only stably generate realistic sam- ples under front-view conditions. When non-front views are used as input, artifacts and distortions appear in non-conditional directions, limiting its diversity.
We train (f) HyPlaneHead our newly created 360 -bllanced dataset but still observe view-biased performance. To address this, we explore using the image clip feature of each view as a semantic condition. However, since these features inherently contain view-related information that is difficult to disentangle, the resulting outputs continue to exhibit directional bias.
In contrast, our BalanceHead aligns all views to the image CLIP feature of their front view as the conditioning signal, effectively climinating the haunted directional bias. Combined with our large- scale, -allnbaleeeed dataset, BalanceHead360, ally coherent outputs across all viewing angles for the first time. The learned 3D full-head space not only captures variations in facial expressions and attributes but also spans diverse identities includ- ing race, hairstyle, accessories, clothing, and headwear, demonstrating its potential for downstream tasks such 3D hair modeling and full-head 3D talking avatars.
method achieves stable, high-quality, and glob-
Quantitative Results We use three variants of the Fréchet Inception Distance (FID) score to quan- titatively compare our method with baselines. FID-view evaluates only the conditional view, which equivalent to the standard FID used prior works. However, it omits the quality and diversity of non-front views. To address this limitation, HyPlaneHead proposes FIlD-random, which renders images from random views to evaluate the overall 360 synthesis quality and diversity. This metric most relevant to our task. FID-front measures outputs conditioned and rendered from the front methods consistently produce
view. In this setting, both cie--conditional and stable, high-quality front-facing portraits, thus reflecting the identity diversity of the full-head space.
As shown in table [ using view or view-semantic as conditioning leads to significant directional bias, evidenced by the much higher FID-random compared to FID-view. In contrast, semantic con- ditioning achieves better quality and diversity, reflected in a significantly lower FID-random. The ViCiCo loss further improves consistency between the output and the condition, resulting in even lower FID-random. Even when evaluated only on the front view, our semantic conditioning com- bined with ViCiCo loss still demonstrates improved diversity.
5.2 SINGLE-VIEW GAN INVERSION
We compare single-view GAN inversion results with previous full-head 3D-aware GANs in fig. Specifically, perform single-view GAN inversion using Pivotal Tuning Inversion (PTI) Roich et al. (2022), which optimizes both the latent code w and generator parameters to match the target image via combination pixel-wise L2 loss and image-level LPIPS loss. PanoHead exhibits per- sistent face-like artifacts on the back head region, while SphereHead generates fewer artifacts but suffers from blurry outputs and lacks fine details such as hair tips and glasses geometry. HyPlane- Head produces better results overall but still struggles with uncommon hairstyles; for example, the unusual hair tips from the second row input are learned as background elements and do not rotate consistently with the head. All these methods tend to produce similar back head regions, primarily due to their imbalanced capability in capturing quality and diversity across views, resulting in lack view-invariant semantic feature.
of global coherence. In contrast, thanks to conditioning BalanceHead faithfully reconstructs 360 full-head images, appropriately filling in invisible back head regions and maintaining global coherence even for outliers.

---

Published as a conference paper at ICLR 2026
Targer Imagee
Targst Imsgge
Parnlesd
Turget Imagee
Splceical
Figure 6: Single-view 3D-aware GAN Inversion.
DISCUSSION
Trained on large-scale, high-quality images, our proposed 3D full-head model exhibits significant fidelity, diversity, and generalizability. These attributes position the model not only as potential foundation for specialized applications, such 3D talking heads and head editing, but also high- fidelity 2D/3D data generator capable of supporting downstream tasks like 3D head reconstruction.
Beyond its immediate applications, this work offers generative priors and 3D consistency. Specifically, our results demonstrate that fully 3D-consistent representations be effectively supervised by imperfect, 3D-in 3D-inconsistent multi-view data. This suggests a potential paradigm shift: rather than relying exclusively on strictly consistent (but ex- pensive) multi-view data purely single-view data (which often suffers from view imbalance), researchers can leverage the vast, though slightly inconsistent, output of powerful 2D genera- tors. key takeaway that future research should perhaps place greater emphasis on developing inconsistency-toleran 3D training strategies and robust semantic-conditioned models, rather than focusing solely on the pursuit of perfect consistency in the input data. Our method serves as ini- tial exploration in this direction, highlighting the critical role of in bridging the gap between 2D priors and 3D consistency.
perspective the synergy between 2D
semantic information
CONCLUSION
In this work, we address the limitations of view-conditioning in full-head 3D-aware GANs by in- troducing novel semantic-condition approach. Our method enables view-invariant generation through front-view-aligned semantic features and large-scale, balanced synthetic dataset. Exten- sive experiments demonstrate that our model achieves superior quality, diversity, and global coher- ence compared to existing methods.
ACKNOWLEDGEMENTS
The work was supported part by Guangdong S&T Programme with Grant No. 202480101030002. the Basic Research Project No. HZQB-KCZXZ.2021166 of Hetao Shenzhen-HK S&T Coopera- tion Zone, by Guangdong Provincial Outstanding Youth Fund with No. 2023B15150200 151000055, the Shenzhen Outstanding Talents Training Fund 202002, the NSFC with Grant No. 62293482, the Guangdong Research Projects No. 20172T07X152 and No. 2019CX01X104, the Guangdong Provincial Key Laboratory of Future Networks of Intelligence (Grant No. 2022B 022B11120100011 and the Shenzhen Key Laboratory of Big Data and Artificial Intelligence (Grant No. ASSG220242211138855888 the Guangdong Province Radio Science Data Center.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Sizhe An, Hongyi Xu, Yichun Shi, Guoxian Song, Umit Y Ogras, and Linjie Luo. Panohead: Geometry-aware 3d full-head synthesis in 360deg. In CVPR, 2023.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang. Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang. Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report. arXiv preprint 2rXiv:2502.1322. 2025.
Stephen Batifol, Andreas Blattmann, Frederie Boesel, Saksham Consul, Cyril Diagne, Tim Dock- hor,, Jack English, Zion English, Patrick Esser, Sumith Kulal, et al. Flux. kontext: Flow match- ing for in-context image generation and editing in latent space. arXiv e-prints urXiv:2506.15742 2025.
Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In SIGGRAPH, 1999.
Eric R Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In CVPR, 2021a.
Eric R. Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. pi-gan: Pe- riodic implicit generative adversarial networks for 3d-aware image synthesis. arXiv preprint arXiv:20/2.00922, 2021b.
Eric R Chan, Connor Z Lin, Matthew Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay. Sameh Khamis, et al. Efficient geometry-avee 3d generative adversarial networks. In CVPR, 2022.
Dawei Dai, YuTang Li, YingGe Liu, Mingming Jia, Zhang YuanHui, and Guoyin Wang. 15m multimodal facial image-text dataset. arXiv preprint arXiv:2407.08555, 2024.
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos 7afeiriou. Areface: Additive angular margin loss for deep face recognition. In CVPR, 2019.
Yu Deng, Jiaolong Yang, Jianfeng Xiang, and Xin Tong. Gram: Generative radiance manifolds for 3d-aware image generation. In CVPR, 2022.
Yu Deng, Duomin Wang, Xiaohang Ren, Xingyu Chen, and Baoyuan Wang. Portrait4d: Learning one-shot 4d head avatar synthesis using synthetic data. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pp. 7119-7130, 2024. 2024.
Matheus Gadelha, Subhransu Maji, and Rui Wang. 3d shape induction from 2d views of multiple objects. In 3DV, 2017.
Yuming Gu, Phong Tran, Yujian Zheng, Hongyi Xu, Heyuan Li, Adilbek Karmanov, and Hao Li. Diffportrait360: Consistent portrait diffusion for 360 view synthesis. arXiv preprint arXiv:2503. 2025.
Jinkun Hao, Junshu Tang, Jiangning Zhang, Ran Yi, Yijia Hong, Moran Li, Weijian Cao, Yating Wang, Chengjie Wang, and Lizhuang Ma. Id-sculpt: Id-aware 3d head generation from single in-the-wild portrait image. arXiv preprint arXiv:2406.16711 2024.
Ayaan Haque, Matthew Tancik, Alexei A Efros, Aleksander Holynski, and Angjoo Kanazawa. Instruct-nerffneeff: Editing 3d scenes with instructions. In Proceedings of the IEEE/CVF in- ternational conference on computer vision, pp. 19740-19750, 2023.
Chengan He, Junxuan Li, Tobias Kirschstein, Artem Sevastopolsky, Shunsuke Saito, Qingyang Tan, Javier Romero, Chen Cao, Holly Rushmeier, and Giljoo Nam. 3dgh: 3d head generation with composable hair and face. TOG, 2025.
Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:231 1.04400, 2023.
11

---

Published as a conference paper at ICLR 2026
Ruixiang Jiang, Can Wang, Jingbo Zhang, Menglei Chai, Mingming He, Dongdong Chen, and Jing Liao. Avatarcraft: Transforming text into neural human avatars with parameterized shape and pose control. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp- 14371-14382, 2023.
Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. arxiv e-prints. arXiv preprint arXiv: 2018.
Vahid Kazemi and Josephine Sullivan. One millisecond face alignment with an ensemble of regres- sion trees. In CVPR, 2014.
Tobias Kirschstein, Simon Giebenhain, Jiapeng Tang, Markos Georgopoulos, and Matthias Nießner. GGHead: Fast and Generalizable 3D Gaussian Heads. In SIGGRAPH Asia, 2024. ISBN 9798400711312. doi: 10.114573680528.3669 36677686..
URL https://6c1: org/ooggg.0.. 1145/
3680528 3687686
Orest Kupyn, Eugene Khvedchenia, and Christian Rupprecht. Vggheads: 3d multi head alignment with a large-scale synthetic dataset. arXiv preprint arXiv:2407. 18245, 2024.
Heyuan Li, Ce Chen, Tianhao Shi, Yuda Qiu, Sizhe An, Guanying Chen, and Xiaoguang Han. Spherehead: stable 3d full-head synthesis with spherical tri-plane representation. In ECCV, 2024.
Heyuan Li, Kenkun Liu, Lingteng Qiu, Qi Zuo, Keru Zheng, Zilong Dong, and Xiaoguang Han. Hyplanchead: Rethinking tri-plane-like representations in full-head image synthesis. NeurIPS, 2025.
Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. Learning model of facial shape and expression from 4D scans. SIGGRAPH Asia, 2017. URL httptp:://pi..//t//t/.pt
1145/3133 313310000031110 3138813
Tingting Liao, Yujian Zheng, Adilbek Karmanov, Liwen Hu, Leyang Jin, Yuliang Xiu, and Hao Li. Soap: Style-omniscient animatable portraits. arXiv preprint 25X5v22505.05022, 2025.
Yiyi Liao, Katja Schwarz, Lars Mescheder, and Andreas Geiger. Towards unsupervised learning of generative models for 3d controllable image synthesis. In CVPR, 2020.
Xinqi Lin, Fanghua Yu, Jinfan Hu, Zhiyuan You, Wu Shi, Jimmy Ren, Jinjin Gu, and Chao Dong. Harnessing diffusion-yielled score priors for image restoration. arXiv preprint arXiv:2507.20550 2025.
Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 9298-9309, 2023.
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In ICCV, 2015.
Zhiyuan Ma, Xiangyu Zhu, Guo-Jun Qi, Zhen Lei, and Lei Zhang. Otavatar: One-shot talking face avatar with controllable tri-plane rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16901-16910, 2023.
Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang. Hologan: Unsupervised learning of 3d representations from natural images. ICCV, 2019.
Thu H Nguyen-Phuoc, Christian Richardt, Long Mai, Yongliang Yang, and Niloy Mitra. Blockgan: Learning 3d object-aware scene representations from unlabelled images. NeurIPS, 2020.
Roy Or-El, Xuan Luo, Mengyi Shan, Eli Shechtman, Jeong Joon Park, and Ira Kemelmacher- Shlizerman. Stylesdf: High-resolution 3d-consistent image and geometry generation. In CVPR, 2022.
Lingteng Qiu, Xiaodong Gu, Peihao Li, Qi Zuo, Weichao Shen, Junfei Zhang, Kejie Qiu, Weihao Yuan, Guanying Chen, Zilong Dong, al. Lhm: Large animatable human reconstruction model for single image to in seconds. In Proceedings of the IEEE/CVF International Conference on Computer Vision pp. 14184-14194, 2025a
12

---

Published as a conference paper at ICLR 2026
Lingteng Qiu, Peihao Li, Qi Zuo, Xiaodong Gu, Yuan Dong. Weihao Yuan, Siyu Zhu. Xiaoguang Han, Guanying Chen, and Zilong Dong. Pf-lhm: 3d animatable avatar reconstruction from pose- free articulated human images. arXiv preprint arXiv:2506.13766, 2025b.
Daniel Roich, Ron Mokady, Amit Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. TOG, 2022.
Fatemeh Salch, Sadegh Aliakbarian, Charlie Hewitt, Lohit Petikam, Xiao-Xian, Antonio Criminisi, Thomas J. Cashman, and Tadas Baltrušaitis. DAViD: Data-efficient and accurate vision models from synthetic data, arXiv preprint arXiv:2507.15365 2025.
Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger. Graf: Generative radiance fields for 3d-aware image synthesis. NeurIPS, 2020.
Yichun Shi, Divyansh Aggarwal, and Anil K Jain. Lifting 2d stylegan for 3d-aware face generation. In CVPR, 2021.
Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang. Blindly assess image quality in the wild guided by self-adaptive hyper network. In CVPR, 2020.
Jingxiang Sun, Xuan Wang. Yichun Shi, Lizhen Wang, Jue Wang, and Yebin Liu. Ide-3d: Interactive disentangled editing for high-resolution 3d-aware portrait synthesis. TOG, 2022. doi: 10.1145/
3550454.3555506
Jingxiang Sun, Xuan Wang, Lizhen Wang. Xiaoyu Li, Yong Zhang, Hongwen Zhang, and Yebin Liu. Next3d: Generative neural texture rasterization for 3d-aware head avatars. In CVPR, 2023.
Attila Szabó, Givi Meishvili, and Paolo Favaro. Unsupervised generative 3d shape learning from natural images. arXiv preprint arXiv: /0fi::////0008// 002877 2019.
Shuai Tan, Bin Ji, Mengxiao Bi, and Ye Pan. Edtalk: Efficient disentanglement for emotional talking head synthesis. In European Conference on Computer Vision, pp. 398-416. Springer, 2024a.
Shuai Tan, Bin Ji, and Ye Pan. Flowvqtalker: High-quality emotional talking face generation through normalizing flow and quantization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 26317-26327, 2024b.
Shuai Tan, Bill Gong, Bin Ji, and Ye Pan. Fixtalk: Taming identity leakage for high-qualit talking head generation in extreme cases. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pp. 24-3,,1 2025.
Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. European Conference on Computer Vision, pp. 1-18. Springer, 2024.
Tengfei Wang, Bo Zhang, Ting Zhang, Shuyang Gu, Jianmin Bao, Tadas Baltrusaitis, Jingjing Shen, Dong Chen, Fang Wen, Qifeng Chen, et al. Rodin: A generative model for sculpting 3d digital avatars using diffusion. In CVPR, 2023.
Yiqian Wu, Jing Zhang, Hongbo Fu, and Xiaogang Jin. Lpff: A portrait dataset for face generators across large poses. In ICCV, 2023a.
Yiqian Wu, Hao Xu, Xiangjun Tang, Xien Chen, Siyu Tang. Zhebin Zhang, Chen Li, and Xiaogang Jin. Portrait3d: Text- Text-guided high-quality 3d portrait generation using pyramid representation and gans prior. TOG, 2024.
Yue Wu, Sicheng Xu, Jianfeng Xiang, Fangyun Wei, Qifeng Chen, Jiaolong Yang, and Xin Tong. Aniportraitgan: animatable 3d portrait generation from 2d image collections. In SIGGRAPH Asia 2023 Conference Papers. pp. 1-9, 2023b.
Shiqi Xin, Xiaolin Zhang, Yanbin Liu, Peng Zhang, and Caifeng Shan. Avatarback: Back-head generation for complete 3d avatars from front-vien view images. arXiv preprint arXiv:2508.20623, 2025.
13

---

Published as a conference paper at ICLR 2026
Dingyun Zhang, Heyuan Li, and Juyong Zhang. sero-shot real facial attribute separation and transfer at novel views. International Conference on Computational Visual Media, pp. 3-26. Springer, 2024.
Longwen Zhang, Qiwei Qiu, Hongyang Lin, Qixuan Zhang, Cheng Shi, Wei Yang, Ye Shi, Sibei Yang, Lan Xu, and Jingyi Yu. Dreamface: Progressive generation of animatable 3d faces under text guidance. arXiv preprint 20411
14

---

Published as a conference paper at ICLR 2026
A APPENDIX
A.1 USE OF LLMS
We acknowledge the use of perneae-purpose Large Language Models (LLMs), such OpenAI's ChatGPT, as tool for refining the manuscript. Their role was confined to improving grammatical accuracy, stylistic flow, and clarity. The core intellectual contributions, including the research prob- lem, the design of the BalanceHead framework, and the ViCiCo loss, were developed and executed solely by the human authors. This manuscript's scientific narrative, structure, and all claims are the original work of the authors.
A.2 DATA GENERATION PIPELINE DETAILS
We detail the configuration of the data generation pipeline in this section.
For both synthesis processes, set the Classifier-Free Guidance (CFG) scale to 1.0 and the denois- ing steps to 20. To ensure the quality of the generated images, we designed specific positive and negative prompts, as illustrated in Fig
Positive Prompt
Negative Prompt
A 4k HD candid photo of person [view]. centered compasition, remove other people, pure white backgmund, natural shoulder posture, only one person visible, no other people, no obstructions, highly realistic hairstyle, subtle and highly detailed hair strands, photorealistic, keep original features intact, maintain identity, maintain expression, enhance this person using super-resolution, natural skin, low suturation, retouching, photographed with DSLR, natural lighting, unstylized. higb-resolution. true-to-life, studio-style portrait, remove unnatural edges caused by mirror padding, replicate padding. and edge padding.
obstructions, occlusions, split image, collage, multiple people, extra people, oceluded head, head covered. object in front of face, hand, microphone, acne, pimples, bad skin, oily skin, zits, blackheads, skin blemishes, cluttered background. busy background, text, logo, watermack, low resolution, bad composition, messy background, poor lighting, multiple views, three views, two views, bad anatomy, distorted face, blurred face, out of focus, monochrome, artifacts, moise, blurry, distigured, deformed, extra limbs, text, signature, unnatural edges, mirror padding, replicate padding, edge padding.
"facing forward, front view", "throe-quarter view. 45-degree angle, partial side, yaw-45 dogree", "side view, lateral view, profile view, yaw-90 degree", "back-side view, rear-left, yaw-135 degree".
"left profile, left side view". "right profile, right side view",
[view]
"left-rear view, back-left",
"back-side view, rear-right, yaw-135 degree". "back view, rear view, posterior view, yaw-180 degree",
"right-rear view, rich-righi"
Figure 7: Prompts in FLUX.1 Kontext. We use different key words in [view] (bottom in the figure) to control the pose synthesis head.
The image synthesis process. particularly for multi-view image synthesis, may suffer from identity distortion and visual artifacts. To address this, we developed a two-stage image filtering process to clean the synthesized head images, leveraging Qwen2.5-Vil comprehensive visual-language model (VLM).
In general, we classify the synthetic head images into two categories based on the complexity of head attributes, including hair, clothing, and accessories. For each generated multi-view image, we compare it with the corresponding front-view image to decide whether to retain or discard it.
In the first stage, we focus on categorizing the images. We define "Standard Images" and "Non- Standard Images" shown in Fig g
In the second stage, we determine whether the synthesized image contains severe artifacts or dis- crepancies that could indicate image manipulation inauthenticity. First, we designed prompts for detecting severe artifacts, such as severe distortion of facial features, visible blending/merrgin errors between the face and background, and more. Then, developed specific prompts for Standard and StnnSttndard images respectively (see IO) to verify whether the synthetic multi-view image shares identical features with the conditional front-view image.
15

---

Published as a conference paper at ICLR 2026
Classification Prompt
Please determine whether this image falls under 'Standard Image' 'Non-Standard Image' based the following criteria.
Standard Image Characteristies:
Minimal head rotation (within =15"), natural expression, consistent facial feature alignment, natural hair flow, clear facial details, high image quality, and good facial symmetry.
The head should not show noticeable tilt or excessive rotation. Facial features should be aligned consisteatly across images, with no asymmetry
hairstyle. The image should be well-lit, and the facial features should be clear without obstruction. For example, person smiling with natural posture, face aligned, and hair flowing naturally without distortion misalignment due head tilt would qualify as standard image.
In terms of accessories, items such glasses, earring,, or hats are present they should not dominate the image or obscure the face. the accessories occupy large portion of the image, such as covering the eyes or cars significantl, the image should classified as 'N Non-standard Image'. Accessories should secondary focus. not taking away from clarity and alignment of face and head the image.
Non-standard Image Characteristics:
Significant head rotation (exceeding a 3,30), pronounced facial expression changes, misaligned or asymanetrical facial features, hairstyle inconsistent with head ungle, obscured or incomplete facial details, poor image quality, and poor facial symmetry.
misaligned facial features, exaggerated expressions, or misalignment due head movement, should be classified non-standurd. Also, if the image quality is poor facial details are hidden unclear, should be categorized as non-standard.
Images exaggerated tilts rotations,
Additionally, if accessories like glasses, carrings, or hats occupy substantial part of the image, such that they obscure the face or distort the natural uppearance, the image should be classified as 'Non-stundard Image'. For example, if the glasses are large and cover the eyes or the hat is too big and hides part of the face, this should be considered a significant inconsistency.
Please respond with only: 'Standard Image' 'Non standard Image', and followed concise sentence explaining the key reason.
Figure 8: Classification Prompts in Qwen2.5-VL VL.
Artifact Perception Prompt
Please determine whether this image contains severe artifacts or discrepancies that may indicate image manipulation or inauthenticity.
Severe Artifacts Criteria:
image shows any unnatural distortions such as:
Severe distertion of facial features proportions, such faces with unnatural distortions, abnornal rolations, disproportionate facial features. This includes instances where heads appear unusually large small compared to the body. Inconsisteut textures or surface abnormalities, such as overly smooth, overly sharp areas, or unnatural pixelation that suggests image manipulation.
Unnatural head-to-body proportions, such as or facial features being disproportionately large, small, or misaligned with the body.
Visible blending or merging errors between the face and background. where facial features (such as hair, accessories) become difficult to distinguish from the surrounding areas background. Artifacts involving multiple faces or surreal features, where more than one head or body part appears in the image. indicating manipulation.
Distortion or of clarity in or accessories, such as overly stretched or distorted hair, or accessories appear unnaturally merged with the face head.
Unnatural facial expressions, such awkward stiff smiles expressions that of place. Unnatural transitions between the head and neck, resulting in disjointed distorted body proportions, misalignment between the head and neck.
any of these severe artifucts are detected, respond directly with: severe artifacts. severe artifacts are present image appears normal, respond with: normal.
Figure 9: Artifact Perception Prompts in Qwen2.5-Vi VL.
A.3 FAILURE CASES ANALYSIS
Although method is generally stable and produces high- samples in the vast majority of cases, there are still few failure cases under certain conditions. We identify two main types of failures:
16

---

Published as a conference paper at ICLR 2026
Matching Prompt for Standard Image
Matching Prompt for Non- Standard Image
Determine two images depict the same person:
Please determine if the images depict the person, focusing on the consistency of hairstyle, clothing, gender, and skin tone.
Minor differences hairstyle, clothing, and accessories
allowed (such slight changes in huirstyle volume clothing wrinkles);
Ensure the head pase and hair direction are naturally aligned, but minor variations in flow or positioning are acceptable.
gender and skin tone must he completely consistent; important aecessories (glasses, hats, etc.) must not be added or removed:
If the face obscured, make comprehensive judgment based fentures such as hairstyle, accessories, or clothing. Differences caused by angle, lighting. or slight pose variations should be considered normal variations. Image attributes: {attrs]] Image attributes: (attes2)
there should rotation and hairstyle direction.
obvious contradiction between head
When the face is partially obscured, comprehensive judgment can be made based on overall features. Image A attributes: [attrs]]
Image attributes: [attrs2]
Output format:
YES or NO, followed by one concise
Ourput format: YES NO, followed by concise sentence explaining the key reason.
sentence explaining the key reason.
Figure 10: Matching Prompts in Qwen2.5-VL.
First, as shown in fig. 13 (a,b), we occasionally observe abnormal shoulder postures. This is primar- ily due to the fact that this work focuses on 3D full-head generation, and it is difficult to precisely control the shoulder pose when using Flux to generate multi-view images from frontal inputs via prompts. As result, the generated multi-view images often exhibit significant variations in shoul- der posture. However, during training, these images with different shoulder poses share the same semantic condition, which can lead the model to attempt to generate multiple conflicting shoulder configurations under the same condition. This results in unrealistic or distorted shoulder shapes in the output.
Second, some rare hairstyles or accessories (e.g., hats) are challenging to learn due to their high diversity and limited availability in the dataset. In such cases, the model may produce implausible outputs when generating under those semantic conditions, as illustrated by the unrealistic accessory in fig. (c).
In addition, we observed extremely rare instances where the generated head is incomplete, as shown in fig. [3) (d). This may be attributed to segmentation errors small number of training images. When the model uses semantic conditions derived from such mis-segmented images, it tends to produce incomplete head structures.
Interestingly, despite these failures, the overall head shape remains realistic in most cases, indicating that our BalanceHead model has strong generalization capabilities. Even under outlier semantic conditions, the model is able to generate reasonable 3D head shapes with minor imperfections.
A.4 SUPPLEMENTARY VIDEO
We provide rendered videos of 3D human heads generated by our model for the readers' reference. In total, there are 5 videos. samples www.srrrupp.... mp4 to samples www.op.p.... 4 mpp show 3D hu- man heads randomly sampled from our BalanceHead model. We provide rendering videos covering 360-degree views, including changes in pitch angle. Each video contains either 12 or 24 samples.
In addition, we provide a video called interpolations between different randomly sampled 3D human heads from our model. Similarly, it includes ren- dering videos with 360-degree views and pitch angle variations.
mp4, which visualizes interpolations
For comparison, we include three videos with results from baselines:
basel bas.r.a..hyyyaannaaaaam...aaaaac baseline_oee
and wttp://wwww.....iiii...... .mp4. These videos generated using the official pretrained models under random-view conditioning, rather than the fixed front-view conditioning used in their original papers and demos. All results are produced with
random seeds from 0 to 31, without any cherry-picking
17

---

Published as a conference paper at ICLR 2026
By comparing these videos, make the following observations: First, the outputs from these three models are highly unstable. Some samples appear artifact-free, typically when a frontal view is randomly selected as the conditioning imput: while others exhibit severe distortions or obvious arti- facts, usually caused by non-frontal conditional views. Second, the diversity identity, expression, hairstyle, and other attributes is limited. Although the appearances vary across samples, their derlying 3D geometry remains strikingly similar. Third, many distorted faces look nearly identical. This occurs because, view-conditioned 3D-aware GANs, the generator's is capacity and output di- versity are strongly dependent on the conditioning view: diversity is high in the conditioned view but significantly reduced in non-conditioned views. Consequently, for samples conditioned on back views, the back sides may differ, but their front views often converge to similar appearances.
In contrast, our method is entirely free from this issue. By conditioning on view-invariant semantic feature, we prevent any view-specific information from leaking into the generator during training. As result, our model achieves consistent quality and diversity across all viewing directions, effectively resolving the view-dependency problem.
A.5 MORE RANDOM SAMPLINGS
fig. 13 shows additional randomly sampled full-head images from the latent space of BalanceHead.
Each group contains six images, rendered at yaw angles of 0º, 45%, 90", 135%, and 180%, with the last image displayed as a randomly sampled view.
A.6 DISCUSSION
A.6.1 IMPACT OF DATASET SIZE AND MULTI-VIEW IMAGES
Our method uses the CLIP feature of the front-view image as the semantic condition, so cor- responding frontal view is required for every training sample. Moreover, the multi-view collection ensures consistent data distributions across viewpoints, e.g., attributes such as ethnicity and hairstyle appear with similar frequencies in all views. This consistency helps the model generate 3D heads that are globally coherent. Therefore, using multi-view images is necessary for our method.
Regarding dataset scale, larger data primarily improves generation quality for rare cases. For exam- ple, with IM training images, common head appearances are already well modeled, but rare styles (e.g.. braids or hats) often lead to artifacts, because some specific hat or hairstyle variants appear in only dozens of training images. When scaling up to 11.2M images, the model sees significantly more examples of these rare cases, leading to markedly better and more robust generation.
A.6.2 NON-FRONTAL SINGLE-VIER GAN INVERSION
Our method not limited strictly frontal images. Specifically, we perform single-view GAN inversion using Pivotal Tuning Inversion (PTI) Roich et al. (2022). which optimizes both the latent code w and generator parameters to match the target image via a combination of pixel-wise L2 loss and image-level LPIPS loss. Critically, this inversion process is independent of the semantic condition c, i.e., does not involve predicting or optimizing e. Therefore, like prior methods, our approach can, in principle, handle target images from any viewpoint.
However, practical limitation arises from head pose estimation: for large pose variations (e.g., profile views and back views), estimated poses are often inaccurate. And PTI relies on accurate head pose to align the rendered output with the target image. For this reason, frontal views are typically used to evaluate inversion performance, to avoid confounding factors from pose estimation errors. We also include additional inversion results on non-from con-frontal images in fig. [4 to demonstrate real-world applicability.
A.6.3 CONDITIONING VS. DATASET IMPROVEMENTS
The core contribution of our work is the use of a view-invariant semantic condition, rather than conditioning view as in prior work. This design is the key reason for method's success, which is why we highlight it in the title, not the dataset. To validate the necessity of this semantic conditioning, trained two ablation baselines the same FL.UX-generated dataset:
18

---

Published as a conference paper at ICLR 2026
Conditioning only view (i.e., camera parameters), in previous methods. This baseline aims to demonstrate that, even when trained on the new dataset, conventional view-based conditioning cannot resolve the directional bias and limited diversity issues.
Conditioning on both view and semantic features. This baseline aims to show that removing view information from the conditioning is necessary to achieve view- inwairaaiiant generation.
As shown in fig. Flf.g), both baselines exhibit strong view-dependent bias: they produce realistic results at the conditioned view but suffer from noticeable distortions at other views. Table I quan- tifies this trend: both achieve low FID the conditioned view (FID-view) but significantly higher FID on random views (FnD-random). These results confirm that even with the improved dataset, the view-invariant semantic condition is essential for consistent, high-quality 3D-aware generation across all viewpoints.
A.6.4 QUALITY OF FLUX- LLUX-GENRRATED IMAGES
We randomly visualize additional examples from the FLUX-generated dataset in fig, 12 of the re- vised version to better illustrate its quality and diversity. As shown, FLUUXI-Kontext generates non-frontal views that preserve key personal attributes from the frontal image, such as identity, ex- pression, hairstyle, and lighting. As discussed in section [32] while these images are not strictly multi-view renderings with perfect 3D consistency, they are sufficient to be compatible with our training pipeline, which ultimately produces 3D full-heads with strong 3D consistency.
A.6.5 ON THE NUMBER OF VIEWS AND DATASET DESIGN
Our method does not require a strictly multi-view dataset. The primary purpose of our data collection is to obtain front-view image CLIP features as the condition to train our view-invariant semantic- conditioned 3D-aware GAN. In fact, only single paired sample per identity is minimally required, because any non-frontal view along with its corresponding frontal image is sufficient for training. In practice, however, wC generate multiple views per frontal image for two key reasons:
Computational efficiency: Generating a frontal image satisfying our selection criteria is sig- nificantly more expensive than generating non-frontal views. As described in section 3.1 we need to select the best frontal candidate from multiple generations, making one frontal image cost several times more than single non-frontal view. To maximize data utility under limited compute, we generate as many non-frontal views as possible per valid frontal image, rather than just one.
Using multiple views per identity ensures that the data distri- bution (e.g.. hairstyle, ethnicity, expression) remains balanced across all viewpoints. For example, in an extreme case where short hair dominates the left views while long hair dom- inates the right views, the model may produce implausible "chimeric" heads, i.c. short on one side and long the other, which is also mentioned in SphereHead. Generating mul- tiple views per identity mitigates this risk, as all views preserve the same identity-related attribute distribution the frontal view.
Distributional consistency:
Finally, controlling view generation with FLUX. 1-oottext is inherently noisy. Even when prompt- ing for a specific view (e.g.. "left view (yaw=90")"), the model often outputs images at unintended yaw angles (e.g.. 30", 75", even 145"), and occasionally generates completely mismatched views (e.g.. front, back or right when "left" was requested). Thus, even if we restrict prompts to fewer directions (front, back, left, right), the actual output still spans a continuous range of poses, making it impractical to enforce a fixed, small set of discrete views.
A.6.6 CLARIFICATION ON VIEW SELECTION FOR SEMANTIC CONDITIONING
No single view captures 100% of the full-head semantic information. However, among all view- points, the frontal view provides the most comprehensive signal. It fully captures the facial region, which is most critical to human perception, and also conveys substantial global cues about hairstyle, hair color, clothing, and overall appearance. For example, if the frontal view shows red afro hairstyle, the back view is highly likely to show the same red afro, but not green ponytail. In other words, while the frontal view may miss some fine details (e.g., of- hair geometry), it still
19

---

Published as a conference paper at ICLR 2026
encodes the majority of delentity-releveee semantics, whereas side views capture significantly less, often lacking facial identity entirely. Therefore, we use the frontal view as the source of se- mantic conditioning, not because it is perfect, but because it is the most informative single view available.
A.6.7 SEMANTIC CONDITIONING FROM A SINGLE FRONTAL VIEW
We do not fuse semantic conditions from multiple views not because of inference constraints (PTI optimizes only the latent code wt without predicting or optimizing the condition c), but for the following key reasons:
Frontal view quality is significantly more reliable. Our data pipeline generates multiple frontal candidates and selects the one with the most accurate pose, highest visual quality, and strongest identity alignment with the original real image (see fig. 3 In contrast, frontal views suffer from unstable pose control (c.g.. requesting a left profile sometimes yields yaw 45 instead of 90%) and occasional identity drift artifacts, despite post- generation filtering. Thus, non-frontal views are less trustworthy as semantic sources.
Semantic consistency across views is anchored to the frontal image. All non-frontal views are generated from the selected frontal image using view prompts. This ensures minimal semantic drift between views. Using multiple independent views as conditions would in- troduce random inconsistencies (e.g., hairstyle, identity, expression mismatches), leading to conflicting semantic signals and unstable conditioning.
Computational and architectural overhead. Our generator uses a StyleGAN2 backbone, where the condition (a 51 512-dimension CLIP feature) and the random number are pro- jected into latent space W an MLP. Concatenating features from multiple views would drastically increase both parameter count and computational cost, with diminishing returns.
The frontal view is already the most informative single view. As explained in our response Section A6.6, while single view captures 100% of full-head semantics, the frontal view contains the majority of identity-critic information, including face, hairstyle, color, and global appearance, which makes it the optimal choice for semantic conditioning.
For these reasons, we use only the frontal view to extract the semantic condition. Content in sec- tion is also informative for this discussion.
A.6.8 WHY IS THE SHARED FRONT-VIEW IMAGE CLIP FEATURE VIEW-INVARIANT
In our context, "align" and "anchor" refer the process of using the frontal view corresponding to a given non-frontal image as the source for semantic conditioning. The logic is:
We need condition to facilitate training, will suffer from mode collapse, Fig. 2(a-c).
However, traditional view conditioning leads to the directional bias issue Fig, 2(d-i).
So, we need a view-invariant condition.
It is inherently difficult to extract truly view-invariant feature from single image, the content of any image inherently encodes its viewing perspective.
We use (align/anchor) the random view image's corresponding frontal-view image to ex- tract the feature the semantic feature.
Since all views derived from the same frontal image share the same semantic feature, this feature is de facto view-invariant.
Experiments prove that using such view-invariant semantic feature as the condition effec- tively prevents mode collapse and solves the directional bias issue.
Sections A.6.6 and A.6.7 further justify why the frontal view the optimal choice for anchoring, due to its richness in identity-critical information and stability under data generation pipeline.
20

---

Published as a conference paper at ICLR 2026
A.6.9 GENERALIZABILITY TO OTHER 3D-AWARE GENERATORS
All results presented in the paper, including ablation baselines, based on HyPlaneHead. How- ever, our proposed view-invariant semantic conditioning is compatible with earlier 3D-aware gen- erator architectures, such as tri-plane (EG3D), tri-grid (PanoHead), and single or dual spherical tri-plane (SphereHead), as they all share the same StyleGAN2- training pipeline, the only difference being their 3D representation.
We present qualitative and quantitative comparisons in fig. an] table 3 The results reveal consis- tent trends across different architectures: integrating our semantic conditioning effectively mitigates directional bias, similar to the behavior shown in fig. ddi)), and leads to significantly more diverse outputs.
As illustrated in fig. 16. all tri-plane-like representations benefit from our semantic conditioning, though they exhibit distinct characteristics. For tri-plane, when combined with our view-balanced dataset and semantic conditioning, the feature penetration issue is substantially alleviated. Never- theless, some results still occasionally display excessive symmetry, as seen in (a). Moreover, due to the limited capacity of the tri-plane representation, outputs frequently suffer from blurriness (b,c) and structural adhesion, particularly around the chin area, as in (d).
For tri-grid, the severe mirroring artifacts are largely eliminated and no longer visually apparent. However, subtle feature penetration can still lead to unnatural hair textures, particularly in the back- of-head region opposite the jawline, in (e). Furthermore, because tri-grid cannot be integrated with the unify-split strategy proposed in HyPlaneHead, inter-channel feature leakage introduces sev- eral characteristic artifacts. For instance, (f) exhibits crisscross, sehiing-et-llike patterns, while (g) shows horizontal ridges on the back of the head. These artifacts do not appear in our hy-plane- hy-plane-based main experiments. Additionally, some samples suffer from global incoherence. For example, in (h), the front of the hair appears purple while the back is black. We hypothesize that this stems from tri-grid's 's use of a larger number of feature planes, which makes maintaining cross-plane consis- tency more challenging. In contrast, hy-plane primarily encodes head information through a single spherical plane, thereby promoting better global coherence.
Interestingly, tri-grid also demonstrates notable strength: it produces significantly more stable shoulder regions without the artifacts described in section A.3 thanks to its enhanced representa- tional capacity in corner areas.
Finally, both single and dual spherical tri- tri-plane representations tend to generate coarser hair strands and weaker fine-grained details, as shown in (k-n). (i.j) In the single spherical variant, although seam artifacts become less pronounced, the representation capacity near the seam is constrained, often resulting in messy or distorted hair in the back-of-head region. (o.p) The dual spherical ver- sion, while more expressive, still occasionally exhibits disordered hair with horizontal and vertical striping, suggesting residual inter-channel feature penetration.
Besides, though at lower frequency, multiple-face artifacts persist across all representations. in- cluding HyPlane. We further validate that incorporating the ViCiCo loss quickly resolves the multiple-face artifacts (typically within a few hundred kimg), consistent with prior work. In sum- mary, our semantic conditioning generalizes well to various 3D generator backbones, consistently improving output quality and diversity. Our main experiments use the most advanced representation (HyPlaneHeead), which achieves the best overall performance.
1. BASELINES USING IDENTITY FEATURES FOR CONDITIONING
We introduce two additional identity-conditioned baselines to analyze the role of semantic condi- tioning in our framework.
Conditioning on the identity feature extracted from the current random-view image. Re- sults: FID-random: 16.37, FID-front: 4.88.
Conditioning the identity feature extracted from the corresponding front- viet image of the current random view. Results: FID-random: 5.06, FID-front: 4.38.
In the first baseline, the identity (ID) feature is less view-variant than the CLIP image feature but is not fully view-invariant. Specifically, for the same person, the IDD feature remains relatively sta-
21

---

Published as a conference paper at ICLR 2026
Table Comparison with state-of-the-art methods.
Method MKNND 1
PanoHead
SphereHead
HyPlaneHead
Ours
0.391 0.086
0.345
0.291
0.456
LPIPS 4
0.083
0.084
0.076
ble under near-frontal views; however, it changes dramatically under Moreover, ID feature extraction is highly sensitive image preprocessing, such cropping and rotation, and typically relies on a head detector and alignment model for stable estimation. Unfortu- nately, these models are not robust extreme poses and often fail entirely back views. To enable training on such views, we must forgo alignment as a compromise: when head detection fails, we directly feed the raw image into the ArcFace ID feature extractor. This makes the ID feature highly unstable across views. Since the ID feature implicitly encodes both semantic identity and residual view information, behaves similarly to the "view-semantic" conditioning in Table resulting in high FID-random. However, because the ID feature is stable in near-frontal views, it functions comparably to the frontal view CLIP feature those cases, yielding a low FID-front.
or back views.
In the second baseline, all non-front views of the same subject share the same front-view-derin vievvderived identity feature. This shared feature is effectively view-invariant and plays a role nearly identical to the shared CLIP semantic feature used in our main experiments, leading to low FID scores on both random and front views. However, since identity features primarily capture facial characteristics and pay little attention accessories such clothing, hats, or hairstyle, models conditioned them tend to generate blurry or incoherent reconstructions in these regions.
A.6.11 DIVERSITY COMPARISON
We compare our method against of-teoofthhe-rt approaches in terms of diversity. To mitigate the influence of outliers and ensure a robust evaluation, we adopt the Mean k-Nearest Neighbor Dis- iance (MKNND) with & = 5 in the ArcFace embedding space as our diversity metric. Specifically, let [2,),N denote the ArcFace identity features of N generated samples. For each sample 2, we compute its cosine distance to all other generated samples and identify its & nearest neighbors {zj JieN (i), where (i) is the set of indices of the k closest samples to z; (excluding itself). The MKNND is then defined as:
N
MKNND
dooa(Zr, (2,, 21)) JENZ(()
Σ
N
T
where deas (a, b) b| is the cosine distance between two feature vectors. A higher MKNND indicates that the generated identities are more dispersed and less clustered in the em- bedding space, reflecting greater identity diversity. As shown in table 2 our method achieves state- of-the-art performance diversity compared existing methods.
A.6.12 QUANTITATIVE GAN INVERSION COMPARISON
To quantitatively compare reconstruction fidelity with existing methods, we perform single-view GAN inversion on a test set of 100 in-the-wild images. We then compute the LPIPS distance using features extracted from the target image and the rendered image from the same viewpoint. Since methods like PTI optimize model parameters for detailed per-image fitting, as shown in table 2 and fig. 6, all approaches achieve visually realistic reconstructions the input viewpoint. Although the LPIPS scores are close, our method achieves the lowest value, demonstrating its superior reconstruc- tion fidelity and practicality in real-world scenarios.
A.6.13 NEAREST-NEIGH ANALYSIS
As shown in fig. 17 we perform nearest-neighbor analysis by visualizing, for each generated sample, the most similar real image from the training set. Similarity is measured using the cosine distance between the CLIP feature of the frontal view of the generated 3D head and the CLIP features of real training images. In every row, the left side is the generated image, and the right side is its nearest neighbor in the training set.
22

---

Published as a conference paper at ICLR 2026
A.6.14 FUTURE WORK
In this paper, have presented new state-of-the-art full-head generative model, demonstrating the significant potential of inconsistency-ooleaat 3D training strategies and robust semantic-conditionn models. By effectively leveraging data that lacks strict multi-view consistency. our approach markably enhances both generative quality and diversity. Looking ahead, we aim to extend these strategies beyond 3D-aware GANs to broader architectures, such as Large Reconstruction Models (oRn) et al. (2023): Tang al. (2024); Qiu et al. (2025a b) and diffusion-bassee models] Wang al. 2023); Liu et al. (2023). Furthermore, as foundational 3D full-head model, BalanceHead provides smooth and well-structured latent space that facilitates various downstream applications, including portrait animation and talking head synthesis Ma et al. (2023); Sun et al. (2023); Tan et al. (202bb) Deng et al. (2024); Wu al. (2023b); Tan al. [2024a] 2025). ) stylization /Zhang et al. 2023); ; Jiang et al. (2023). J, and editing Sun et al. (2022); Zhang et al. (2024); Haque et al. 2023
These directions will be the focus of our future research.
Table 3: Compatibility of semantic conditioning with different tri tri-plane- representations
Representation
tri-plane
tri-grid
single sph-tri-plane
dual sph-tri-plane
hy-plane
FID-random
5.46
4.42
4.85
5.27
3.67
23

---

Published as a conference paper at ICLR 2026
Start
Input near-front-view face image
Estimate image quality (Hyper (QA)
Yes
image
Score <55
No
Enhance (HYPIR)
Generate multiple frontal-view images (Flux.1 Kontext)
Estimate head pase & Crap (VGGHeads)
Extract foreground mask (DAViD)
No
Yaw angle & 10"?
Yes
Extract identity features (ArcTace (ce)
Select image with smallest cosine similarity
End
Figure 11: The Front-view Image Generation and Preprocessing Pipeline.
24

---

Published as a conference paper at ICLR 2026
original
front
Front-right
right
backriggt
bock
bscklleft
left
front-ieft
Figure 12: Samples from our training dataset generated by FLUX.1-Kontex. Images enclosed in gray bounding boxes exhibit global incoherence with their corresponding front-view references, while those in red bounding boxes contain severe visual artifacts.
(a)
(b)
(c)
(d)
Figure 13: Failure Cases.
25

---

Published as a conference paper at ICLR 2026
(a)
(b)
(c)
(d)
(g)
(h)
(i)
(k)
(1)
(m)
Target image
Yaw: 0"
45"
90"
135"
180"
Targetview
Figure 14: Single-view GAN inversion using Pivotal Tuning Inversion (PTI). (a-j) Inversion results for near-froo nean-frontal target images. (k-m) Inversion results for large-pose target images.
26

---

Published as a conference paper at ICLR 2026
Figure 15: Random Sampling Results.
27

---

Published as a conference paper at ICLR 2026
(a)
(b)
(c)
(d)
(e)
(h)
(k)
(1)
(m)
nn)
(0)
(p)
Yaw: 0"
45"
90'
135*
180"
Random vitt
Figure 16: Compatibility with other tri-plane-like representations. (a-d) Tri-plane representation. (e-h) Tri-grid representation. (i-l) Single spherical tri-plane representation. (m-p) Dual spherical tri-plane representation.
28

---

Published as a conference paper at ICLR 2026
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(k)
(I)
Yau:
45'
90
135'
100
Newrest Neighbor
Figure 17: Nearest-neighbor analysis. The left side is the generated image, and the right side is its nearest neighbor in the training set.
29


Reviews:
[{"review_id": "L1u0Siizj6", "reviewer": "Reviewer_WZgK", "summary": "The method produces a training procedure for better training of GANs for generating 3D human heads, which are always 3D-consistent and have a realistic appearance from all sides, including the back of the head, and do not feature artefacts such as Janus artefacts or symmetry breaking at the back of the head. The method is based on an observation that view conditioning is suboptimal for training such GANs, as it introduces instability and 3D-inconsistency. The authors propose semantic conditioning based on a CLIP feature of an image from a frontal view. The results are demonstrated as unconditional sampling visuals and FID-* metrics. The method requires multi-view data to be trained. To obtain such, the authors propose to use FLUX diffusion model to generate such multi-view data and a curation pipeline to filter out unrealistic samples.", "questions": "- How much do we benefit from the condition itself, not from switching to a new FLUX-generated dataset?\n    - If multi-view data is necessary for training of this method, one could do an ablation with respect to the size of the dataset and the number of views in it\n- How good are the images generated by FLUX? Probably a visual would help to understand it (except one example in Figure 3)\n- Even though the pitch is pretty clear, not sure why we can't apply it to the existing generator; do we also absolutely necessarily need an (orthogonal) contribution with FLUX data?\n    - There's an explanation for that in the Intro: \"Specifically, for a person’s multi-view images, we align the conditioning of all views to the semantic feature of the frontal view, since it contains the most comprehensive information\" --> multi-view data is rare --> we generate it.  Two questions regarding that:\n        - What does \"we align\" mean? (Or \"we anchor\" later in the text). This remains unclear after reading the whole manuscript too.\n        - Does this sentence mean that the method can only work when there are multi-view images of a person available during inference (to obtain a frontal image feature to condition the GAN)? Or is it a requirement only in training? If only in training, then how is inference performed when the image is not exactly frontal? Perhaps it would make sense to train another network that would try to predict the frontal-view CLIP feature from other views that are close to the frontal? And then no multi-view data would be required in training. \n- In any case, GAN inversion results of not exactly frontal images should be shown. Otherwise, the direct applicability of the method would be highly limited. \n- Do I understand correctly that all the experiments are done for HyPlaneHead only? What happens if the trick is applied to other generators? For instance, does the semantic conditioning fix the PanoHead's issue of symmetry breaking in the back of the head, or is SphereHead's ViCiCo loss still crucial to achieve that? \n- L269: \"Nvidia A10 GPU\" -- just to confirm: was A10 or A100 meant?\n- Will the dataset be released publicly?\n- L315: \"frontal views capture rich facial features but lack hair details, while rear views emphasize hair but miss facial features.\" And then, L320: \"Among all possible views, the front view contains the most comprehensive global information, because it includes not only the facial region, which is most sensitive to human perception, but also reflects overall appearance, clothing, and hairstyle.\" Isn't it illogical? Basically, first, the authors are saying that the back view is essential, and then they are saying that the frontal view contains enough info. I understand that the back view does not contain info (it's evident from the visuals), but the writing seems a bit misleading. \n- Why not stack the semantic info from several (predefined) views then? Is it because we don't have those in inference?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Paper story sounds generally reasonable. Very clear pitch right from the abstract. \n- The paper is interesting and, despite presenting a simple observation and a fix for it, has value for the community and provides an additional important insight into training GANs. For me, reading the paper was interesting mainly because of the observation about the GAN instability and a bit less about the proposed fix for it (see Weaknesses).\n- Image-flitering head is a smart idea that ensures high quality of the results. \n- Great unconditional sampling results, even for the failure cases in Fig. 12. Generally, the improvement in both metrics and visuals is very substantial.", "weaknesses": "- Not very clear how much the size of the dataset + presence of multi-view images in the dataset is actually helping.\n- Writing is often unclear. \n    - Teaser is also a bit unclear: the results there look just very similar to other generators, e.g., SphereHead. I think it would be clearer for the reader if some conditioning is also shown. Also, the geometry of the back of the head is not shown there, even though it's likely one of the strongest improvement points.\n    - My (subjective) feeling from the text is that LLMs might have rephrased it (as authors fairly acknowledge), but in a way that is sometimes hard to understand and with repeated claims/sentences (see Questions). Some things like absent spaces around an em-dash (\"—\") -- e.g. see L313) clearly reveal the lack of post-editing after the use of LLMs. \n- The fact that the frontal view feature is used means that the single-view GAN inversion can only be done for ideally frontal images, right? What happens if the head is at least slightly rotated? And when it's e.g. a profile view? \n    - How exactly was GAN inversion done? Was it latent code optimization, PTI, or anything else? \n- A bit raw experimental section (see Questions)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "P9YpovF3gK", "reviewer": "Reviewer_t6Lb", "summary": "This paper introduces a new conditioning method for full-head 3D GANs. Unlike previous work which uses camera view directions as the condition, this paper proposes to use semantic image features extracted from the frontal view images as the condition. To do so, the authors curated a large-scale synthetic portrait image dataset with balanced coverage of the view directions and identities, in which each identity has multi-view images generated with Flux. By training on this curated dataset with the proposed conditioning method, experiments report improved performance compared to existing 3D GAN methods.", "questions": "I do not have major questions regarding the manuscript, only a small question:\n1. Will the curated dataset be released? As far as I know, data curation should be the most time consuming and dirty part of this work. I’m glad to see the authors put lots of effort into solving it, and I believe the release of this dataset will facilitate further research in this community.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "[Originality]\n- How to improve the quality of 3D full head synthesis has been a long problem since the publication of EG3D, and the inherited view conditioning has been a quite annoying part. This paper proposes a new conditioning method under the help of current 2D image generation and editing models. Though the data are synthetic, they maintain a level of realism and help to learn a more geometrically consistent 3D GAN. I personally like this idea.\n\n[Quality]\n- This paper conducts common qualitative experiments regarding 3D GAN, including random sampling, novel view synthesis, GAN inversion, and interpolation. The results look plausible to me, especially regarding the hair details.\n\n[Clarity]\n- The manuscript is easy to follow and understand. If you have basic knowledge of 3D GANs such as EG3D, you should be able to follow the details and solutions proposed in this paper.", "weaknesses": "Overall I do not see significant weaknesses of the current manuscript.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "re8xx41J6U", "reviewer": "Reviewer_9wYw", "summary": "The paper introduces a novel approach for generating high-fidelity and diverse 3D human heads using Generative Adversarial Networks (GANs). The core contribution is the model, BalanceHead, which addresses the severe mode collapse that typically plagues unconditioned full-head 3D-aware GANs. The authors propose a novel conditioning strategy by integrating view-invariant semantic features into the model's training process. This conditioning is claimed to stabilize training and enable the generation of diverse, multi-view renderings and their corresponding 3D geometries", "questions": "1. Please include a comprehensive quantitative comparison against relevant SOTA full-head 3D-aware GAN models. This should include metrics such as FID (Fréchet Inception Distance), Diversity/Recall scores, and potentially LPIPS or KID across multiple common head datasets.\n2. Please compare the proposed view-invariant semantic features against simpler conditioning methods (e.g., using explicit pose/camera parameters alone, or using a simple identity embedding). And please  demonstrate the difference in performance (especially diversity/mode collapse) when using view-invariant versus view-variant features.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The work targets a well-known instability issue in 3D-aware GANs, specifically the severe mode collapse that results from a lack of proper conditioning. \n2. The idea of using view-invariant semantic features is a promising direction for decoupling identity/semantics from viewing pose, which should inherently lead to more stable and diverse generation compared to simpler conditioning methods.\n3. The  BalanceHead is shown to generate high-quality results, including random-view, multi-view renderings, and visualizations of the underlying 3D geometries, indicating successful 3D structure synthesis and view consistency.", "weaknesses": "1. Experiments do not offer a clear comparison to established State-of-the-Art full-head 3D-aware GANs. Without standard quantitative metrics (e.g., FID, diversity scores, LPIPS), the claimed high-fidelity and diversity are difficult to objectively verify.\n2. While the conditioning is claimed to be novel, the paper needs to demonstrate the necessity of using view-invariant semantic features specifically. Without an ablation comparing this approach to simpler, non-semantic conditioning or existing view-variant conditioning methods, the complexity of the proposed feature extraction is not fully justified.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "203MIIlAVZ", "reviewer": "Reviewer_VqNs", "summary": "The authors first create a dataset with a range of view angles using FLUX.1 Kontext - 10 multi-view head images. Therefore, they start with existing front-view datasets first, and then use FLUX.1 Kontext to predict the respective other views. While the 2D prior is not 3D consistent but it preserves the identity which is sufficient for training their 3D-aware GAN.\n\nThe paper then proposes a method similar to 3D-aware GAN head generator, in the spirit of existing works such as PI-GAN, EG3D, SphereHead, PanoHead, GGHead, HyPlane etc. The latter approach is the closest method. Here, the authors also propose a semantic-conditioned 3D-GAN head generator. The front view is used to anchor all image vies as a single reference image. Semantic features are then used as a condition – in their current versions the authors use CLIP as a feature embedding.\n\nOverall, the method is essentially HyPlane with a semantic conditioning, trained on a new MVS FLUX-generated multi-view dataset.", "questions": "Comparison are somewhat unfair as GGHead, PanoHea, and SphereHead are trained on different data.\n- What if the proposed method was trained on the same data as these baselines?\n- What if the baselines were trained on the proposed dataset?\n\nI would’ve also loved to see the comparisons in the video. At the moment, the only comparisons are shown in Figure 5, which is insufficient to properly evaluate the method. \n\nSomewhat optional but a nearest neighbor / novelty analysis with respect to the train images would be nice. I would expect this to work well though.\n\nThe writing in the quantitative results paragraph (end of 5.1) is a bit confusing; it would be great if it could be checked for clarity. For instance, what features are used for the FID evaluation? Are they used from the generated dataset? Could you please clarify.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- Nice results\n- The dataset is quite valuable (is it being released?)", "weaknesses": "- technically quite incremental over previous methods such as SphereHead or HyPlane\n- poor evaluation (see below)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)