HyPlaneHead: Rethinking Tri-plane-like Representations in Full-Head Image Synthesis
Authors: Heyuan Li
Kenkun Liu
Lingteng Qiu
Qi Zuo
Keru Zheng
Zilong Dong
Xiaoguang Han
Venue: NeurIPS 2025 poster ()
Keywords: 3d Head Generation; 3d-aware GANs; tri-plane representation
Primary area: applications
TL;DR: HyPlaneHead introduces a novel hybrid-plane representation that overcomes feature entanglement, inefficient mapping, and channel interference in tri-plane-based head image synthesis, achieving state-of-the-art results.

Abstract: Tri-plane-like representations have been widely adopted in 3D-aware GANs for head image synthesis and other 3D object/scene modeling tasks due to their efficiency. However, querying features via Cartesian coordinate projection often leads to feature entanglement, which results in mirroring artifacts. A recent work, SphereHead, attempted to address this issue by introducing spherical tri-planes based on a spherical coordinate system. While it successfully mitigates feature entanglement, SphereHead suffers from uneven mapping between the square feature maps and the spherical planes, leading to inefficient feature map utilization during rendering and difficulties in generating fine image details.Moreover, both tri-plane and spherical tri-plane representations share a subtle yet persistent issue: feature penetration across convolutional channels can cause interference between planes, particularly when one plane dominates the others (see Fig. 1). These challenges collectively prevent tri-plane-based methods from reaching their full potential. In this paper, we systematically analyze these problems for the first time and propose innovative solutions to address them. Specifically, we introduce a novel hybrid-plane (hy-plane for short) representation that combines the strengths of both planar and spherical planes while avoiding their respective drawbacks. We further enhance the spherical plane by replacing the conventional theta-phi warping with a novel near-equal-area warping strategy, which maximizes the effective utilization of the square feature map. In addition, our generator synthesizes a single-channel unified feature map instead of multiple feature maps in separate channels, thereby effectively eliminating feature penetration. With a series of technical improvements, our hy-plane representation enables our method, HyPlaneHead, to achieve state-of-the-art performance in full-head image synthesis.

HyPlaneHead: Rethinking Tri-plane-like Representations in Full-Head Image Synthesis
Heyuan Li" SSE, CUHK (Shenzhen)
Kenkun Liu SSE, CUHK (Shenzhen) kenkunliu@ling cuhk, edu, on
heyuanli@link. cuhk. edu. en
Lingteng Qiu Tongyi Lab, Alibaba Inc. qiulingteng qlt@alibaba-inn con
Qi Zuo Tongyi Lab, Alibaba Inc. muyuan z inc com
Keru Zheng
Zilong Dong
SSE, CUHK (Shenzhen)
Tongyi Lab, Alibaba Inc. list. dz1 com
keruzheng@link. cuhk. edu. cn
Xiaoguang Han SSE, CUHK (Shenzhen) FNii-Shenzhen
Guangdong Provincial Key Laboratory of Future Networks of Intelligence hauxiaoguanggoulx. edu. cn
Abstract
Tri-plane-like representations have been widely adopted in 3D-aware GANs for head image synthesis and other 3D object/scene modeling tasks due their ef- ficiency. However, querying features via Cartesian coordinate projection often leads feature entanglement, which results mirroring artifacts. A recent work, SphereHead, attempted to address this issue by introducing spherical tri-planes based spherical coordinate system. While it successfully mitigates feature en- tanglement, SphereHead suffers from uneven mapping between the square feature maps and the spherical planes, leading to inefficient feature map utilization during rendering and difficulties in generating fine image details. Moreover, both tri- tri-plane and spherical tri-plane representations share a subtle yet persistent issue: feature penetration across convolutional channels can cause interference between planes, particularly when one plane dominates the others (see fig. I These challenges collectively prevent tri-plane-based methods from reaching their full potential. this paper, we systematically analyze these problems for the first time and propose innovative solutions address them. Specifically, we introduce a novel hybrid-plane (hy-plane for short) representation that combines the strengths of both planar and spherical planes while avoiding their respective drawbacks. We further enhance the spherical plane by replacing the conventional theta-phi warping with a novel near-equal-arg
warping strategy, which maximizes the effective utilization of the square feature map. In addition, our generator synthesizes single-channel unified feature map instead of multiple feature maps in separate channels, thereby effectively eliminating feature penetration. With series of technical improve- ments, hy-plane representation enables our method, HyPlaneHead, to achieve state-of-thed performance in full-head image synthesis.
WWork done during internship at Tongyi Lab, Alibaba Inc. Project lead.
"Coresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(a) Tripplane
(b) Sphatical Thi-plane
(c) Hy-plane (Ours)
Pyz
3z
Figure 1: Figure (a. b, c) respectively illustrate the feature map visualizations and geometric structures of the tri-plane, spherical tri-plane, and our proposed hy-plane representation which integrates both planar and spherical planes. Figure (d) shows head geometry model for defining the coordinate system. Note that in b), the dominant planes (Pyz for tri-plane and Pay for spherical tri-plane) cause significant inter-channel feature penetration into the other planes (best viewed when zoomed in), thereby limiting the model's expressiveness. In contrast, (c) resolves this issue entirely by employing unify-split strategy, where all feature maps are generated within single channel. As a result, each plane effcctively learns its corresponding information without interference from other planes.
Introduction
Photorealistic full-head synthesis Zhuang et al. (0022) Park et al. (2021): Canela et al.) 20233) He
:
al. (2024) Doukas et al. (2021 stands cornerstone technology for emerging applications
in augmented virtual reality avatars, immersive telepresence systems, and next-generation digital
content creation. While modern 2D generative adversarial networks (GANs) Goodfellow et al. (2020); ;
Radford et al. Mao et al. ( et al. (2017); Zhou et al. (2021): Kang et al. (2023
achieve remarkable image quality in frontal face generation, their fundamental limitation in 3D scene modeling becomes apparent when synthesizing head images under arbitrary viewpoints.
Recent advancements in 3D-aware Schwarz al. (2000 Deng et al. (2022); Xue al. (2022) Nguyen-Phuoe et al.] (2019); Chan et al. (2021): Shi et al. (2021): Chan et al. (2022) An et al.
(2023): Li et al. (2024) have tackled this challenge by leveraging neural implicit representations, enabling view-consistent synthesis while maintaining photorealistic quality. Among these methods, the pioneering work EG3D Chan al. (2022) employs tri-plane structure to represent human heads or other 3D objects. The tri-plane representation Gao et al. (2022); Shue et al. (2023) ) Zou al. (2024); Wang et al. (2023b); Hong et al. (2023); Gupta et (2023): Zuo et (2023): : Wu
et al. (204aa) Zuo et al. (2024) efficiently captures symmetrical regions because two 3D points that symmetric with respect
feature plane will query the same feature on the plane via Cartesian coordinate projection. However, this inherent coupling of features becomes problematic in asymmetrical areas, leading to mirroring artifacts. As shown in fig. (a, b), a typical example in full-head synthesis is that the back-view of the head shares the same features on the Pxx plane as the front-view face, resulting in noticeable fake face artifacts on the back the head. While PanoHead An et al. (2023) mitigates this issue by augmenting each plane with additional parallel planes, its tri-grid representation does not fundamentally resolve the problem, as still inherits the same geometric and projection limitations of the Cartesian coordinate system.

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(b)
(c)
(d)
Pytna
Pl
(e)
(1)
gg
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Figure 2: In the tri-plane representation, the feature entanglement issue results mirroring artifacts, where (b) the back of the head incorrectly exhibits front-face attributes, or (c) the hair's 's texture and shape display an unnaturally high degree of left-right symmetry. In the spherical tri-plane representation, (d, the non-equal-area warping caused by mapping a square to sphere using (0, +) coordinates introduces (f) artifacts in the seam polar regions, as well as uneven spatial feature distribution after warping. By contrast, (g) the tri trlpplane representation exhibits even spatial feature distribution, whereas (h) the spherical tri ria-plane representation shows uneven distribution, with features being overly dense the polar regions and sparse near the equator.
recent work, SphereHead ei (2024), creatively addresses the mirroring issue by introducing spherical tri-plane representation that projects features in a spherical coordinate system. However, this approach introduces new challenges. First, it fails to leverage symmetry, which is prevalent in real- world objects. Second, the mapping from the square feature map to the spherical plane Poş involves non-equal-aree projection. Specifically, as illustrated in fig. (c-f). after this mapping, features are sparsest near the equator and densest at the poles. This uneven distribution results in inefficient feature map utilization when rendering 2D images, reducing the model's ability capture fine details. Moreover, referring to fig. 2 (d), a single spherical tri- tri-plane can produce artifacts in the seam region due the numerical discontinuity of Pes at á = and ş = π in the spherical coordinate system. Although SphereHead mitigates this issue by incorporating an additional orthogonal spherical tri-plane, this solution complicates the model and introduces parameter redundancy. Worse still, the least expressive equatorial region of one sphere are used to cover the most expressive polar regions of the other, further diminishing the overall expressiveness of the representation.
Besides, we are the first to observe that subtle yet persistent issue exists in both tri-plane and spherical tri-plane representations: feature penetration across convolutional channels can lead to interference between feature planes, particularly when one plane dominates the others. This issue arises because, unlike RGB images where channels are spatially aligned in 2D, each feature plane has unique distribution, resulting in significantly different spatial meaning and values at the same uv position. In convolutional layers, however, all output channels given uv position are computed using the same input values. Ideally, the network is supposed to learn appropriate kernels to separate information for different planes. Yet, this particularly challenging for 3D-aware GANs, as they are trained on 2D images without direct supervision on feature maps. Consequently, feature penetration often manifests visibly across planes, as shown in fig. [ Although visible feature penetration gradually diminishes training progresses, the issue itself remains difficult to fully resolve, subtly limiting the model's expressiveness and causing seemingly inexplicable artifacts.
In this paper, we introduce simple yet effective unify-split strategy that generates single-channe feature map and then splits it into multiple feature planes, instead of using different output channels to generate different feature planes. This approach completely eliminates the issue of feature penetration between channels. Building on this, we propose novel hybrid-plane (hy-pland representation that integrates both planar and spherical feature planes, as illustrated in fig. [ (c). This design leverages the strengths of both tri-plane and spherical tri-plane representations while mitigating their respective limitations. Specifically, the hy hy-plane representation automatically learns symmetrical

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features using planar planes and captures anisotropic features through the spherical plane. This approach avoids mirroring artifacts while ensuring uniformly high feature density throughout 3D rendering. Furthermore, to optimize the mapping from the square feature map to the spherical plane. we employ Lambert azimuthal equal-area projection Öztürk (2024) combined with elliptical grid mapping Fong (2019). These techniques maximize the utilization of the square feature map and eliminate the seam artifacts. We also explore several variant models to further enhance performance. For instance, we increase the area proportion of the spherical plane to boost its expressive power and propose a dual-plane-dual-ppherr variant to fully resolve polar artifacts. These innovations collectively contribute to the robustness and versatiliity of our hy-plane representation.
Building on the aforementioned technical advancements, the novel hy-plane representation enables our HyPlaneHead model to achieve tfate-of-the-ae art performance in full-head image synthesis, delivering high-quality results with significantly fewer artifacts compared to existing 3D-aware GAN Sguyen-Phuoc
methods Schwarz et al. (2020); Deng et al. (2022); ; Xue et al. (2022): ; Nguyen- Phuoc et al. (2019);
Chan et (2021); Shi et al. (202) Chan et al. (2022) An et al. (2023) at. (2024). summary, our main contributions are follows:
We conduct an in-depth analysis of the limitations inherent in tri rii-plane-like like representations used in 3D-aware GANs, Based on this understanding, we introduce the hy-plane represen- lation, which combines the strengths of both planar and spherical planes while addressing their respective drawbacks.
To achieve seamless integration of planar and spherical planes, we propose series of tech- nical innovations, including unify-split strategy, novel near-equal area warping method, area-biased splitting, and exploration of alternative combination strategies.
Through comprehensive experiments, we validate the effectiveness of our proposed rep- resentation. Our HyPlaneHead model achieves state-of-the-ar performance for full-head image synthesis, demonstrating superior quality and reduced artifacts.
2
Related Work
3D Morphable Head Representations. Traditional approaches for representing 3D faces with diverse shapes and appearances rely on 3D Morphable Models (3DMM) Blanz and Vetter (199); Paysan al. (2009), with FLAME Li et al. (2017) extending this framework to full head modeling. However, the coarse geometric details provided by 3DMMs have motivated numerous works to combine them with implicit neural representations, such as NeRF et al. (2023): Zanfir et al.
2022); Zheng et al. (:022); : Gafni et al. (022)); Guo et al. (2021): Park et al. (2021); Wu et al.
(2023a) Yenamandra al. 22021) Zhang et al. (203) Zhuang et al. (2022). While volume-based
rendering techniques have significantly enhanced the capabilities of 3DMM-based models, their inherent topological constraints limit the expressiveness of implicit representations, particularly in capturing fine details like hair and wrinkles. Consequently, recent 3D-aware generative models have shifted towards directly synthesizing implicit neural fields of heads without relying on 3DMM priors.
Generative Neural Head Representations. Emerging neural head generative models Nguyen Phuoc et a ( Schwwazz et al.) (2020); Deng al. (2022); Chan al. (2021); (Shi et al. (2021); Nguyen-Phuoe et al. (2019): Xue et al. (2022) adopt 3D-aware representations Mildenhal et al. (2021) which can be optimized by multi-view images through differentiable rendering. Though these implicit representations offer potential memory efficiency and structure complexity compared with traditional 3DMM-- representations Paysan et al. (2009) y Blanz and Vetter (1999); ( al. (0017). Query-based feature sampling and fully connected mapping slow down the convergence process. To maintain representation complexity while accelcrate the optimization process, EG3D Chan et al. (022) proposes tri-plane representation to explicitly store features on axis- axis-aligned planes that are aggregated by lightweight implicit feature decoder for efficient volume rendering. However, inherent coupling of features and mirroring issue are also brought with the efficiency. PanoHead (nn et al. (2023) propose to exploit extra in-the-wild data to supervise the back of head, thus can generate views in 360' full head setting. Although it enriches the tri-plane's representational capacity through adding more parallel feature planes, PenoHead can not thoroughly solve the mirroring issue from representation level. SphereHead Li et al. (2024), through shift in formulation from Cartesian coordinate representation cubic space to spherical coordinate representation in spherical space, greatly climinates the mirroring issuc and avoids many artifacts. While SphereHead Li ct al. (024)
EG3D[Chan

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(a)
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[c)
(c)
N,
Pry
X
Pior
Figure 3: (a) The Lambert azimuthal equal-area projection (LAEA) opens the South Pole, and maps the sphere to (b) flat circular plane, with the North Polc at its center. (c) Elliptical grid mapping transforms the circular plane into square. Conversely, the square be inversely mapped hack sphere with near-equal-arge properties. (d) In the hy-plane (2+2) representation, two spheres coincide, with their North Poles facing in opposite directions. Please refer the supplementary videos for more comprehensive understanding.
addresses many limitations of prior work, it fails to preserve the simplicity of representing symmetric objects and introduces discontinuities along the seam between the two poles. To this end, we propose novel hy-plane representation that effectively represents both symmetric and asymmetric regions, eliminates the mirroring issue, and avoids representation discontinuity.
3 Method
3.1
Hy-Plane Representation
As illustrated in fig. 2 both tri-plane and spherical tri-plane have distinct strengths and limitations. The tri-plane representation benefits from uniform and dense spatial feature distribution, enabled by Cartesian coordinate projection. It efficiently renders high-resolution images from various angles and leverages symmetry effectively. However, it struggles with disentangling asymmetric features, leading to unwanted mirroring artifacts caused by feature entanglement. In contrast, the spherical tri-plane uses spherical coordinate projection to naturally distinguish directional features and learn anisotropic representations, avoiding feature entanglement. Yet, its non-uniform feature distribution reduces feature map utilization and complicates the rendering of high-resolution details.
Recognizing the complementary nature of these approaches, we propose hy-plane, novel hybrid representation combining planar and spherical planes. hy-plane uses planar components to capture symmetric features and spherical components to model anisotropic features. This design retains the efficiency and uniformity of the tri-plane while eliminating feature entanglement and mirroring artifacts.
Hy-Plane (3+1) The basic version of our representation consists of three planar planes plus one spherical plane, referred to as hy-plane (3+1). The three planar planes are arranged mutually orthogonally, with the positive z-axis aligned toward the human face, the positive y-axis pointing to the top of the head, and the positive x-axis directed toward the left ear. Features are queried using Cartesian coordinate projection. The spherical plane adopts a spherical coordinate system, with the polar axis aligned with the head's top direction. Notably, instead of directly querying the feature map using (0, Φ) coordinates, we employ a novel near-equal-are warping method to improve feature map utilization.
Hear-Equal-Area Warping Directly querying the feature map using Φ) coordinates, in spher- ical tri-plane, is straightforward but introduces significant side effects. Geometrically (fig. 2(d-f), wrapping square feature map Pos into a spherical plane causes numerical discontinuities at Ø -T and & leading to artifacts in the seam region. Additionally, the edges 0 = 0 and 0 = contract into polar points, converting fluctuations along these lines into sigh-freeuency noise around the poles. Furthermore, this warping is aan-equll-ares unevenly distributing features from the square feature map onto the sphere. The equator region becomes feature-sparse, reducing expressive ability, while the poles become feature-dense, causing polar artifacts. Although SphereHead] [.i et al. (2024) addresses seam and polar artifacts by introducing an orthogonal dual-sphere setup, this approach doubles the number of feature planes but compromises the model's overall expressiveness, because
5

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each sphere uses its feature- equator region to cover the other's feature-dense polar regions, further limiting representation quality.
To address the challenging wrapping problem, we propose an elegant solution based on Lambert
azimuthal equal-area projection (LABA projection):
(R, θ) = 2 cos 20, 20.-0).
where (0, o) denote colatitude and longitude in spherical coordinates the spherical feature plane, and (R. )) denote radius and azimuth in polar coordinates on the flatten circle feature map. As shown in fig. 3 (a, b), LAEA projection unfolds the spherical surface from the South Pole and flattens it into circular plane centered on the North Pole. This method ensures equal-area transformation by adaptively adjusting latitudinal line density along the radius, achieving uniform feature distribution during warping. Additionally, it consolidates the seam and two poles of the spherical coordinate system into a single point, making them easier to handle. We align this point with the downward direction of the 3D head, which remains invisible in the rendering.
Next, we elliptical grid mapping transform the circle into square, illustrated in fig. (b. c).
which can be formulated as follows:
(z. v) (R.cos θ Rsin θ)
(2)
2 V + x2 -2 22 2v/2x /\ 2 x² 112 -2v2n,
(3)
0 2V 2- x2 +2 3² 2222 22 2 V x2 y² 2/22y
where (it, y) are coordinates on the circle, and (u, r) are coordinates on the square feature map. This near-equal-area mapping minimizes severe deformation, preserving feature quality. When querying a 3D point's 's feature on the spherical plane, we first convert its spherical coordinates (0, Φ) to polar coordinates (R, θ) on the wrapped circle using eq. (T). We then transform these polar coordinates into 2D Cartesian coordinates (z, 3) via eq. (2) and finally map them to the corresponding uu v) location on the square feature map using eq. (3). This approach maximizes the utilization of the feature map while effectively eliminating seam artifacts.
Hy-Plane (2+2) While in human head modeling, we can hide the final pole (the South Pole) by orienting it downward, this approach may not be applicable to other scenes or objects where no such unimportant direction exists for hiding. This limits its broader applicability. To address this, we introduce hy-plane (2+2), a variant consisting of two orthogonal planar planes and two spherical planes with opposing poles. When querying 3D point's feature on the spherical planes, we compute features separately and combine them using weighting function:
WA [Rmaxx Ra)²²
Wh (I2 Rio)2
(4)
Wafa whfo
figh
Wa wbb
Here, R. and Rs represent the radii of the 3D point projected onto the two wrapped circles. Rmaz and Renaz denote the radii of the circles. The weights wa and un are inversely proportional to these radii, peaking at the center (R = 0) and decreasing toward the edges (R gmmee), This design optimizes the use of the feature map's flat central region while minimizing the impact of the distorted edge areas, effectively resolving artifacts at the poles.
The reason for reducing one planar plane while adding a spherical plane is to be compatible with the unify-split strategy, will be explained in section 3.2 Notably, demonstrated in Wang et al. (2023a), two orthogonal planar planes can function nearly identically to three, since any two planes (PxY, Pxz, Pyz) encompass all three coordinates (z, 2).

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X
(a)
(b)
(c)
(d)
Figure 4: Unify-Split Strategy. (a) Hy-plane (3+1) with evenly splitting; (b) Hy-plane (2+2) with evenly
splitring: (c) Hy-plane (3+1) with area- beca-b splitting; (d) Hy-plane (2+2) with arca-biased splitting.
3.2 Unify-Split Strategy
Feature Penetration across Channels A key reason the widespread adoption of tri-plane-like representations is their ability represent 3D objects using 2D feature planes, whose data structure similar to 2D images. This allows researchers to directly leverage existing 2D image generation architectures for 3D-aware object synthesis. However, reusing these models directly, without adapting to the inherent differences between 2D RGB images and 3D-wware tri tri-plane-lic leads to a critical oversight. In RGB images, the three channels represent different colors but share the same 2D spatial context. That is, the same uv position corresponds to the same spatial location, with only color variations across channels. This creates strong correlations during neural network training, enabling the network to first learn shared features layer by layer and then separate them into individual channels the final output layer.
representations,
In contrast, in tri ri-plane-like representations, each plane encodes features from different spatial directions. Consequently, the same position different planes corresponds to entirely distinct spatial meanings. Forcing convolutional networks to learn unrelated features at identical uv positions across planes increases learning complexity and causes feature entanglement between disparate spatial locations. This issue is particularly pronounced in 3D-aware GANs, where the model indirectly optimizes feature planes by learning from 2D images. The difficulty in disentangling information across feature planes leads to visible interference between output channels, resulting in unexpected artifacts in the generated images.
Evenly Splitting Based on the aforementioned observation, we adopt a simple yet effective unify-split strategy for synthesizing tri-plane-like representations. Instead of using separate channels for different feature planes, we allocate distinct regions on a large unified one-channel feature plane and then split it into parts corresponding to individual feature planes. This spatially disentangles features across planes 2D space. fig. (a. b) illustrates the splitting process for hy hy-plane (3+1) and hy-plane (2+2), where all four planes evenly divided into two-by-two configurations.
Area-Biased Splitting Additionally, we can refine the 2D splitting scheme to enhance specific capabilities of the hy-plane. For instance, in full-head synthesis, the ability to model anisotropic features is crucial for generating high-resolution back-head details. As shown fig. 4c, d), for hy-plane (3+1), we increase the area of the spherical plane and elongate the feature maps Pxy and Pyx along different axes. This maximizes their expressive power when combined. For hy-plane (2+2), we enlarge primary spherical plane while shrinking the other. The larger plane remains full sphere, while the smaller one forms a spherical cap, covering the problematic polar region the larger sphere.
3.3 HyPlaneHead
We integrate our hy-plane representation into HyPlaneHead, 3D-aware GAN pipeline akin to Chan let al. 2022); An al. b: Li et al. (2024). Given sampled z and conditioned camera parameter Ceons the generator G produces one-channel unified feature map, which is then split into individual feature planes of the hy-plane representation. Features are queried from each plane and volumetrically rendered using the viewing camera Cren- enabling HyPlaneHead to generate head image I and
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Yaw: 45"
90*
135'
180'
Yaw: 0
45"
90"
135
180
(b)
(c)
(cD
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Figure 5: Qualitative comparison with state-of-the-and methods. (a) Tri-plane representation from Chan al.
2002)) (b) Tri-grid representation from An et al. (2023). (c) Single spherical tri-plane representation from Li al. 2024), where the white dashed box highlights a discontinuity in the hair region. (d, e) Dual spherical tri-plane representation from et al.] (2024). (f-j) Our proposed Hy-plane representation. A closer inspection recommended for finer details, and refer readers to the supplementary material for higher-resolution visualizations.
2024). the output passes through a super-resolution module to produce the high-resolution head image T+ FFollowing An et al. (0023) Liet al. (2024), we also introduce a background generator to allow G to focus specifically on the head region. In addition the conventional 3D-aware GAN losses used in Chan et al. (2022), we further employ view-image consistency loss, as proposed in Li et al. (2024). to guide the discriminator to focus the alignment between images and their corresponding viewpoints.
mask 7 As in Chan et al. (002 An et al. (2023) et al.
Experiments
In this section, we conduct comprehensive qualitative and quantitative experiments on full-head image synthesis demonstrate that hy-plane representation well-suited for rendering from any viewpoint. Our comparative analysis includes tri-plane, tri-grid, spherical tri-plane from EG3D, PanoHead, SphereHead respectively, and various hy-plane variants and settings for ablation study, all trained on our dataset and pipeline. All experiments are trained on eight NVIDIA V100 GPUs with batch size of 32. We follow PanoHead and SphereHead, using training set that includes FFHQ Niemeyer and Geiger (202T), CelebA Liu et al. (2018). LPFF| Wu et al. (20235), WildHead Li al. (2024), K-Hairstyle| Kim al, (202T), and 6K in-house dataset of large-pose head images processed with SphereHead's ' toolbox. All training images are 512x512 12 in resolution and augmented with horizontal flips. The entire training process spans 25 million images.
4.1 Qualitative Comparison
We visualized synthesized samples and feature planes under varying configurations. For feature plane analysis, channel activations were averaged across patterns. As shown in fig. Ta.b), tri-plane and spherical tri-plane models exhibit notable cross-plane interference: secondary feature maps show identical texture patterns from the dominating plane, alongside anomalous noise pattern. We suspect this phenomenon arises from inter-channel feature penetration, where competing planes disrupt each other's activations. Contrastingly, our unify-spli strategy (fig. [c)) allows each plane to specialize its directional features without cross-channel interference, thereby producing informative and clear feature maps. From fig. (c) and fig. 4 our hy-plane representation effectively integrates planar and spherical planes, enabling seamless collaboration through a division of labor. The planar planes specialize in capturing symmetric
to visualize spatial texture

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Table 1: Quantitative comparison Full-head Image Synthesis task. We list FID, FID-random metries with
different representations. denote that we output two spherical tri-planes simultaneously by shared branch.
Representation
Unify-Split
Wrapping
FID
FID-random
Trinplane (ha3DCChan al.
20221
11.23
Tri-plane
evenly split
8,
11.52
Spherical Tri- Tri-plane (SphereHead] et
2024
8.64
10.71
Sphericul Tri-planc
evenly split
8.36
10.42
Dual Spherical prinp
8.68
10.28
Dual Spherical Tri- plane
11.9
13.54
Tri-grid (PanoHead An al. 2023
8.77
10.66
Tri-plane 512"
9.27
10.89
Spherical Tri-plane 512
8.82
10.47
10
Tri-grid 5122
8.79
10.78
11
Hy-plane (3+1)
8.54
10.66
12
Hy-plane (3+1)
evenly split
8.31
10.18
Hy-plane (3+1)
evenly split
yes
8.18
9.96
14
Hy-plane (3+1)
area-bias split
8.14
9.88
15
Hy-plane (2+2)
evenly split
yes
8.28
10.01
16
Hy-plane (2+2)
arca-bias split
yes
8.17
9.84
features (e.g., Zyz learns left-right symmetric details such as side hair, ears, and shoulders), while the spherical plane excels at modeling anisotropic features (e.g., the frontal face and back-view hair).
fig. compares our method with state-of-the-art full-head 31D-aware GANs. (a) The tri-plane representation from Chan et al. (2022), retrained on our dataset, generates mirrored faces at the back due to feature entanglement. (b) PanoHead An al. (2023) uses tri-grid structure but still exhibits excessive symmetry in hairstyles. (c) The single spherical tri-plane from SphereHead Li et al. (022)
produces full-head geometry but introduces seam and polar artifacts via (8, Φ) warping. (d. e) Its dual spherical variant reduces artifacts but leads to over-smoothed textures and loss of detail. (f Our HyPlaneHead combines planar and spherical representations, achieving high-quality synthesis with rich texture and geometric fidelity, setting new benchmark for full-head 3D-aware GANs.
4.2 Quantitative Comparison and Ablation Study
To quantitatively evaluate the visual quality, fidelity, and diversity of the synthesized full-head images, we employed the Frechet Inception Distance (FID) metric Szegedy et al. (2016) on 50K real and synthetic samples. As noted in prior 3D-aware GANs based full-head image synthesis works An et al.) )023);; Li et al. (2024), current 3D-aware GANs typically perform well under the conditioning camera pose during synthesis but degrade significantly non-conditioned rendering angles. To rigorously assess performance under arbitrary viewing angles, which is especially critical for full-head image synthesis, we introduced new evaluation metric, FID-random, which decouples the conditioning pose from the rendering pose. Specifically, during generation, we first randomly sample a camera parameter Ccon from the dataset's camera distribution to condition the tri-plane-lit like representation; subsequently, we render the head image using different random camera parameter Cren (also sampled from the same distribution). The FID score is then calculated based on the images rendered under these random viewpoints, thereby providing unbiased evaluation of the model's robustness and generalization across all possible angles.
Comparing (T with table T 11) demonstrates the advantages of augmenting the tri-plane with a spherical plane, consistent with our carlier visualizations. The effectiveness of our unify-split strategy is evidenced by the general reduction in FID and FID-random scores across table 1,23,,4,1,11111111 144,1,1122) Notably, while applying the unify-split strategy to the tri-plane reduces FID, increases FID-random. This occurs because the strategy eliminates inter-channel feature penetration, allowing each plane to fully express its directional features. However, since the tri-plane does not separate directional features, the enhanced plane expression exacerbates mirroring artifacts on the backside, thereby worsening FID-random. In contrast, both the spherical tri-plane and hy-plane benefit from the separation of directional features provided by the spherical plane, enabling them to leverage the improved expressiveness unlocked by the unify-split strategy, resulting in reductions in both FID and FID-randon table I 3,4.5.6.9) reveal that directly outputting dual spherical tri-planes leads

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Figure 6: Single-view 3D-aware GAN inversion results. From top bottom: (a) PanoHead An et al. (b) SphereHead Li et al. 2024), and (c) our HyPlaneHead.
2023
to significant interference between the two dominant theta-phi planes, yielding the highest FID scores. SphereHead mitigates this issue by introducing two small convolution-baas -based branches, albeit at the cost of increased parameters. However, adopting the unify-split strategy achieves superior results without additional parameters, as demonstrated by the single spherical tri-plane's performance. table T 12,13) validate that wrapping improves performance by fully utilizing the square feature map. In table [ [1,16)) we split a 512x512 feature map into four parts via area-bias splitting: 384x384, 384x128, 184x128, and 128x128, with the largest allocated to the spherical plane. Comparing these configurations with table 13,15) confirms the effectiveness of this partitioning scheme for full-head synthesis. Finally, tested the tri-plane, tri-grid, and spherical tri-plane with a feature map size of 5125512. table 8,99, 1,,9,101) show minimal impact from increasing the feature map size, ruling out model parameter scaling significant factor influencing our experimental outcomes.
4.3 Single-view 3D-aware GAN Inversion
We compare our method with PanoHead and SphereHead on 3D full-head reconstruction from single-view image using Pivotal Tuning Inversion (PTT) Roich et al. (2022). As shown in fig. [ PanoHead consistently produces noticeable artifacts with strong left-right symmetry. SphereHead generates a plausible back-of-hear region but yields blurry hair details, resulting in an overly coarse appearance. In contrast, our HyPlaneHead produces reasonable and high-quality renderings from all viewing angles.
5 Conclusion
In this paper, we conduct an in-depth analysis of the limitations inherent in tri-plane-like representa- tions used in 3D-aware GANs, particularly focusing mirroring artifacts, uneven warping from the square feature map to the spherical plane, and feature penetration across channels. Based on these insights, propose the hybrid-plas -lane (hy-plane) representation. which combines the strengths of planar and spherical planes while mitigating their respective weaknesses. Our technical contributions include unified planar-spherical representation, near-equal-area warping for seamless and efficient tosaee- mapping, and a unify-split strategy to eliminate feature penetration. These inno- vations enable HyPlaneHead achieve state-of-the-ar performance in full-head image synthesis, significantly reducing artifacts and enhancing rendering quality.
6
Acknowledgements
The work was supported in part by NSFC with Grant No. 62293482, the Basic Research Project No. HZQB-K HZQB-KCZYZ- KCZYZ-202 1067 of Hetao Shenzhen-HR HK S&T Cooperation Zone, Guangdong Provin- by Shenzhen Science and Technology
cial Outstanding Youth Fund(No. 202381515020055 Program No. JCYJ202203301436600000 NSFC No.62172345 the Shenzhen Outstanding Tal- ents Training Fund 202002, the Guangdong Research Projects No. 20172T07X152 and No. 2019CX01X104, the Guangdong Provincial Key Laboratory of Future Networks of Intelligence (Grant No. 2022B121201000 Intelligence (Grant No. SYSPG20241211118530077
and the Shenzhen Key Laboratory of Big Data and Artificial
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Supplementary Material
A
Overview
This supplementary document provides additional materials to support the main paper "HyPlaneHead: Rethinking Tri- Tri-plane-like Representations in Full-Head Image Synthesis". We present high high-resolution qualitative comparisons that highlight the performance of our method against existing approaches section B A high-resolution qualitative comparison provided in section C We also provide detailed ex explanation of the Near-Fqual-Ares Warping technique and our proposed Hy-Plane (2+2) representation, which are central to achieving view-consistent and artifact-free full-head image synthesis in section D Comprchensive measurements of parameter count, training and inference speed, and VRAM usage across various methods and HyPlaneHead configurations are reported in section D An in-depth discussion and comparison with related works, including OrthoPlanes, algorithms, can be found in section F Additional qualitative results across broader set of examples are included to further demonstrate the effectiveness of our model in section G We discuss the current limitations of our approach and potential directions for future work in section H Finally, we include a section Code of Ethics, where address the ethical considerations and potential misuse of 3D head generation technologies in section !
SYM3D, and other tri- tri-plane-relatee
B High-Resolution Qualitative Comparison (Main Paper Fig. 5)
Due to the page limit of the paper, were only able to include low-resolution version of the qualitative comparison (Main Paper Fig. 5). To better demonstrate the advantages of our method in generating fine details, provide high-resolution version of Main Paper Fig. 5.
fig. presents a high-resolution qualitative comparison with state-of-the-ee methods. Each subfigure corresponds to the following representations: (a) Tri-plane representation from Chan et al. (2022). (b) Tri-grid representation from An et al. (2023). (c) Single spherical tri-plane representation from
Li et al. (2024), where the white dashed box highlights discontinuity in the hair region caused by
seam artifacts. (d-c) Dual spherical tri-plane representation from Li al, (2024). (f-j) Our proposed Hy-plane representation.
While both the tri-plane and tri- tri-grid representations and b) yield rich details in front-views, they suffer from inherent symmetry artifacts due to their Cartesian coordinate projections. Specifically, (a) exhibits clear mirroring face artifacts on the back of the head, reflecting front-view facial attributes. Similarly, (b) shows excessive left-right symmetry in the rear view.
The single spherical tri-plane (c) addresses the symmetry issue by introducing spherical projection. However, introduces seam artifacts due to the discontinuity in the (0, Φ) warping at the boundary of the spherical feature map shown in the white-dashed box, where the hair texture misaligned).
To mitigate these seams, the dual spherical tri-plane approach (d-e) introduces an additional or- thogonal spherical tri-plane. While this effectively eliminates seam artifacts, it comes at the cost of increased parameter numbers. Moreover, when merging the two spherical tri-planes, the regions with the lowest feature density-ias -i..,, the equatorial areas used to cover the high-density polar regions of the other plane. This results in reduced expressiveness for fine details such as hair textures and shape contours.
In contrast, our method employs the Hy-plane representation, which leverages the dense and even spatial feature distribution of the tri-plane, as well as its efficient representation of symmetric regions, to ensure high-fidelity detail reconstruction. At the same time, a spherical tri-plane is utilized to provide anisotropic representation for asymmetric areas, effectively eliminating mirroring artifacts. Furthermore, we introduce a novel near-equal-ares sphere-to- warping strategy that avoids seam artifacts without compromising detail preservation.
C Details of Near-Equal-Area Warping
The -qaa-Eaual-Area Warping method ensures that each region of the spherical input is mapped onto the planar representation with approximately equal surface area, add avoid excessive distortion
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Yaw: 0
45
90a
135
180
a
(b)
(c)
(d)
(e)
(f)
(h)
Figure 7: High-resolution qualitative comparison with state-of-the-arr methods.
and eliminating seam artifacts. This warping strategy is implemented in two steps: first, we use the Lambert Azimuthal Equal-Area Projection (LAEA projection) to flatten the spherical surface into
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(a)
(b)
(d)
(e)
(f
(h)
Figure 8: Illustrations of (a-d) the Lambert Azimuthal Equal-Area projection and (e-h) Elliptical Grid Mapping
circular domain while preserving area; second, apply Elliptical Grid Mapping to transform the circle into a square domain, enabling efficient utilization of the qquare-shaped feature map.
To better understand our proposed Near-Equal-Ares Warping, we provide additional details and illustrative diagrams in this supplementary material.
In the Lambert Azimuthal Equal-Area Projection, the south pole of the sphere is "opened" and then flattened into circular domain centered at the north pole. During this unfolding process, the distances between latitude lines are adjusted such that the resulting circular projection maintains equal-area correspondence with the original spherical surface. A clearer understanding of this transformation can be gained from fig. fig. g (a-)) illustrates the dynamic process of the Lambert Azimuthal Equal-Area (LAEA) projection. fig. d( illustrates the Lambert Azimuthal Equal-Area (LAEA) projection using a world map example, demonstrating that it preserves area. Each orange circle represents region of equal size the original spherical surface.
Subsequently, we employ Elliptical Grid Mapping to convert the cireular domain into near-equal- area square grid. Among various methods for transforming a circle into square, we choose Elliptical Grid Mapping due to its following advantageous properties: Approximate equal-area mapping: The variation in local area across the transformed plane is minimized. 2. Smooth central region and minimal distortion the boundaries: This preserves important structural details, especially near edges. 3. Computationally simple and stable: It avoids division operations, which is crucial for maintaining gradient stability during training. An intuitive illustration of this mapping is provided in fig. [ fig. ee.f) show the deformation of Elliptical Grid Mapping under black-and white stripe patterns, indicating that most regions experience minimal area distortion. fig. Btg.h) show the feature maps of the spherical plane before and after applying Elliptical Grid Mapping. Without this mapping, the model fails to effectively utilize the corner regions the feature map. contrast, with Elliptical Grid Mapping, most regions of the feature map efficiently utilized.
For convenience, we have included the core implementation of the Near-Equal-Aree Warping method in the supplementary material as near near_equal area aertaawwainnn....ee py.
D Details of Hy-Plane (2+2)
Although the LAEA projection addresses seam artifacts, its implementation still requires "opening" the South Pole, which inherently leaves remaining polar region. This region is prone to high-frequency noise and distortion.
https://en. http:///ee..aa.aaaaaaaaaaaaaaaaa
grid_mapprr midd_mppping.r

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While in the Hy-Plane (3+1) formulation we can hide the problematic area by orienting it downward, this approach limits the generality of our representation, particularly for objects or scenes that require rendering from all directions. In such cases, relying a single hidden region is insufficient, as any arbitrary viewpoint may expose the problematic area and lead visible artifacts. To make the Hy-Plane representation more universally applicable, we propose Hy-Plane (2+2) to resolve this issue.
The Hy-Plane (2+2) representation combines two planar planes (Pxy and Pyz) and two spherical planes (Pa and Pt). These two spherical planes overlap spatially but are oriented such that their respective South Poles face opposite directions. Specifically, as shown in main paper Fig. 3 (d), the North Pole of Pa oriented along the negative z-axis, while the North Pole of P. is oriented along the positive z-axis; consequently, their South Poles point in opposite directions. By assigning weights to each plane and summing them, the smooth North Polar regions of one spherical plane can effectively cover the problematic South Polar regions of the other, thereby eliminating the distortion-pret areas entirely.
E Parameter Count, Speed and VRAM Usage Comparison
To provide comprehensive comparison of parameter count, training and inference speed, as well as VRAM usage across different methods and HyPlaneHead configurations, we conducted measurements for each experiment listed in Table 1 of the paper. The results are shown in table 2 where asterisk (+) denotes that two spherical tri-planes are output simultaneously by a shared branch.
To provide a comprehensive and fair comparison, we clarify the definitions of the metrics used in our evaluation. Representation Parameters refer to the number of parameters in the feature maps of different tri- ti-plane-like representations (e.g., tri-plane, spherical plane. hy-plane, etc.). Total Learnable Parameters denote the total number of trainable parameters in the entire model architecture, such as FG3D, PanoHead, SphereHead, and HyPlaneHead. Training Speed and Training VRAM Usage are measured on single V100 GPU with a batch size of 2, representing the average time and memory consumption required to train 1,000 images. Similarly, Inference Speed and Inference VRAM Usage are evaluated under the same hardware setup but with a batch size of 1, by generating 100 images and computing the average time and memory cost per image.
From the statistics in the table, we can observe that different representations vary significantly in terms of feature plane parameter count. The tri-plane uses the fewest parameters (3 x 256 x 256 moating--pintt values), while the tri-ge tri-grid with 512x512 resolution uses the most (9 x 512 512). Our proposed hy-plane uses 1 x 512 x 512 floating-poit used by the tri-plane. However, the overall difference in total learnable parameters across models relatively small. The minor variations are mainly due to differences in the final convolutional layer configuration of StyleGAN2, which depends on how each representation is generated.
values, which is only 1.33 times the number
Notably, the total learnable parameters for entries No. 3 and No. 5 are identical. This is because in experiment No. 3, we did not modify the model code at all, which means only the rendering pipeline was adjusted to use one spherical tri-plane. We reported the actual parameter count to remain consistent with our experimental setup.
In terms of training and inference speed, as well as VRAM usage, there are some differences among the experiments. Overall, however, the additional computational overhead introduced by our innovations is relatively small.
Replacing the tri-plane with hy-plane introduces:
+5.5% training time,
+3.8% inference time,
* *+8,89 8% training VRAM,
-0.8 inference VRAM.
Adding the evenly split strategy further increases the cost by:
+8.4% training time,
+6699% inference time,
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+12.2% training VRAM,
+16.6% inference VRAM.
When combining with the near-equal-aree projection, the overhead becomes:
+0.5% training time,
+3.5% inference time,
-5% training VRAM,
-.7%% inference VRAM.
Please note that in some cases. VRAM usage may actually decrease, likely due memory fragmen- tation causing inaccuracies in the nvidia- smi measurement.
In summary, while our method does introduce increase is relatively modest and justifiable given the significant improvements in disentanglement and reconstruction quality.
computational and memory overhead, the
F Comparison and Discussion with Related Works
In this section, we provide a detailed comparison our hy-plane representation with two closely related recent works: OrthoPlanes He et al. (2023) and SYM3D Yang et al. (2024), and discussion on compatibility with several tripplane-related algorithms.
F.1 Comparison with OrthoPlanes
OrthoPlanes enhances the standard tri-plane representation by introducing multiple parallel planes along each Cartesian axis, effectively increasing the capacity of the feature field. This design is conceptually similar to the tri-grid formulation, where additional planar slices are used to capture finer geometric details.
However, this strategy does not address the underlying structural limitations of Cartesian- projections. Specifically, OrthoPlanes still relies axis-aligned planar projections in Cartesian Coordination, which inherently suffer from mirroring artifacts. As demonstrated in our main paper (fig. D. such artifacts manifest as mirroring-face artifacts and unnatural left-right duplication. Our hy- plane representation mitigates this by integrating spherical projection planes that naturally align with the radial symmetry of human heads, thereby disentangling symmetric and asymmetric components more effectively.
Moreover, the increased number of planes in OrthoPlanes leads to higher storage overhead. For downstream applications such as head reconstruction or model initialization (e.g., Portrait3D)
Wu et al. (2024b), AnimPortrait3D) Wu et al. (2025), ID-Sculpt Hao et al. (2025) ), each sample must store K times more feature maps (where K is the number of parallel planes per axis), significantly increasing memory and bandwidth requirements. In contrast, hy hy-plane uses only four planes (three planar one spherical) while achieving superior fidelity.
Finally, due to the increased number of planes, it becomes difficult to integrate these approaches with our unify-split strategy. As a result, inter-channel feature penetration remains an issue in these methods.
F22 Comparison with SYM3D
First, our goal differs from that of SYM3D. SYM3D enhances the symmetry of tri tri-plane representa- tions through symmetric regularization, which is beneficial for generating fully symmetric artificial objects. In contrast, our hy-plane is designed to support broader range of real-world scenarios where both symmetric and asymmetric structures coexist, such as in full-head portraits. Second, regarding feature penetration, SYM3D employs attention based scheme (View-wise Spatial Attention) to learn how to alleviate feature penetration across channels, whereas our hy-plane utilizes the unify-split strategy geometrically and fundamentally prevent feature penetration its source.
In fact, the correlation across Pxy, Pyz and Pxz planes discussed in SYM3D essentially corre- sponds to the inter-channel feature penetration problem we identify in paper, though they observe
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Infer: VRAM (MIB)
1100
11-49
1481
1097
1481
1235
1245
1333
1103
2463
1098
1277
1205
1321
1215
1255
Table 2: Comparison different 3D representation configurations terms of parameter count, training/inference speed, and VRAM usage.
Training VRAM (MiB)
6436
7670
8048
7692
9296
6810
6836
6450
6744
8642
6966
7818
7432
7558
7780
8012
Inference Speed (ms/mmage)
42.06
44.17
54,99
48.19
54.87
50.42
45.19
47.77
59.91
58.79
43.64
46.27
47.89
49.61
49.81
$1.74
Training Speed (seciking)
197 89
222 84
198 57
245. 245.79
38
198 59
181 .000
182 28
266 13
206 54
211
226 31
212.27
219.75
190
53,174,956
53,230,222
54,713,868 3,868
53,234,479
54,713,868
53,462,509
53,741545 1548
53,423,246
54,96 $4,9621158
54.002,318
53,230,222
53,230,222
53,230,222
53,230,222
53,230,222
53 23,269,
Total
Rep. Params
3x256xx22 x566
1x512x512
3x 256
1x512x512
6x256x2566
6x256x256
9x256x255
3x:12x512
6x512x512
9x512x512
4x256x2256
1x512x512
1x512x512
1x512x512
1x512x512
Ix512x512
Wrapping
yes
yes
aree-bins split
areo split
Unift--plit
ovenly split
evenly split
evenly split
evenly split
evenly split
Dual Spherical Tri-plane
Doal Spherical Tri-plane
Spherical Tri-plane 5124
Spherical planp
Spieeiical Tri- plane
Representation
Tri plane 5122
Hy plaane (3+1)
Hy plane (3+1)
Hy-plane (3+1)
Hy Hy-plame (3+1)
Hy plane (2+2)
Hy plane (2-2)
Tri-grd 31.2
Iri-plane
Tri-plane
Tri-gnd
No.
2
10
12
13
14
15
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it from a different angle. We detect this issue through visual inspection of feature maps (as shown in fig. [ab))), while SYM3D quantifies it using correlation metrics.
Inter-channel feature penetration causes similar values at the same UV positions across different feature maps, resulting in visually similar patterns, exactly what can be seen in our fig. a.. This phenomenon is also visible in the top-right panel of fig. 7 in SYM3D, where GET3D's feature maps exhibit repetitive vertical lines at the same spatial locations when zoomed in. Numerically, this leads to high correlations between different feature maps.
The difference lies in terminology: SYM3D does not explicitly refer to this inter-channel feature penetration, but rather describes it as correlation. SYM3D attributes this issue to the use of single- view images during training, as opposed to multi-view data. While this is certainly true more views provide richer geometric cues and reduce ambiguity more fundamental perspective, however, the issue arises due the structural nature of convolutional networks, where different channels are prone to interfere with each other, especially in the absence of direct supervision. Our solution addresses this root cause directly by modifying the architecture via the unify-split strategy, which effectively eliminates inter-channel feature penetration at its source.
also discuss this point in our paper. From a
In contrast, SYM3D uses view-wise spatial attention to alleviate the issue. As shown in the bottom- right panel of fig. 7 in SYM3D, this approach does reduce correlation to some extent. However, as illustrated in the middle-botton bottom panel, strong correlations still exist between certain planes, particularly Pyz and Pxz. We have also computed and visualized similarity matrices similar to those in SYM3D's fig. 7, and our results show significantly lower feature correlations across different planes.
Moreover, in terms of implementation complexity, our unify-split strategy is simpler and more intuitive compared to SYM3D's view-wise attention mechanism, while achieving a more complete resolution of the problem.
F33 Discussion on Compatibility with Tri-plane-related Algorithms
Dual Encoder Bilecen et al. 2024) introduces a dual-encoder GAN inversion approach for single- view 3D full-head reconstruction. It uses one encoder for the visible front region and another for the occluded back region, addressing issues such as mirroring artifacts in PanoHead's 's W space. In contrast, our HyPlaneHead improves the underlying representation structure to reduce such artifacts and enhance W space quality. As a result, standard inversion methods like PTI a general-purpott technique for common GAÑS GANs-already yield better performance than previous approaches. We believe that combining our method with specialized inversion strategies like Dual Encoder, which are specifically tailored for full-head generation, could further improve results.
Tri2-plane [Song et al. (2024) introduces a cascaded tri-plane representation across multiple scales of facial features, similar to feature pyramid. This hierarchical design enables the model to generate both global structure and fine-grained details, resulting in richer and more detailed head reconstructions. Although it is still based on the tri-plane formulation and thus remains susceptible to mirroring artifacts, we believe that the multi-scale feature pyramid concept could be beneficially integrated with our hy-plane representation in future work.
Our method is also compatible with recent local editing approaches, such as Bilecen et al. (2025), which has been successfully applied to both tri- plane (EG3D) and tri trid (PanoHead) representations. Given that our hy-plane shares a similar structure, we believe the method should be directly applicable to our representation as well.
Inspired by recent 3D avatar approaches Qian et al. (2024): Zhang et al. (2025); Qiu et al. 2025b);
Kirschstein et al.) (2025); Chu and Harada ) Qiu et al. (2025a); Kirschstein et al. (2024) based on 3D Gaussian Splatting (3DGS) Kerbl et al. (2023), we believe our hy-plan representation can be effectively integrated with 3DGS to further enhance rendering quality. We plan to explore this promising direction in future work.
Additionally. we believe that replacing the standard tri-plane representation in methods like LRM Hong et al. (2023) and InstantMesh Xu et al. (2024) with our hy-plane has the potential to improve their generation quality. On one hand, our unify-split strategy eliminates inter-plane feature penetra- tion, allowing each plane to express its features more clearly and effectively. On the other hand, by incorporating a spherical plane, we enhance the model's ability to represent asymmetric regions, such
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as facial details and hair on the back of the head. Therefore, we expect that integrating hy-plane into these models would lead to more accurate and artifact-free 3D reconstructions.
G More Qualitative Comparison
Figure 9 shows additional qualitative comparisons between method and existing approaches on larger set of examples. On the left side, samples (1-2) are generated by the official EG3D model, which is trained exclusively on the FFHQ dataset that lacks large-pose and back-view head images. As a result, these samples exhibit severe mirroring artifacts their back views. Samples (3-4) use the tri-plane representation trained within our pipeline and data; while hair appears in the rear region, the front-facing facial attributes still dominate, indicating incomplete adaptation to non-frontal views. Samples (5-7) come from the official PanoHead model, which can produce detailed facial and hair textures but suffers from strong left-right symmetry and visible artifacts in the back view. Similarly, samples (8-10), using the tri-grid representation trained with our setup, also exhibit comparable symmetry and artifact issues. These problems-mirrorinn artifacts and severe left-right symmetry-ant primarily caused by the Cartesian projection used both tri tri-plane and tri-grid representations. Samples (11-13) are from the official SphereHead model, which addresses the mirroring issue through spherical tri-plane design but results more blurred outputs with less detailed facial and hair textures. The same trend is observed in samples (14-16), where a spherical trinplane model is trained using our pipeline and data. In contrast, on the right side, samples (17-32) are generated by our HyPlaneHead model. By leveraging the novel hy-plane representation, our method not only eliminates mirroring artifacts but also achieves high-quality, consistent rendering from arbitrary view angles.
H Limitations
Despite the promising results of our method, there are still several limitations that warrant further investigation. First, similar previous works such as EG3D, PanoHead, and SphereHead, our model exhibits minor visual flickering or instability in fine details when rendering from gradually changing viewpoints. We believe this is partly due the current GAN backbone's limited capacity for high-fidelity view-consistent generation, and we plan to address this by adopting a more powerful generator architecture in future work.
Second, our method, like existing approaches, struggles with generating highly complex hairstyles such as ponytails, braids, or other structured hair arrangements. This limitation likely stems from insufficient training data covering such styles. We regard this as an important direction for future research and intend to expand our training dataset include broader variety of hairstyles and appearances.
I Code of Ethics
Our work presents method for learning generalizable 3D full-head modeling from monocular images, which has potential applications in virtual avatars, digital content creation, and immersive experiences. However, such technology also raises ethical concerns, particularly regarding privacy and the potential for misuse, such as identity deception or unauthorized generation of realistic 3D head models. We are aware of these risks and emphasize the importance of responsible deployment, transparency, and user consent in any real-world application of this technology.
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(17)
(18)
(1
(20)
(10)
(26)
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[{"review_id": "0lAvKoZhVw", "reviewer": "Reviewer_Xp1z", "summary": "HyPlaneHead introduces a new hybrid-plane (hy-plane) representation for 3D-aware GANs, improving full-head image synthesis by reducing artifacts like feature entanglement and mirroring. The proposed method combines planar and spherical planes, using innovations like near-equal-area warping and a unify-split strategy, achieving state-of-the-art performance. In addition,  the single-channel unified feature map design mitigate the feature penetration problem, which is a significant contribution of this work.", "questions": "**Q1**: As mentioned in the weakness part, could the authors share the influence of each component on the training and inference speed? \n\n**Q2**: The proposed \"Unify-Split Strategy\" did managed to mitigate the feature penetration problem, but might introduce additional GPU memory usage. Could this method be extended to higher resolution image generation without a super-resolution module? More training details will be highly appreciated. \n\n**Q3**: Could the authors provide some single-view GAN Inversion results with PTI? The inversion results might be useful for some other real-world application and could better showcase the model capacity.", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strength:\n\n**Novelty and Innovation**: The hy-plane representation is a novel approach, addressing limitations in existing tri-plane methods. It leverages planar planes for symmetry (e.g., capturing left-right symmetry in heads) and spherical planes for anisotropy, potentially setting a new standard for 3D-aware GANs.\n\n**Technical Advancements**: The near-equal-area warping and unify-split strategy are innovative, with potential applications beyond full-head synthesis. For instance, the unify-split strategy could reduce feature entanglement in other 3D object synthesis tasks, enhancing model expressiveness.\n\n**Empirical Support**: The paper provides comprehensive experiments, including ablation studies and comparisons with state-of-the-art methods (e.g., Chan et al. 2022, An et al. 2023, Li et al. 2024). The superior FID scores and reduced artifacts in visualizations support the claimed improvements.\n\n\n**Weakness:**\n\n**Model Complexity**: My major concern lies in the model complexity. The proposed method introduce several novel component. Despite the impressive results, these components might introduce additional computational overhead, but the authors did not discuss the training and inference speed. It would be great if the authors could share the influence of each component on the speed.", "was_revised": true, "final_justification": "Issuses resolved: The author provided a detailed analysis regarding the computational cost and proposed a potential solution for extending the method to higher resolutions.\n\nIssuses unresolved: Due to the rebuttal policy, the author was unable to provide experimental results on inversion. While this is unfortunate, it is also understandable.\n\nI believe the issues unresolved should not detract from the fact that this is an innovative work, especially considering its significant contribution to alleviating mirroring-face artifacts.", "rebuttal": "Thanks for your insightful review! Here are our responses to your concerns.\n\n**Q1 & Q2: Training/Inference Speed & VRAM Usage**\n\nTo provide a comprehensive comparison of training and inference speed, as well as VRAM usage across different methods and HyPlaneHead configurations, we conducted measurements for each experiment listed in Table 1 of the paper. The results are as follows.\n\n| No. | Representation              | Unify-Split     | Wrapping | Representation Parameters | Total Learnable Parameters | Training Speed (sec/kimg) | Inference Speed (ms/image) | Training VRAM Usage (MiB) | Inference VRAM Usage (MiB) |\n|-----|-----------------------------|-----------------|----------|---------------------------|----------------------------|-------------------------|---------------------------|---------------------------|----------------------------|\n| 1   | Tri-plane                   | -               | -        | 3x256x256                 | 53,174,956                 | 180.29                  | 42.06                     | 6436                   | 1103                    |\n| 2   | Tri-plane                   | evenly split    | -        | 1x512x512                 | 53,230,222                 | 197.89                  | 44.17                     | 7670                   | 1149                    |\n| 3   | Spherical Tri-plane         | -               | -        | 3x256x256                 | 54,713,868                 | 222.84                  | 54.99                     | 8048                   | 1481                    |\n| 4   | Spherical Tri-plane         | evenly split    | -        | 1x512x512                 | 53,234,479                 | 198.57                  | 48.19                     | 7692                   | 1097                    |\n| 5   | Dual Spherical Tri-plane    | -               | -        | 6x256x256                 | 54,713,868                 | 245.79                  | 54.87                     | 9296                   | 1481                    |\n| 6   | Dual Spherical Tri-plane \\* | -               | -        | 6x256x256                 | 53,462,509                 | 196.88                  | 50.42                     | 6810                   | 1235                    |\n| 7   | Tri-grid                    | -               | -        | 9x256x256                 | 53,741,548                 | 198.59                  | 45.19                     | 6836                   | 1245                    |\n| 8   | Tri-plane 512^2             | -               | -        | 3x512x512                 | 53,423,246                 | 181.00                  | 47.77                     | 6450                   | 1333                    |\n| 9   | Spherical Tri-plane 512^2   | -               | -        | 6x512x512                 | 54,962,158                 | 182.28                  | 59.91                     | 6744                   | 1103                    |\n| 10  | Tri-grid 512^2              | -               | -        | 9x512x512                 | 54,002,318                 | 266.13                  | 58.79                     | 8642                   | 2463                    |\n| 11  | Hy-plane (3+1)              | -               | -        | 4x256x256                 | 53,269,388                 | 190.40                  | 43.64                     | 6966                   | 1095                    |\n| 12  | Hy-plane (3+1)              | evenly split    | -        | 1x512x512                 | 53,230,222                 | 206.54                  | 46.27                     | 7818                   | 1277                    |\n| 13  | Hy-plane (3+1)              | evenly split    | yes      | 1x512x512                 | 53,230,222                 | 207.21                  | 47.89                     | 7432                   | 1205                   |\n| 14  | Hy-plane (3+1)              | area-bias split | yes      | 1x512x512                 | 53,230,222                 | 226.31                  | 49.61                     | 7558                   | 1321                    |\n| 15  | Hy-plane (2+2)              | evenly split    | yes      | 1x512x512                 | 53,230,222                 | 212.27                  | 49.81                     | 7780                   | 1215                    |\n| 16  | Hy-plane (2+2)              | area-bias split | yes      | 1x512x512                 | 53,230,222                 | 219.73                  | 51.74                     | 8012                   | 1255                    |\n\nTo provide a comprehensive and fair comparison, we clarify the definitions of the metrics used in our evaluation. Representation Parameters refer to the number of parameters in the feature maps of different tri-plane-like representations (e.g., tri-plane, spherical plane, hy-plane, etc.). Total Learnable Parameters denote the total number of trainable parameters in the entire model architecture, such as EG3D, PanoHead, SphereHead, and HyPlaneHead. Training Speed and Training VRAM Usage are measured on a single V100 GPU with a batch size of 2, representing the average time and memory consumption required to train 1,000 images. Similarly, Inference Speed and Inference VRAM Usage are evaluated under the same hardware setup but with a batch size of 1, by generating 100 images and computing the average time and memory cost per image.\n\nFrom the statistics in the table, we can observe that different representations vary significantly in terms of feature plane parameter count. The tri-plane uses the fewest parameters (3 × 256 × 256 floating-point values), while the tri-grid with 512×512 resolution uses the most (9 × 512 × 512). Our proposed hy-plane uses 1 × 512 × 512 floating-point values, which is only 1.33 times the number used by the tri-plane. However, the overall difference in total learnable parameters across models is relatively small. The minor variations are mainly due to differences in the final convolutional layer configuration of StyleGAN2, which depends on how each representation is generated.\n\nNotably, the total learnable parameters for entries No. 3 and No. 5 are identical. This is because in experiment No. 3, we did not modify the model code at all, which means only the rendering pipeline was adjusted to use one spherical tri-plane. We reported the actual parameter count to remain consistent with our experimental setup.\n\nIn terms of training and inference speed, as well as VRAM usage, there are some differences among the experiments. Overall, however, the additional computational overhead introduced by our innovations is relatively small.\n\nReplacing the tri-plane with hy-plane introduces:\n* +5.5% training time,\n* +3.8% inference time,\n* +8.8% training VRAM,\n* −0.8% inference VRAM.\n\nAdding the evenly split strategy further increases the cost by:\n* +8.4% training time,\n* +6.9% inference time,\n* +12.2% training VRAM,\n* +16.6% inference VRAM.\n\nWhen combining with the near-equal-area projection, the overhead becomes:\n* +0.5% training time,\n* +3.5% inference time,\n* −5% training VRAM,\n* −5.7% inference VRAM.\n\nPlease note that in some cases, VRAM usage may actually decrease, likely due to memory fragmentation causing inaccuracies in the nvidia-smi measurement.\n\nIn summary, while our method does introduce some computational and memory overhead, the increase is relatively modest and justifiable given the significant improvements in disentanglement and reconstruction quality.\n\nIn addition, regarding Q2, which asks \"Could this method be extended to higher-resolution image generation without a super-resolution module?\" This is indeed a very insightful question. Current 3D-aware GANs typically rely on super-resolution modules to enhance the rendering resolution. However, it is possible that alternative strategies could achieve similar results. For example, Tri2-Plane [1] proposes a method of incorporating a feature pyramid into the tri-plane structure to improve fine-grained detail generation. Whether such an approach can fully replace the widely used super-resolution modules remains an open and interesting question. We consider this a promising direction for future work and will explore it in more depth in subsequent research.\n\n[1] Tri2-Plane: Thinking Head Avatar via Feature Pyramid, 2024\n\n\n**Q3: Single-View GAN Inversion Results:**  \n\nWe have conducted single-view reconstruction experiments using the same input image via PTI inversion. However, due to the NeurIPS’25 rebuttal policy, we are unable to include figures or external links in this response. Instead, we provide a qualitative discussion based on our results.\n\nThe results show that all 3D-aware GAN-based methods (EG3D, PanoHead, SphereHead, and our HyPlaneHead) are capable of reconstructing highly detailed front-view images that closely match the input, as PTI finetunes the model parameters to fit the target image. However, significant differences emerge in the quality of back-view reconstructions.\n\nSpecifically, EG3D and PanoHead often suffer from mirroring-face artifacts or lack sufficient expressiveness, leading to noticeable distortions in the back-of-the-head region. While SphereHead alleviates these issues, it tends to produce blurred and messy hair textures in the back view. In contrast, our method not only avoids such artifacts but also reconstructs clearer and more realistic details in the rear area.\n\nWe will include the full set of single-view GAN inversion results and their corresponding analysis in the final version of the manuscript."}, {"review_id": "BlWLFPTLdg", "reviewer": "Reviewer_gW4q", "summary": "The paper proposes HyplaneHead, a new re-parameterization of feature planes predicted by 3D GANs that operate with head portraits. The paper identifies two issues with commonly used output representations of 3D GANs which are tri-planes and spherical tri-planes, and proposes solutions to address these. First they identify the extensive interference between the planar output features in a tri-plane representation. This interference leads to the mixing of facial features across the different planes and often leads to strong artifacts when synthesizing novel views of the full head. For example, this interference can lead to frontal face features being visible when generating views of the back of the head. The authors argue that spherical tri-plane mitigate this issue to some level but unfortunately suffer from other drawbacks such as the poor spatial utilization of the square feature plane, leading to poor visual quality. To address these shortcomings, the paper proposes a Hyplane: a hybrid represent that has overcomes both drawbacks while leveraging the strengths of both tri-planes and spherical tri-planes. HyPlane is made up of two main contributions: i) The first is a lambertian azimuthal equal area (LAEA) projection scheme that maximizes the area occupied by spherical projection onto an rectangular plane. ii) The second contribution of the paper is a unify-split strategy where output planes in standard tri-plane representation are stitched onto a single plane to minimize cross-channel interference. Both improvements are analyzed through quantitative studies and lead to meaningful improvement in FID over the state-of-the-art.", "questions": "- Stitching the different planes into a single mosaic through the unify-split strategy implies that each plane now has a smaller effective resolution on the feature plane assuming the spatial resolution of the generator is kept the same as the baseline. The authors discuss a couple of different splitting strategies (even vs. biased). Does the smaller effective resolution for a dominating plane have any adverse effects on the quality of the results?    \n\n- The paper discusses HyPlane (2+2) as a generalized solution that could extend to general scenes. Did the authors try training a 3D GAN on general scenes as well?", "limitations": "The paper suffers from the standard limitations of 3D GANs that are trained with 2D image datasets including the flickering artifacts when generating novel views and its inability to generalize to diverse, out-of-distribution hairstyles, identities and so on. \n\nThe paper discusses both limitations.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "*Strengths*\n- The paper addresses a practical problem plaguing 3D GANs and proposes two working yet simple solutions to address them. I like the focus of the paper.  \n- Both contributions of the paper (the unify-split strategy and the LAEA projection) are shown to offer meaningful improvements in FID.\n\n*Weakness*\n- The primary criticism I have is around the presentation of results. I would have liked to see comparisons of the extracted surface for different methods. Specifically it would have been interesting to see what effect the two proposed improvements have on the underlying 3D surface.\n- The qualitative results are presented in a manner that makes it hard to compare the different methods. It would help a lot if the paper had text labels next to the different results (Chan et al., Li et al., Ours etc.). Currently one is forced to parse a large amount of text to understand which result is what. The same is true for most figures in the supplemental as well. The figure caption simply states “comparison” and one is left with no visual guidance to parse the figures. \n- While the paper addresses practical problems with 3D GANs used for generating 360 degree head portraits, I wonder whether its contributions are of sufficient novelty for NeurIPS.", "was_revised": true, "final_justification": "My concerns were addressed by the authors in the rebuttal. As long as the promised results are included in the final version of the paper, I am happy to recommend acceptance.", "rebuttal": "Thanks for your insightful review! Here are our responses to your concerns.\n\n**W1: Surface Comparisons**\n\nWe apologize that, due to the NeurIPS’25 rebuttal policy, we are unable to include additional figures or external links in this response. However, we would like to provide a qualitative discussion based on the geometry visualizations presented in the paper and some results not included in the main submission.\n\n*All surface/shape/geometry comparisons mentioned below were extracted from tri-plane-like representations using the Marching Cubes algorithm and saved as .mrc files for visualization with ChimeraX software. This is consistent with the extraction methods used in EG3D, PanoHead, and SphereHead.*\n\nFor the tri-plane representation, originally proposed in EG3D (as shown in Figure 5(a)), one can observe a face-like artifact appearing on the back of the head when viewing at 90°, 135°, and 180° angles. In the corresponding 3D surface, this manifests as an unnatural facial-shaped protrusion. This issue is commonly referred to as the \"mirroring-face artifact,\" as discussed in the SphereHead paper.\n\nFor the tri-grid representation, introduced in PanoHead, the addition of parallel planes enhances representational capacity, which alleviates the mirroring problem to some extent. However, a similar face-like artifact still appears on the back of the head. From the extracted surface, this artifact is less pronounced and more flattened compared to the tri-plane case. Nevertheless, the issue is not fully resolved. Moreover, both tri-grid and tri-plane always suffer from high-frequency grid-like noise patterns, which are visible as horizontal and vertical stripes, in certain regions of the extracted surface, especially around the back of the head. This is caused by the use of Cartesian coordinate projection for feature querying.\n\nFor the single spherical tri-plane representation, as proposed in SphereHead (Figure 2(f)), the extracted surface appears smoother but lacks fine details. This is primarily due to the uneven expressiveness across different spatial locations and the underutilization of the square feature map of the spherical plane. Additionally, polar artifacts appear on left and right sides and seam artifacts are visible on the back of the head.\n\nFor the dual spherical tri-plane representation, also from SphereHead (Figure 5(e)), while polar artifacts and seam artifacts are disappeared, the extracted surface looks even smoother. This is largely because each spherical plane uses its weakly expressive equatorial region to cover the other’s strongly expressive poles, leading to suboptimal overall detail and quality.\n\nIn contrast, our hy-plane representation (Figure 1(d) and Figure 5(j)) achieves a surface that retains rich geometric detail while eliminating the grid-like noise patterns seen in tri-grid/tri-plane methods. It also completely resolves the mirroring-face artifact, just as the spherical tri-plane does. We further observe that the introduction of the unify-split strategy eliminates cross-channel feature penetration artifacts. For example, the abnormal horizontal streaks often seen in hair regions in tri-plane/tri-grid methods (caused by features from Pyz leaking into Pxy) no longer appear in our method.\n\nAdditionally, the integration of the near-equal-area warping strategy (LAEA projection + elliptical grid mapping) significantly improves the clarity of fine details in the extracted surface. This is due to the more efficient utilization of the square feature map for the spherical plane.\n\nWe will include detailed surface comparison figures in the final version of the manuscript.\n\n**W2: Lack of Clear Visual Labels in Qualitative Comparisons**\n\nWe sincerely thank the reviewer for this valuable feedback. We agree that clearer visual labels would greatly improve the readability and comparison of our qualitative results. In the final version of the paper, we will revise all figures (including those in the supplementary material) to include explicit text labels next to each method’s output for better clarity and ease of comparison.\n\n**W3 & Q2: Novelty and Generalization to Beyond Full-Head Scenarios**\n\nWe appreciate the reviewer’s thoughtful question regarding the novelty and general applicability of our method.\n\nWhile the current work focuses on full-head generation due to data availability and practical relevance, we have also conducted preliminary experiments on other objects to evaluate the generalization potential of hy-plane. \n\nSpecifically, we collected multiple categories (e.g., cars, motorcycles, furniture, fruits, and houses) from public 3D model datasets (e.g. Objaverse, OmniObject3D and ShapeNet), and trained our model on each category. The preliminary results show that hy-plane achieves performance comparable to the standard tri-plane representation, particularly for symmetric objects such as cars and furniture. For asymmetric objects like fruits or houses, our method can produce more accurate asymmetric regions and generates fewer mirroring artifacts.\n\nFor example, when generating a house, although some houses exhibit symmetry in shape and window placement, they typically do not have two symmetric doors, i.e. one on each side. However, using a standard tri-plane tends to generate either two unrealistic \"doors\" or door-like artifacts on the opposite side. In contrast, our hy-plane naturally captures the asymmetry and typically generates only one realistic door, without any mirror-like artifacts on the other side.\n\nExploring the application of hy-plane to more general objects and scenes is an interesting and valuable direction. Due to time and data constraints during the rebuttal phase, we have not yet extended our experiments to more general scenarios such as object generation across arbitrary categories. We will include these additional experiments and discussions in the final version of the manuscript.\n\n**Q1: Resolution in Unify-Split Strategy**\n\nIn our experiments, we used a larger unified feature map of size 512×512 so that after splitting, each individual feature map would have the same resolution (256×256) as those used in tri-plane, tri-grid, and spherical tri-plane methods. Although this increases the model’s parameter count to some extent, our experiments show that the performance improvement is not due to the increased number of parameters. \n\n*Due to space limitations, please refer to the Representation Parameters and Total Learnable Parameters columns in the table provided in our response to Reviewer Xp1z (Q1 & Q2) for detailed information.*\n\nAs shown in Table 1 (rows 8, 9, and 10), even when the feature maps of tri-plane, tri-grid, and spherical tri-plane are also upscaled to 512×512, their performance does not improve compared to the 256×256 version. This indicates that the performance gain in our method is attributed to the novel representation design rather than an increase in model capacity. \nBesides, the same observation can be found in Table 2 of OrthoPlanes [1]. The first and second rows compare the same EG3D-based setup with only one difference: the former uses a 256×256 feature map, while the latter uses a 512×512 feature map. However, the FID score of the latter is actually higher than that of the former, demonstrating that simply increasing the feature map size does not necessarily lead to better performance.\n\nFurthermore, although we use a larger unified feature map (512×512), it has little impact on inference speed or GPU memory usage. Therefore, we believe the comparison between different methods remains fair and meaningful.\n\n[1] OrthoPlanes: A Novel Representation for Better 3D-Awareness of GANs"}, {"review_id": "8AVMvy0EBj", "reviewer": "Reviewer_abHE", "summary": "This paper analyses the limitations inherent in triplane-like representations used in 3D-GAN, and introduces the hy-plane representation, combining the strengths of both planar and spherical planes. To maximise its representation ability, a series of improvements are proposed. Experiments clearly demonstrate the advantages of the proposed representation.", "questions": "* Please clarify what form of disentanglement the proposed hybrid representation intends to achieve. It would help to include consistent visualizations or quantitative metrics (e.g., view separation score, plane correlation matrix) that support this claim.\n\n* How does your method compare to recent methods that also address triplane entanglement, such as OrthoPlanes [1] or SYM3D [2], a discussion is encouraged.\n\n* In your qualitative results, each method seems to use different conditions or reference views. How does your model perform in single-view settings under the same condition as other methods?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n* The motivation of this work is clear, and the proposed method appears practical and feasible.\n\n* The mixing algorithm for planar and spherical could generalize beyond the current application to other triplane-based 3D representations.\n\n* The ablation studies have shown how each component contributes.\n\nWeakness:\n\n* The disentanglement issue in triplane representations has been previously studied in works such as [1] and [2]. For example, [1] introduces additional depth dimensions within each triplane slice, and [2] applies symmetric regularization to improve cross-plane consistency.\n\n* In [2], the core problem is identified as the correlation across xy, yz, and xz planes, primarily caused by single-view synthesis. In contrast, multi-view synthesis tends to better capture front, bottom, and side views. In Figure 4 of the paper, it remains unclear how the proposed method effectively addresses this disentanglement.\n\n* The visualization does not clearly show distinct view-specific attributes. Clarifying the type of triplane disentanglement targeted by this work would strengthen the contribution.\n\n[1]OrthoPlanes: A Novel Representation for Better 3D-Awareness of GANs\n[2]SYM3D: Learning Symmetric Triplanes for Better 3D-Awareness of GANs\n\n* The issue of feature penetration across channels is discussed, but no quantitative metric is provided to evaluate this property. The paper would benefit from a clearly defined metric or analysis to assess this claim.\n\n* For the qualitative comparisons, the input or reference views vary across different methods, making direct visual comparison difficult. Including a consistent single-view reconstruction task, with matched inputs across all methods, would provide a fairer evaluation of visual fidelity and structural consistency.", "was_revised": true, "final_justification": "The rebuttal addressed most of my concerns.\n\nThe authors should include the additional results in the rebuttal, especially W4, and relation to the previous works (W1-2) in the revised paper.", "rebuttal": "Thanks for your insightful review! Here are our responses to your concerns.\n\n**W1 & Q2: Discussion and Comparison with Prior Works**\n\nThank you for pointing out these related works. We agree that discussing them is essential to the completeness of our paper, and we will incorporate the following analysis into the final version.\n\nComparison with OrthoPlanes:\n\nThe main difference between our method and OrthoPlanes lies in their design choices. OrthoPlanes introduces additional parallel planes within tri-plane to enhance expressiveness, which is similar to the tri-grid. In contrast, our method achieves disentanglement and effective learning of symmetric and asymmetric features by combining planar planes with spherical planes.\n\nAs a result, OrthoPlanes still suffers from the following limitations, which are effectively addressed in our approach:\n1. Incomplete resolution of mirroring artifacts: The root cause of mirroring artifacts lies in the use of Cartesian coordinate projection when querying features. As a result, both OrthoPlanes and tri-grid still suffer from mirroring-face artifacts and excessive left-right symmetry in certain regions.\n2. Increased storage requirements: When saving a sample (e.g., for downstream tasks such as 3D model reconstruction or initialization for methods like Portrait3D [1], AnimPortrait3D [2], and ID-Sculpt [3]), they must store K times more feature planes (where K is the number of added parallel planes) leading to a significant increase in memory usage.\n3. Incompatibility with unify-split strategy: Due to the increased number of planes, it becomes difficult to integrate these approaches with our unify-split strategy. As a result, inter-channel feature penetration remains an issue in these methods.\n\nComparison with SYM3D:\n\n1. Goal Difference\n    - SYM3D enhances the symmetry of tri-plane representations through symmetric regularization, which is beneficial for generating *fully symmetric artificial objects*.\n    - Our hy-plane is designed to support a broader range of real-world scenarios where *both symmetric and asymmetric structures coexist*, such as in full-head portraits.\n    \n    Nevertheless, we believe that the symmetric regularization used in SYM3D could be beneficial if selectively applied to the planar planes in our method, especially the Pyz plane, which is responsible for learning symmetric facial features such as ears. We will explore this possibility in future work.\n    \n2. Different Strategies to Address Feature Penetration\n    - SYM3D employs an attention-based scheme (View-wise Spatial Attention) to *learn how to alleviate* feature penetration across channels.\n    - Our hy-plane, utilizes the unify-split strategy to *geometrically and fundamentally prevent* feature penetration at its source.\n\n\n[1] Portrait3D: Text-Guided High-Quality 3D Portrait Generation Using Pyramid Representation and GANs Prior\n\n[2] Text-based Animatable 3D Avatars with Morphable Model Alignment\n\n[3] ID-Sculpt: ID-aware 3D Head Generation from Single In-the-wild Portrait Image\n\n**W2: Discussion and Comparison with SYM3D**\n\nThank you for introducing this related work. This is a very insightful and meaningful comparison, as it supports our findings from a different perspective.\n\nIn fact, the correlation across xy, yz, and xz planes discussed in SYM3D essentially corresponds to the inter-channel feature penetration problem we identify in our paper, though they observe it from a different angle. We detect this issue through visual inspection of feature maps (as shown in Figure 1(a,b)), while SYM3D quantifies it using correlation metrics.\n\nInter-channel feature penetration causes similar values at the same UV positions across different feature maps, resulting in visually similar patterns, exactly what can be seen in our Figure 1(a,b). This phenomenon is also visible in the top-right panel of Figure 7 in SYM3D, where GET3D’s feature maps exhibit repetitive vertical lines at the same spatial locations when zoomed in. Numerically, this leads to high correlations between different feature maps.\n\nThe difference lies in terminology: SYM3D does not explicitly refer to this as inter-channel feature penetration, but rather describes it as correlation.\n\nSYM3D attributes this issue to the use of single-view images during training, as opposed to multi-view data. While this is certainly true—more views provide richer geometric cues and reduce ambiguity—we also discuss this point in our paper (lines 65–71 and 224–227). From a more fundamental perspective, however, the issue arises due to the structural nature of convolutional networks, where different channels are prone to interfere with each other, especially in the absence of direct supervision. Our solution addresses this root cause directly by modifying the architecture via the unify-split strategy, which effectively eliminates inter-channel feature penetration at its source.\n\nIn contrast, SYM3D uses view-wise spatial attention to alleviate the issue. As shown in the bottom-right panel of Figure 7 in SYM3D, this approach does reduce correlation to some extent. However, as illustrated in the middle-bottom panel, strong correlations still exist between certain planes, particularly Pyz and Pxz. We have also computed and visualized similarity matrices similar to those in SYM3D's Figure 7, and our results show significantly lower feature correlations across different planes. Please refer to our response to W4 & Q1 below for specific quantitative results.\n\nMoreover, in terms of implementation complexity, our unify-split strategy is simpler and more intuitive compared to SYM3D’s view-wise attention mechanism, while achieving a more complete resolution of the problem.\n\nWe will include this detailed comparison in the final version of the manuscript to further clarify how our method improves upon previous approaches.\n\n\n**W3 & Q1: View-Specific Disentanglement Visualization**\n\nThank you for pointing out the need for clearer visualization of view-specific disentanglement. Due to the NeurIPS’25 rebuttal policy, we are unable to include additional figures or external links in this response. However, we would like to provide a qualitative explanation based on Figure 4 in the paper.\n\nAs designed in our hy-plane representation, the planar planes learn highly symmetric features, such as ears and shoulders, which are captured by the Pyz plane. On the other hand, the spherical plane captures asymmetric information such as the front face and back hair. This clear separation between symmetric and asymmetric features is a key aspect of the disentanglement achieved by our method.\n\nWe will add more detailed visualizations and explanations of view-specific feature learning in the final version of the manuscript to further clarify this point.\n\n\n**W4 & Q1: Missing Quantitative Evaluation for Feature Penetration**\n\nThank you for this valuable suggestion. We agree that a quantitative evaluation is essential to validate the effectiveness of our method in reducing feature penetration across channels.\n\nWe recognize that the similarity matrix is a suitable metric for measuring inter-plane correlation and thus serves as an effective indicator of feature penetration. However, due to the NeurIPS’25 rebuttal policy, we are unable to include visualizations or figures in this response.\n\nInstead, we show the similarity matrix below and we will add it in the final manuscript. The results demonstrate that hy-plane(3+1) with the unify-split strategy achieves significantly lower correlations between different planes compared to hy-plane(3+1) without the unify-split strategy, indicating that the unify-split strategy effectively reduces inter-channel feature penetration.\n\n```\nSimilarity Matrix of hy-plane(3+1) with unify-split strategy:\n[[ 1.          0.13848868  0.19367712 -0.24672825]\n [ 0.13848868  1.         -0.11731562  0.31794619]\n [ 0.19367712 -0.11731562  1.         -0.47516028]\n [-0.24672825  0.31794619 -0.47516028  1.        ]]\n\nSimilarity Matrix of hy-plane(3+1) without unify-split strategy:\n[[1.         0.59808226 0.63007857 0.4739821 ]\n [0.59808226 1.         0.6070948  0.71127384]\n [0.63007857 0.6070948  1.         0.48041942]\n [0.4739821  0.71127384 0.48041942 1.        ]]\n```\n\n**W5 & Q3: Comparison in Single-view Setting**\n\nThank you for raising this important point.\n\nWe have conducted single-view reconstruction experiments using the same input image via PTI inversion. However, due to the NeurIPS’25 rebuttal policy, we are unable to include figures or external links in this response. Instead, we provide a qualitative discussion based on our results.\n\nThe results show that all 3D-aware GAN-based methods (EG3D, PanoHead, SphereHead, and our HyPlaneHead) are capable of reconstructing highly detailed front-view images that closely match the input, as PTI finetunes the model parameters to fit the target image. However, significant differences emerge in the quality of back-view reconstructions.\n\nSpecifically, EG3D and PanoHead often suffer from mirroring-face artifacts or lack sufficient expressiveness, leading to noticeable distortions in the back-of-the-head region. While SphereHead alleviates these issues, it tends to produce blurred and messy hair textures in the back view. In contrast, our method not only avoids such artifacts but also reconstructs clearer and more realistic details in the rear area.\n\nWe will include the full set of single-view GAN inversion results and their corresponding analysis in the final version of the manuscript."}, {"review_id": "Sd0Pp6G0mk", "reviewer": "Reviewer_QRHG", "summary": "This paper addresses the challenging task of generating high-fidelity 3D head avatars. Building upon the EG3D framework [1], the authors introduce a novel hybrid triplane representation that effectively combines spherical and planar planes. To enhance feature learning, they propose two key contributions: Unify-Split Strategy, preventing feature disentanglement across triplane channels by unifying and then splitting feature maps and improved warping Formulation, a refined warping method for better spherical plane representation. Their approach is highly flexible, allowing adjustments in the number of planes and their configurations to suit different tasks. Extensive experiments demonstrate superior performance over existing methods, including recent state-of-the-art techniques like PanoHead [2] and SphereHead [3], both quantitatively and qualitatively.", "questions": "1. Could the merged triplane structure with single channels and splitting approach support triplane-localization as detailed in [4] for fine grained editing?\n2. Could the authors show what each of the planes learn/represent in the output domain by just sampling from planar planes or single spherical planes, if possible.\n3. Methods like LRM [5] and InstantMesh [6] also utilize triplane structure. In your opinion, how would your method improve the generation quality of those models?\n4. Could the authors provide some examples of single view GAN inversion using your model’s triplane representation as in [2]?\n5. The 2+2 approach should be supported further, since there are no visual samples to compare with 3+1. Could the authors provide an accompanying qualitative comparison to Table 1, where visual outputs of No 11-16 are compared? With this new figure, it would be also interesting to see how mapping network conditioning camera parameter affects the rendering results, with different camera parameters (FID vs. FID-random).\n\nI am willing to increase my score a point once the questions and weaknesses have been adequately addressed.\n\n[1] Chan et al., EG3D: Efficient Geometry-aware 3D Generative Adversarial Networks, CVPR 2022\n\n[2] An et al., PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360°, CVPR2023\n\n[3] Li et al., SphereHead: Stable 3D Full-head Synthesis with Spherical Tri-plane Representation, ECCV2024\n\n[4] Bilecen et al., Reference-Based 3D-Aware Image Editing with Triplanes, CVPR2025\n\n[5] Hong et al., LRM: Large Reconstruction Model for Single Image to 3D, ICLR 2024\n\n[6] Xu et al., InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models, 2024\n\n[7] Bilecen et al., Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images. NeurIPS 2024\n\n[8] Tri2-plane: Thinking Head Avatar via Feature Pyramid, 2024", "limitations": "The limitations are adequately addressed.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper is well-written with great explanations and visuals in provided in the supplementary material.\n2. The paper nicely identifies the feature penetration issue present in the multi-channel triplane representation and proposes a method called Unify-Split Strategy to mitigate the issue in a novel and intuitive way.\n3. The flexibility of splitting is well-presented. The connection between (2+2) representation of spherical planes and ability to make one region smaller while keeping other larger to “cover” the problematic areas is a good idea.\n4. The method is generalizable to different needs. As the authors state, while using 3+1 combination for head domain one could hide the disappearing pole at the bottom of the head but when it comes to general domain they need two spherical planes with their south poles facing opposite ways. The fact that the method can support different needs with simple modifications shows the generalizability of the method.\n5. Their approach solves the low quality 3D structure problem present in SphereHead due to merging of spherical planes in an elegant way by combining planar planes and spherical planes.\n6. Using LAEA (Lambert Azimuthal Equal-Area) Projection is a good idea as the authors explain the shortcomings of using spherical coordinates such as uneven feature distribution and numerical discontinuities.\n\nWeaknesses:\n1. Even though it is mentioned in the checklist, the paper does not detail the VRAM usage and a comparison for inference durations for different models. This is needed to measure how much overhead is added by the introduction of hybrid representation.\n2. The authors only use FID metric to measure quantitive performance. Supporting FID with IS (Inception Score) as in [2] could better support the findings.\n3. The authors do not include results for diverse ethnicities and skin colors. Addition of such results are required to show the model’s ability to generalize.\n4. For the sake of completeness, it would be good to add some other related works based on triplanes [7,8] to the discussion.", "was_revised": true, "final_justification": "I find the rebuttal adequate, hence I will increase my final score. However, I expect to see the results of Q4 and Q5 to be in the final paper & supplementary material.", "rebuttal": "Thanks for your insightful review! Here are our responses to your concerns. We will include the following content in the final version of the paper and the supplementary material.\n\n**W1: Inference Speed and VRAM Using**\n\n*Due to space limitations, please refer to our reply to Reviewer Xp1z, Q1 & Q2*\n\n**W2: Additional Quantitative Metrics**\n\nFollowing PanoHead, we compute the Inception Score (IS) for our HyPlaneHead model. The IS results of Table 1 (from No. 1 to No. 16) are as follows:\n3.72, 3.86, 3.99, 4.06, 3.96, 3.61, 4.01, \n3.76, 4.03, 3.97, \n4.10, 4.17, 4.22, 4.18, 4.21, 4.21\n\n**W3: Diversity in Qualitative Results**\n\nSimilar to previous works (e.g., EG3D, PanoHead, SphereHead), our training data includes widely used portrait datasets such as FFHQ, CelebA. These datasets are typically collected from public sources, where Caucasian individuals constitute the majority, while East Asian and South Asian individuals are present in smaller proportions. Other ethnic groups are underrepresented. As a result, the data distribution directly influences the randomly sampling results of the model. Therefore our method tends to generate predominantly Caucasian faces, with occasional East or South Asian characteristics, as seen in Supplementary Figure 3 (images 30 and 32).\n\nHowever, this does not necessarily mean that our model is unable to handle underrepresented ethnicities or skin tones. Instead, it reflects the fact that the sampling distribution aligns with the distribution of the training data. By increasing the representation of diverse ethnicities and skin tones in the training set, we expect the model to generate a broader range of identities accordingly.\n\nMoreover, when using GAN inversion techniques (e.g., PTI) on images from underrepresented groups, such as those of African descent, we consistently observe reasonable reconstructions of full-head 3D models. This is because our model has learned a rich and accurate distribution of head shapes and appearances through exposure to large-scale data, which provides a certain level of generalization. For out-of-distribution samples, targeted adaptation via inversion can further yield reasonable results.\n\nSince we are unable to include these results here via figures or external links, we will add them to the final version of the manuscript.\n\n**W4: Discussion with Related Work**\n\nDual Encoder [7] introduces a dual-encoder GAN inversion approach for single-view 3D full-head reconstruction. It uses one encoder for the visible front and another for the occluded back region, addressing issues like mirroring artifacts in PanoHead’s W space.\n\nIn contrast, our HyPlaneHead improves the representation structure to reduce such artifacts and enhance W space quality. As a result, standard inversion methods like PTI, which is a general-purpose technique for common GANs, already yield better performance than previous approaches. We believe that combining our method with specialized inversion strategies like Dual Encoder, which are specifically tailored for full-head generation, could further improve results. We will explore this possibility in the final version of the paper.\n\nTri2-plane [8] introduces a cascaded triplane representation across multiple scales of facial features, similar to a feature pyramid. This hierarchical design allows the model to generate both global and fine-grained details, leading to richer and more detailed head reconstructions. We believe that the idea of multi-scale feature pyramids could be beneficial to integrate with our hy-plane representation in future work.\n\n**Q1: Compatibility with Triplane-Localization for Editing**\n\nWe have carefully read the paper, and find the proposed triplane-localization method has been successfully applied to both tri-plane (EG3D) and tri-grid (PanoHead) representations.\n\nGiven that our hy-plane shares a similar structure, we believe the method should be directly applicable to our representation as well. Moreover, we do not expect the combination of planar and spherical planes or the use of the unify-split strategy to interfere with the application of this localization approach.\n\n**Q2: Interpretability of Individual Planes**\n\nSince we are unable to include figures, we provide a qualitative explanation instead.\n\nFirstly, when rendering using only a single planar or spherical plane, the resulting output is barely informative. This is because, for a planar plane, all rays passing through the same point on that plane share the same feature value, leading to visual artifacts such as parallel lines that reflect the shape of the feature map itself. Similarly, when visualizing only the spherical plane, the output consists of many radial lines extending from the center of the sphere, which do not convey meaningful 3D structure.\n\nInstead, we can infer the contribution of each plane by removing one feature map at a time and observing the impact on the final rendering.\n\nWhen the spherical plane is removed among the four feature planes, we observe significant missing regions in the facial area and back-of-the-head hair in the rendered result. This indicates that the spherical plane has learned to represent asymmetric features such as the front face and rear hair. However, structures on both sides, such as the shoulders and ears, remain mostly present, though with some degree of distortion.\n\nWhen any one of the three planar planes (e.g., Pxy, Pyz, Pxz) is removed, significant distortions appear in the reconstructed head shape. This is because, in addition to carrying meaningful feature information, these planes also serve a role similar to \"positional encodings\" during training. If one of them is absent during inference, the positional representation becomes inconsistent with that used during training, resulting in structural degradation.\n\nAmong the three, the Pyz plane contributes the most to 3D structure, as its removal leads to a noticeable loss of symmetric features such as ears and shoulders. Removing the other two planar planes also introduces distortion, though to a lesser extent.\n\n**Q3: Discussion with Other Triplane-Based Methods (LRM, InstantMesh)**\n\nWe believe that replacing the standard triplane representation in methods like LRM [5] and InstantMesh [6] with our hy-plane has the potential to improve their generation quality.\n\nOn one hand, our unify-split strategy eliminates inter-plane feature penetration, allowing each plane to express its features more clearly and effectively. On the other hand, by incorporating a spherical plane, we enhance the model’s ability to represent asymmetric regions, such as facial details and hair on the back of the head.\n\nTherefore, we expect that integrating hy-plane into these models would lead to more accurate and artifact-free 3D reconstructions.\nDue to time constraints during the rebuttal phase and the lack of prepared data for such integration experiments, we have not yet conducted these evaluations. We will explore this direction experimentally in future work and include the results in the final version of the paper.\n\n**Q4: Single-View GAN Inversion Examples**\n\n*Due to space limitations, please refer to our reply to Reviewer Xp1z, Q3.*\n\n**Q5: Qualitative Comparison between 2+2 and 3+1 Configurations & FID vs. FID-random**\n\nSince we are unable to include additional figures in this response, we would like to describe our findings qualitatively here and will incorporate detailed visual comparisons into the final version of the manuscript.\n\nNo. 11, which does not use the unify-split strategy, still suffers from inter-channel feature penetration. This is visible as irregular horizontal patterns in the hair or color artifacts on clothing, which is actually because of features that leak across different channels. Nevertheless, the overall quality is already better than the baseline methods.\n\nNo. 11 and 12 also lack the near-equal-area warping, resulting in slightly blurred fine details, especially in the texture of the back-of-the-head hair.\n\nThe results of No. 13–16 show very small differences, which can only be observed through careful and extensive comparison. The main distinction lies in the orientation of the spherical plane. In No. 15 and 16, one of the spherical planes has its north pole aligned with the front of the face. Since the LAEA projection minimizes distortion around the pole, these configurations capture more facial details, leading to a slightly sharper facial appearance. Meanwhile, No. 14 and 16, which use an area-biased split, enhance the expressive power of the spherical plane, allowing for better reconstruction of asymmetric features, which is consistent with the quantitative results in Table 1.\n\nRegarding FID vs. FID-random, as discussed in the paper, we observe that 3D-aware GANs tend to generate higher-quality results in regions aligned with the conditional camera view. For example, when the camera is conditioned to the left side of the head, the left side is well-generated, while the right side tends to be blurry or distorted. This is why we introduced the FID-random metric to evaluate how well the model generalizes across arbitrary viewpoints.\n\nAcross all 3D-aware GANs, it is common for FID-random to be higher than FID, and they reflect the same trend. However, we want to highlight an interesting anomaly in No. 2: although adding the unify-split strategy to tri-plane reduces FID, it actually increases FID-random. This occurs because the strategy eliminates inter-channel feature penetration and allows each plane to express its directional features more fully. However, since the tri-plane does not explicitly separate directional information, this enhanced expression leads to stronger mirroring artifacts on the backside of the head, thereby worsening the FID-random score."}]

Author rebuttal:

Meta-review: All four reviewers rated the paper positively, with concerns addressed convincingly in the rebuttal. HyPlaneHead’s ​hybrid-plane representation​ effectively eliminates mirroring artifacts in 3D head synthesis by combining planar/spherical planes with innovations like ​near-equal-area warping​ and a ​unify-split strategy. Quantitative results show significant FID improvements over SOTA methods while rebuttal responses thoroughly addressed concerns in computational over head, generalizability, ​inversion quality​ etc. The rebuttal is successful and reviewers' concerns are mostly resolved.
Decision: Accept (poster)