HairFree: Compositional 2D Head Prior for Text-Driven 360° Bald Texture Synthesis
Authors: Mirela Ostrek
Michael J. Black
Justus Thies
Venue: NeurIPS 2025 poster ()
Keywords: Text-to-Image Synthesis
Human Head Texturing
3D Face Synthesis
Facial Texture Generation
FLAME Head Mesh Texturing
Generative Texture Models
3D Texture Mapping
Image Synthesis
Bald Textures
Primary area: applications

Abstract: Synthesizing high-quality 3D head textures is crucial for gaming, virtual reality, and digital humans. Achieving seamless 360° textures typically requires expensive multi-view datasets with precise tracking. However, traditional methods struggle without back-view data or precise geometry, especially for human heads, where even minor inconsistencies disrupt realism. We introduce HairFree, an unsupervised texturing framework guided by textual descriptions and 2D diffusion priors, producing high-consistency 360° bald head textures—including non-human skin with fine details—without any texture, back-view, bald, non-human, or synthetic training data. We fine-tune a diffusion prior on a dataset of mostly frontal faces, conditioned on predicted 3D head geometry and face parsing. During inference, HairFree uses precise skin masks and 3D FLAME geometry as input conditioning, ensuring high 3D consistency and alignment. We synthesize the full 360° texture by first generating a frontal RGB image aligned to the 3D FLAME pose and mapping it to UV space. As the virtual camera moves, we inpaint and merge missing regions. A built-in semantic prior enables precise region separation—particularly for isolating and removing hair—allowing seamless integration with various assets like customizable 3D hair, eyeglasses, jewelry, etc. We evaluate HairFree quantitatively and qualitatively, demonstrating its superiority over state-of-the-art 3D head avatar generation methods. https://hairfree.is.tue.mpg.de/

HairFree: Compositional 2D Head Prior for Text-Driven 360 Bald Texture Synthesis
Mirela Ostrek 1.2
Michael Black'
Justus Thies 1.2
Max Planck Institute for Intelligent Systems TTechnical University of Darmstadt
Abstract
Synthesizing high-quality 3D head textures is crucial for gaming, virtual reality, and digital humans. Achieving seamless 360 textures typically requires expensive multi-view datasets with precise tracking. However, traditional methods struggle without back-view data or precise geometry, especially for human heads, where even minor inconsistencies disrupt realism. We introduce HairFree, an unsuper- vised texturing framework guided by textual descriptions and 2D diffusion priors, producing high-consistency 360 bald head textures textures-including non non-human skin with fine actails-withord any texture, back-view, bald, non-human, or synthetic dataset of mostly frontal faces,
training data. We fine-tune diffusion prior conditioned on predicted 3D head geometry and face parsing. During inference, HairFree uses precise skin masks and 3D FLAME geometry as input conditioning, ensuring high 3D consistency and alignment. We synthesize the full 360 texture by first generating frontal RGB image aligned to the 3D FLAME pose and mapping it to UV space. As the virtual camera moves, we inpaint and merge missing regions. A built-in semantic prior enables precise region eeparatin-ppatticllee lating and removing hair allowingg seamless integration with various assets like customizable 3D hair, eyeglasses, jewelry, etc. We evaluate HairFree quantitatively and qualitatively, demonstrating its superiority over state-of- sate-of-the-art 3D head avatar generation methods. https://hairfree..ss.eeee
for iso-
Introduction
Generating realistic and consistent textured 3D head avatars is essential for applications in gaming, virtual reality, and digital human modeling. Achieving 360 appearance consistency in head texuring is persistent challenge, especially, because scalp visibility varies dramatically with dynamic hair animations and different hairline shapes (e.g., straight, rounded, widow's 's peak, M- or V-shaped,
receding), Figure] I Existing methods 16 14 28
30 40
31 34 64 2 70) either bake
hair (and its specific hairline) directly into the texture leading to visible artifacts when swapping to any other hairstyle-or limit textures to the facial region, neglecting the full scalp. Moreover, other approaches rely on large-scale multi-view datasets of human heads with precise tracking, making them -intensce-iteesivve and less flexible.
This lack of flexibility undermines true compositionality, where any 3D hairstyle or other assets (hats, helmets, eyeglasses, jewelry) can be seamlessly added, swapped, or animated without visible seams or mismatches. For practical applications, fully disentangled, hair-free scalp and face texture crucial, serving as neutral base for compositional 3D layering. In this paper, we introduce HairFree, an unsupervised generative texturing framework that generates high-quality, 360 head textures, providing fully bald, neutral base for 3D asset integration. Unlike existing methods, HairFree diffusion- based inpainting approach guided by textual descriptions and produces consistent, detailed head textures without relying any texture, back-view, bald, non-human, synthetic training 39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Bald
Bald
Bald
Widdle-Agge East Asian
Elderly Indigenous
Widdle-Aged African
Figure 1: HairFree is hybrid 2D/3D neural rendering method that synthesizes diverse, high-fidelity 360 bald human head textures given 3D head mesh and textual description. The generated bald textures (columns 1, 7) seamlessly integrate into classical graphics pipelines, allowing compatibility of hairline type (in green)-in fully compositional manner.
with any 3D
data. Our method is based on compositional 2D human head prior, trained on 100K mostly frontal images of faces, conditioned RGB background, 3D head geometry, and partial facial semantics. The 3D head geometry and facial semantics are estimated using off-the-shelf methods (Spectre 12] FPM (8)).
At inference time, HairFree begins by generating a high-quality frontal view image of face, conditioned accurate 3D FLAME geometry [35] and precise skin masks. This initial image is projected onto the 3D FLAME texture space, creating a base texture. As the virtual camera moves around the head, progressive inpainting process is applied upplied-rendering the visible regions and filling in missing areas using the diffusion model image space. These completed regions are then re- re-mapped to texture space, maintaining texture consistency. The entire process is guided by the same 3D-consistent FLAME geometry and skin masks, ensuring precise alignment across all views. The semantic conditioning allows for skin-hair separation, effectively isolating and removing hair, producing a clean bald texture. The final result is a complete 360 bald head texture, fully compatible with various 3D assets, such as strand-based hairstyles (Figure
Our results demonstrate that HairFree generates high-quality, consistent, and detailed 360 textures for fully bald heads, including both human skin (Figure 4 and non-human skin textures (Figure 3). This opens new possibilities for creating customizable 3D head avatars using textual descriptions, without the need for extensive multi-view datasets supervised training. Finally, evaluate HairFree both quantitatively and qualitatively, showing its superiority over state-of-the-ar the-art methods.
In summary, we make the following contributions:
Compositional 2D head prior: diffusion model that generates high-qualice photorealistic face images, conditioned on 3D geometry, facial semantics, and text prompts, enabling precise separation of hair and skin regions.
360 bald head texturing pipeline: robust, unsupervised texturing method that synthesizes consistent, high-quality 360 bald head textures from text prompts. The pipeline leverages compositional 2D head prior in combination with generic inpainting prior. This method integrates image-space inpainting with texture mapping onto a FLAME--based head mesh in UV space. It generates full head textures without relying on any texture, back-view, bald, non-human, or synthetic data, and generalizes to both human and non-human skin types.
A dataset of 1,000 generated, high-quality, photorealistic human head textures, providing a diverse and scalable resource for realistic avatar and face model development.
Related Work
Related work spans three areas: 2D image synthesis, mesh texturing, and 3D character generation.
While GAN- and diffusion-based models excel at face image creation and 3DMM or reconstruction methods yield plausible head textures, none offer a compositional 2D prior that cleanly separates hair and skin for full-head texturing on arbitrary meshes. Below, we review key advances in each area and show how approach fills this gap.

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2D Image Synthesis:
Generative Adversarial Networks (GANs) 19 and their StyleGAN suc-
cessors 25
23
24
3 have set the standard for photorealistic image generation across objects and
human faces 61
62
56 with StyleAvatar extending StyleGAN to texture maps for 3DMM-hased
avatars 665 63
More recently, diffusiin models [48] 52 51
55 45 4 trained on LAION-5B [57]
surpass GANs in quality and diversity, powering robust text-to-image synthesis [54] and supporting fine-grained control through arbitrary mmage-condttionng-- landmarks, segmentation masks, depth maps, rendered geometry via ControlNet [71]. We fine-tune Stable Diffusion with ControlNet guidance for our avatar texturing pipeline.
conditioning to provide precise,
Texturing and Face Textures: 3D morphable models (3DMMs) [10]. such as the Basel Face Model [47]. use PCA on textured scans to represent facial geometry and texture, becoming standard for face tracking 72 and neural rendering techniques like NeRF 43] and Gaussian splatting [27]. However, their texture spaces lack diversity due to limited 3D data used to create it. Methods like Flame'Tex [I]. Slossberg et al. [59] Gecer et al. [66] 17] and CLIPFace ( expand texture variety using in-the-wil wild images. CLIPFace uses the FLAME model [35] with a StyleGAN-like architecture for high-quality textures. DreamFace (70 uses CLIP-based selection for coarse geometry. then refines details with Score Distillation Sampling (SDS), combining generic and texture latent diffusion models to generate diverse, high-quality frontal textures of the facial region. FitMe (29) (GAN inversion), Relightify [46] (diffusion), and Luo et al. [40] (StyleGAN) reconstruct photorealistic facial textures directly from images but also cover only faces, not full heads, same UV-IDD (33).
Most of these methods bake at least some hair into the texture, preventing compositional layering with separate hair assets.
3D Character Generation: Several recent works generate 3D textures and geometry via "gen- eration by reconstruction." synthesizing multiple 2D views and then lifting them into 3D B7 49
53 7 General-purpose methods like TEXTure (53, Text2Tex (6. and SceneTex
use Iiffusion-based priors in a generate-then-refine pipeline to texture arbitrary objects and scenes. Human-specific approaches include TADA 36 and HoranNorm 21 which recover full body assets via DMTet [58) and SDS optimization
49 and FaceLift [4], which directly predicts multi- view im- ages with a latent diffusion model before reconstructing with Gaussian splatting. TECA [69] further introduces mresh-volumetrie disentanglement of skin and hair under an SDS loss. Are2Avatar [18) produces high-quality 3D heads from single images by leveraging a human face foundation model and full 3DMM integration for superior realism and identity preservation. None of these methods designed to generate specifically bald textures or fully disentangle hair. In contrast, our compositional 2D diffusion prior disentangles hair from skin in a single-stage process, yielding fully editable, high-fidelity 360 head textures.
Preliminaries: Diffusion Models
Our 2D human head prior is latent diffusion model (LDM) 54), fine-tuned to transform rendered head meshes, partial face parsing masks, and RGB backgrounds into photorealistic human heads.
Denoising Diffusion Probabilistic Model (DDPM): In the DDPM framework [20] noise- corrupted samples are progressively denoised over T timesteps. The forward process adds Gaussian noise (0, I) in I' steps using variance schedule {
xxx:|x=-1 1) N(x;;; v 11x
The reverse denoising process, parameterized by approximates Po 1xx):
(x [x,) = (Nx (x,, t). Eg (x,, t)):
(2)
with an objective function:
CDDPM Ex,,,
(x 1]]33
Latent Diffusion Model (LDM)): Operating in the latent space, LDMs [54] use a pre-trained VAE to map images to latent codes, reformulating the objective as:
LLMM
[||e e (Z,, 1|
where z, is the latent code at timestep t.
3

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[A] Data Procssiing
(8.1) 20 Prior (Training)
(C) Textured Avetar
LGM
Dercking Gater-Speed
Prompt
Mapping
Merging
Cataset
Q
Cam, View Dependent
Compostioona Pamne
D
Input LDM Conditions
8
Pröor
BY Spare
hountan
Ciriiee
30 FLAMR
Inpainting Mush
RGBl image
Denied mos Cordnriiinn
10 Skin Eurs
(LOW Ovipuil
(Inference):
Views/ Inpainiing ng
Sdts Siaas:
22 LiWt
1
Our Caffeer
Charnel
Chonsel2
Channel
Figure 2: System Overview: (A) Data Processing: Estimate face parsing, 3D head mesh, and remove the foreground to form compositional inputs. (B.1) Training Prior: Fine-tune an LDM via ControlNet using these imputs and generic "face" prompt. (B.2) Inference Prior:
Swap in generic LDM-Inpainting prior. (C) Texture Generation: Generate a frontal view, map to UV space, iteratively render "seen" and inpaint "unseen" regions while moving the camera, building full 360 texture.
4 Method
Figure outlines our system. We first train a compositional 2D head prior via latent diffusion on predominantly frontal face images, conditioned on rendered FLAME meshes, face parsing masks, and backgrounds. At inference, we use the FLAME model to condition the generation of a frontal view, project into UV space, and then iteratively fill in unseen texture regions. The following sections describe (1) our diffusion prior and its training and (2) the progressive texturing process.
4.1 Compositional 2D Human Head Prior
We fine-tune Latent Diffusion Model (LDM) [54] using ControlNet 771 to generate high-quality images conditioned on specific input features. ControlNet operates as a trainable copy of the base diffusion model (Stable Diffusion 2.1 (54). which is kept frozen in locked mode during training. This ensures stable training while ControlNet learns to map conditions from a dataset of 100K images
(FFHQ 26 and CELEB-A-HQ (22)
We condition our model using a set of inputs, collectively denoted as C, which include skin masks (Cakin), hair masks (Chair), cars and accessory masks (Cears), 3D head mesh (Cmesh) rendered in 2D, and background information (Chackground). These conditions are combined as follows:
C {Cssinn Chairy Cears Cmesh: Cbackground
(5)
The training objective is defined
LCOND E. C.owN..) [||e ( (t)
(6)
To further enable text-based conditioning, we employ CL.IP text encoder, transforming prompts into embeddings that guide image generation via cross-attention layers. Classifier-fie guidance (CFG)
applied to balance text adherence and image quality, adjusting the model's prediction as:
(Z C) (1 w) EZ(Zes C) w ea ez(2),
where w controls the guidance strength.
Guided Inpainting: At inference, we replace the locked LDM with an inpainting variant, allowing for guided completion missing texture regions. We introduce inpainting mask (Cmas) specifying areas to be filled, while maintaining coherence with the existing texture using the same conditioning set The inpainting objective defined as:
LIP Ezo i. e>.(.1) [|c- co (Z t, C, Cmask)
(
Training Details: Our prior is trained for 1500 GPU hours on an NVIDIA H100 (see [54
71

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4.2 3D Texturing Pipeline
First, we render skin and ear masks aligned to the 3D FLAME model B5] from multiple viewpoints and use them to condition our 2D diffusion prior, producing consistent pixel outputs. Next, these pixel colors are projected onto the mesh's UV atlas in an iterative process: visibility checks ensure only previously untextured regions are filled, while morphological erosion and bilinear interpolation refine boundaries and smooth transitions. Together, these steps yield a high-quality 360 head texture.
Rendering 3D-Consistent Input Controls: At test time, we generate 3D-consistent conditioning signals using the FLAME model. Specifically, we extract skin and ear maps aligned with the FLAME mesh, while omitting features like hair, earrings, and eyeglasses to ensure clean, bald head generation. The background is set to a uniform color to minimize artifacts, keeping the model's focus on the head region. The FLAME model, 3D Morphable Model (3DMM), parameterizes human head shapes and expressions through a low-dimensional latent space. It outputs a 3D head mesh M = fFLAME (a, 6, f). where M is represented by vertices {v; E R controlled by shape parameters a, expression parameters 6, and pose parameters 0, enabling precise manipulation of head structure and expressions.
Ji=1 and fixed topology of faces F. These vertices are
To capture a complete 360 representation, we render the mesh from 14 viewpoints by rotating a virtual camera around the head. Each vertex V; = (z,, using a perspective transformation, where the camera intrinsies K and extrinsics R. define the projection as p: (K(Rv, + t)). The perspective division is given by π m(x) = 2) for 3D point (z, y, 2)
Ve. 2;) is projected onto the 2D image plane
These rendered meshes and accurate skin/ear segmentation masks serve as the conditioning signals for our compositional 2D image prior.
Progressive UV Mapping: Guided by our 3D-consistent input controls, the generated images of the 2D prior align with the FLAME mesh. Each vertex v; on the 3D mesh has a corresponding UV coordinate (u,, 0z), allowing us to map surface points to a 2D texture space. For each view, visible pixels the 2D render are mapped to UV coordinates (u, v) based on the surface-to-UV correspondence. Each pixel (z, y) in the rendered frame has an RGB color value col(x, y) = vrgg. b).
which we splat to the texture space at the corresponding (u, v) locations.
Instead of computing the images of all views at once, we iteratively render the images using already existing texture parts. To improve texture quality and avoid blending artifacts near boundaries, we apply a morphological erosion operation to the existing UV texture mask before accumulating new color information. Specifically, for pixel (x, v), we compute its corresponding UV coordinates (u, v)) using precomputed channels:
Uchamnet (v) R
Uchannel (V,, x) RJ
where Mccannel and Vchannel are UV maps providing (u, v) coordinates for each pixel and R denotes the image resolution. These (u, v) values are rounded down the nearest integer for indexing into the texture map.
We conditionally update the UV texture based visibility checks, ensuring that only new visible regions accumulate:
col((, v) visible at (u, v), T(u v) = T(u otherwise.
(10)
For each pixel (z, y) with color col(r, v) [r, g, b] in the 512 x 512 image space, we compute its UV
coordinates on the 1024 1024 atlas and define uig = [u]. un = [u] un = [w]. and in = [v]. We
then "splat" col(x, y) into each of the four texels (ux: Ue) E { vo), (W1, vo). (uo, v1), (u1, v1) by setting:
T(us, vz) col(z, y) if that texel is not yet filled. This effectively closes small gaps and holes in the accumulated texture. Applying this process across all pixels and viewpoints yields full 360 UV texture.
(11)
5

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Image/ Text
Ours
Facelift
ArclAvatar
DiffSplat
HumanNorm
DreamCCraft3 Icu
Mark Ruffalo"
Magic123 10X 2034
TECA
Tada! x7x 2014
Relightify I02V 2013
Zero1-3 tce 203
DreamFace 100 M83
FitMe cye 2013
30V
GT Image/ / Text
Ours
Facelift Con.mm
Arc2Avatar 06692222
DiffSplat 335
HumanNorm 2004
DreamCraft3D Ica 2004
"Beyonce"
Magic123
TECA
Relightify
Zero1-3
DreamFace
IC0V 2003
Figure 3: Qualitative Comparison with State-of-the-Art: We compare HairFree against recent
3D avatar techniques Lice [41]. Arc2Avatar (18), DiftSplat [38] HumanNorm [21]
Dream-
Craft3D (60), Magic 123 [50). TECA (69), TADA! (36,, Relightify! [46), Zero 123-XL (39], Dream-
Face 770, and FitMe 229 on "Mark Ruffalo" and "Beyonce" with explicit bald-head constraints.
HairFree delivers the most accurate 360 head shapes, realistic textures, and uniform lighting.
5 Experiments
We evaluate our proposed method in two critical aspects: (1) the compositional 2D human head prior and (2) the texture generation pipeline. Our 2D head prior offers precise, region-specific control over facial components, including skin, hair, and ears. The texture generation pipeline assessed based on its ability to produce photorealistic, high-quality human head textures, maintaining diversity in ethnicity, age, and style, ensuring accurate texture synthesis throughout the full 360 range.
5.1 2D Human Head Prior
Comparison with State-of-the-Art 2D Bald Proxy Methods: To qualitatively evaluate the human head prior, we compare it as bald proxy method with the following approaches: (i) diffusion- based generic inpainting (L.MM [54)), (ii) GAN-hased 2D bald proxy estimation (HairMapper (67]). and (iii) the ofth-of-theearr hair editing method HairCLIPv2 (66). The results highlight the ability of

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Easi Asian
Indigeness
South Asian
Elderiy
Evropodnn
Afrizan
Middll -hyed
East Asiom
Exropcot
Southh Asian
YoueViisll
Celeberites
Lizeo
Selena 6pies
Mery Streep
Lady Gpga
Varisal merkhes
A
B
C
D
M
N
Figure 4: Photorealistic Rendering Results. Without using any captioned text prompts during training, our method accurately follows text prompts test time. (A) Demographics Attributes: Rendered textures demonstrating variations in age and demographics across head avatars. (B) Celebrity Renderings: Multi-view renderings and textures of famous celebrities. (C) Varied Meshes and Nationalities: Showing additional variety in nationality based text prompts applied to different meshes. Corresponding text prompts, additional results and camera views are available in Appendix A
our approach to preserve more accurate head shapes and poses while effectively removing hair. The results are shown in Appendix A
Quantitative Evaluation ControlNet Strength (CS): We evaluate our model using FID, KID, LPIPS, and PSNR, across different ControlNet strengths (CS). By reducing CS, we can generate more diverse examples, enabling the synthesis of novel head views that do not exist in the training and Figure (C).
data, Table and
Ablation Inpainting (RGB): We compare the performance of our approach with and without inpainting. As shown in Figure (A), the model without inpainting is limited to generating accurate frontal views, while using gradual inpainting from front to back enables the synthesis of broader range of views, including extreme side, back, and top perspectives (not present the training data).
5.2 3D Textured Avatar Synthesis
Comparison with State-of-the-Art 3D Methods: We conduct a qualitative comparison against recent state-of-the-art 3D avatar generation methods. Zero Zero123-X3 123-XL [39] and Magic (50) rely on 2D diffusion-basse priors using Score Distillation Sampling 449 which often leads to oversaturation. low detail preservation, and inconsistent geometry across views. Tada! which is specialized for humans, also SDS based and struggles with oversaturation, while HumanNorm 21] introduces unnatural reddish skin tones. DreamCraft3D [60] and DiftSplat (38] leverage 3D Gaussian splatting, which improves efficiency but tends to produce low-frequee tow-frequency details and blurry textures. TECA (69) generates clothing in addition to the head. Facel.if .ift 41 produces relatively accurate head shapes but may hallucinate hair instead of adhering to the bald constraint, and like many other methods, suffers from extreme siew-specific illumination artifacts (c.g., shadows). Arc2Avatar [18), Relightify [46),

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Krusty the Clown
Sailor Waon
Wonder Womon
Spiderman
Jason Vearhees
Franiceas hein's Monster
Rusted motal
Jadoite
Bloodstone
Crocodic
Figure 5: Generalization Properties. Our method enables blending between photorealistic and stylized renderings through the classifier-free guidance (CSG) and control strength (CS) parameters. Despite being fine-tuned only realistic human faces, it generalizes strongly to diverse, non-uniform bald textures, enabling appearances beyond natural human skin. CSG: Impacts the alignment with the prior, allowing for greater style diversity
text. CS: Lower values relax the model's adherence (see row 3). Additional results are available Appendix
Method
Runtime
Stages
Quality
FitMe
minutes
Mid
CS
PSNR 1
MSF
LPIPS 4
FID 1
KID
DreamFace
minutes
Mid
Zerol-3
minutes
Low
1.0
18.38
0.018
0.24
6.6
0.0027
Relightify
minutes
0.75
17.14
0.023
0.29
11.5
0.0056
Tada!
hour
Low
0.5
15.05
0.035
0.39
27.9
0.0167
0.25
12.53
0.060
0.53
78.9
0.0518
TECA
minutes
Low
0
10.27
0.097
0.7268
384
0,4706
Magic123
hour
Low
DreamCraft3D
hours
High
Table 2: Quantitative Analysis/Ablation: We vary ControlNet Strength (CS) from to and
HumanNorm
hour
Mid
DiffSplat
seconds
Mid
Arc2Avatar hours
measure image similarity over 10K samples. At
FaceLift
seconds
High
CS = 1.0, outputs closely match the input; as CS decreases, diversity increases, enabling the synthe- sis bald, back-view, and extreme side-views.
HairFree (Our)
minutes
High
Table 1: Quantitative Comparison with State-of-the-Art: Runtime, stages, quality.
all struggle to generate a complete, bald scalp. Arc2Avatar produces full 360 avatar but suffers from entanglement issues, with visible clothing and non-bald scalp showing a hairline instead of clear skin. Relightify and FitMe both reconstruct texture and geometry together rather than focusing texturing, limiting their ability to produce consistent high-quality results. Neither method effectively addresses hair removal, and their outputs are of medium quality. DreamFace is further limited to generating only the face region without the scalp, and has limited skin diversity. Most of the baselines fail to enforce baldness, introducing unwanted hair despite explicit constraints. Additional quantitative analysis with respect runtime, method complexity, and image quality is given Table I Our approach achieves the highest fidelity 360 textures, setting a new benchmark for textured 3D avatar quality (Figure 3)
DreamFace 70). and FitMe 29
Qualitative Evaluation: We demonstrate the diversity and realism of our textures across various text prompts, including photorealistic, celebrity, and stylized outputs (Figures | and [5). Additional results covering fantasy, artistic styles, animal faces, and material synthesis are in Appendix A
(B), we demonstrate how varying CSG affects the alignment of the output with text. Higher CSG values lead outputs more aligned with the text prompt, while lower values relax this alignment and result in photorealistic appearance.
Ablation Classifier-Free Guidance (CSG): In Figure 6

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Fiont
Back View
View
Top View
Captale Pieriis
Horley Qkimn
The triie (Kall (90)
Irpus Cantrol
Ground
Our c2-10 ciecs
Control
High
Wil hi
LOW
0.ah
cleadher--ing
Figure 6: Ablation Studies: (A) Without our progressive inpainting, only frontal views are accurate.
(B) Higher CSG values improve adherence to text prompts; lower values produce more natural look.
(C) Reducing ControlNet strength gradually weakens alignment with the input conditioning signal.
User Study: We conducted a user study for Person 1 (87 users) and Person 2 (94 users), asking to select the top 1 method in terms of avatar plausibility. Each user was shown one frontal, two side, and one back view of avatars generated by 13 different methods (Figure Appendix For Person 1, HairFree led with 40.23% of participants, followed by Dream- Craft3D at 18.39, FaceLiff and Arc2Avatar tied at 8.05% each, and DiffSplat with 6.90%. For Person 2, HairFree led with 39.36%, fol- lowed by DreamCrafi3D at 14.89% FaceLifi 9.57%, and Arc2Avatar and DiffSplal tied at 7,45% each. These results confirm the su- periority of our method (Figure
Person Wark Raffasl
Perzon Becouss
Figure User Study: Distribution of prefer- ences among the selected methods for two individuals,
highlighting the preference for our method.
Comparison with Inpainting Methods:
Top-View (Renderings):
Figure 8 compares three approaches for in- painting in the UV space: (I) a naive method where all non-frontal views are filled with a uniform color, resulting in visible seams, (2) Content-Aware Fill (CAF)-based inpaint- ing in UV space, which also produces seams, particularly the back, and (3) our method, which performs inpainting directly in image ap-
X
space. Unlike the other methods, proach seamlessly preserves intricate patterns without any visible seams in the UV space, even for intricate non-human skin textures.
Naïve Inpainting
Content-Aware Fill
Ours
Figure 8: Comparisons: Inpainting in UV Space.
Limitations & Societal Impact: Our method is sensitive to lighting inconsistencies addressable via intrinsic decomposition (e.g., IntrinsicAnything [8] for relightable textures. Text prompts inherit any ethnicity appearance biases from the pre-trained diffusion prior. High-fidelity textures could enable identity spoofing or deepfakes, so responsible, careful use is erucial.
6 Conclusion
We introduced HairFree, a diffusion baased framework that generates realistic, 3D- cD-cossist bald head textures by conditioning large latent diffusion model on face parsing maps, 3D meshes, and background cues. During inference, guided inpainting fills unseen regions as the camera moves, yielding seamless 360" textures. Our compositional 2D prior cleanly separates skin from hair, enabling flexible 3D layering of external assets-sus as strand-based Our evaluations demonstrate that HairFree delivers state-of-the-a the-rartt fidelity and compositionality compared to existing 3D head avatar methods.
of hairline.

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The authors thank Tsvetelina Alexiadis, Tomasz Niewiadomski, and Taylor Obersat for perceptual study; Yao Feng, Tingting Liao, Dimitrios Gerogiannis, Weijie Lyu, Alexandros Lattas, and Foivos Paraperas Papantoniou for help with the baselines; Peter Kulits, Yuliang Xiu, and all reviewers for their valuable feedback; and Benjamin Pellkofer for support. Justus Thies is supported by the DFG Excellence Strategy- EXC-3057 and the project is co- co-funded by the European Union (ERC, Lemo, 101 162081). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.
Acknowledgements::
10

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A
Appendix
rendered mash, hair mask
accesssorie (except from earrings)
background
Figure 9: Outputs from three models trained with progressively richer conditioning inputs: (left) rendered mesh with hair mask only; (middle) accessories (excluding earrings); (right) background. All examples from an early training epoch and highlight the effect of each conditioning signal. The model gradually learns to associate additional inputs with their corresponding visual semantics. Note that earrings are not included in the conditioning, which causes them appear or disappear inconsistently across examples. The final model, trained for longer, achieves more stable results and can fully remove hair during inference.
A.1 Ablation Study on Semantic Conditioning Signals
The face and skin mask covers all facial regions including eyes, nose, lips, etc, teaching the model what skin and facial features look like (see "Devised Input Conditioning" in Figure 2). The hair mask is used during training only, helping the model learn hair locations it can remove hair at inference (e.g., for bald heads). Ear masks are used during both training and inference; at inference, precise 3D ear masks from FLAME for accurate, mesh- aligned synthesis. Accessory masks allow the model to learn to recognize and remove the accessories by omitting the mask at test time. Note that during training time those semantic masks can not be derived from the 3D mesh. See Figure and Figure I0 for an ablation study on semantic conditioning signals.
Figure 10: Examples showing inconsis- tent ear synthesis when ear masks are omitted. Each column shows a bald head generated from the same text prompt (e.g.. "old Asian woman") with zoomed- ear crops. Without explicit ear guid- ance, the model produces visible varia- tions in ear shape, size, and placement across samples. This instability indicates that accurate, mesh-aligned ear-mask su- pervision crucial for maintaining struc- tural consistency during generation.
The Janus effect arises because the Con- trolNet is trained mostly on frontal and side views. When directly conditioned on back-view mesh and semantic mask, especially with the default ControlNet strength of 1.0, it tends hallucinate face, having never seen such inputs during training (see Figure (A). Figure [T.. Pro- gressive inpainting resolves this by gradually completing the texture from front to back, so later views only need to fill in small missing regions. Lowering ControlNet strength during these steps prevents over- conditioning on unfamiliar views. Additionally, facial attributes (eyes, lips, nose, etc.) into one region. We avoid using separate masks for each part, many of these features small and prone to inaccuracies in the off- shel face parsing model. See "Devised Input Conditioning" in Figure for an example of this mask.
Janus effect:
use single face and skin mask that combines all
A.2 Controlnet Strength Influence on Distribution Shift
The ControlNet strength has major influence on shifting from one distribution to another, see Figure 12 Unfortunately, we don't have a large dataset of bald heads, where we could directly evaluate the generation of the backside of the head. However, conducted experiment on changing the distribution from human faces to cat faces (which an extreme case of a distribution shift), where could analyse the effects of the ControlNet Strength. Specifically, we applied our
15

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Control
V4
12Wens
x
x
Viwa
X
Figure 11: Ablation on the number of conditioning views: We evaluate the effect of reducing the number of input views used for texture generation. Row shows the 14 control views. Row 2 shows our full setting with 14 views. Rows and show results with 12 and 11 views, respectively. Red highlights indicate artifacts that emerge view coverage decreases.
0-00
R20
050
0
110
FID
KID
1.0
231.32
0.2074
0.75
228.46
0.2035
0.5
212.10
0.1910
0.25
39.88
0.0237
81.93
0.0347
Table 3: Quantiative Control- net strength influence (con- trolled with 3) on distribution shift.
Figure 12: Visualization of the distribution shift between the cat and the human face distribution by controlling the ControlNet strength.
method to the AFHQ-Cat dataset from StarGAN v2 [9] a commonly used dataset from significantly different domain. We measured FID and KID scores at various values, see Table For higher values of ControlNet strength (e.g., 3 >= 0.5), the model remains biased toward synthesizing human faces. This prevents effective generation of samples from the cat distribution, resulting high FID/KID scores and poor visual aligmment with the AFHQ domain. At 3 0.25, the ControlNet guidance reduced enough to allow the diffusion prior to generate realistic cat faces, while still retaining enough conditioning to preserve global head structure. This balance aligns well with the AFHQ-Cat distribution, as reflected the significantly improved scores. At 3 = O, although the model continues to produce cat faces, the samples often zoomed-in facial crops rather than full cat heads, deviating from the AFHQ distribution. In contrast, at = 0.25, structural guidance helps preserve head framing consistent with the dataset.
A.3 Fixed & Varied Meshes with & without Hair
Figures 13 and 14 illustrate texture maps rendered on head models from three different views, highlighting the consistency of our method in maintaining photorealistic details. The examples span a diverse range ethnicities and age groups, emphasizing the versatility of our approach in capturing the nuanced characteristics of human faces.
To explore the expressive capabilities of our method, Figures L5 and 16 contain examples generated from text prompts with "fantasy" elements. The rendered textures demonstrate the ability of our method to produce imaginative and stylistic results, maintaining consistency and coherence across different views.
To analyze the ability our method to replicate artistic styles, Figures 17 and 18 present generations inspired by famous paintings and painting techniques. These results highlight how our approach captures distinet brushwork, color schemes, and compositional elements, preserving the essence of each referenced style while maintaining structural coherence.
16

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Elderly Wonggolune fomale, lightt olive sich
pilaa spgee che soco wede puermn irn siáin
Youing Sonan Fore d663 éare broo sliw
Iidelee Paliieaaa spge fer snale olve Aprovnt sizin
precee Formnn seedee howoo skdn Bdeeri poo
Elder'y Iesit maloo pulc bel Sei
sayh golde sian
weddi
Hankattt Tives raad
Figure 13: Photo-Realistic Results (I): Rendered texture maps of the head are shown from three different views. The textures are displayed across a range of ethnicities and ages. illustrating the versatility and effectiveness of our method in handling diverse facial features with high fidelity. These results correspond to the Figure: "Photorealistic Rendering Results," in the main paper, further demonstrating the robustness of our approach in capturing fine- one-grained details.
To further evaluate versatility, Figure 19
contains synthesized animal faces, illustrating the method's capability to generate lifelike textures and anatomical consistency. The results reflect detailed fur patterns, expressive facial structures, and species-specific characteristics, demonstrating both realism and artistic stylization.
22 23 and 24jinclude generations resembling various materials and gemstones, emphasizing the model's sss ability to synthesize diverse surface qualities. From the translucency of crystals to the roughness of natural stone, the results capture essential visual properties such light refraction, texture variation, and intricate reflections, reinforcing the adaptability of our technique across different material types.
Finally, Figures 20 21
A.4 Comparison with 2D Bald Proxy Baselines
In Figures 25 and 26 we compare our approach several 2D baseline methods, including L.DM Inpaint [54], HairMapper [67], and HairCLIPy2 (66). Our results shown alongside bald proxy and hairstyle editing methods, demonstrating superior preservation of head shapes and poses while addressing limitations such as quality degradation and inconsistent outputs. This highlights the robustness of our 2D diffusion prior.
A.5 Texture Maps Results
Finally, Figure 27 provides additional examples of celebrity textures generated from text prompts using fixed meshes. These results include a variety of skin tones, facial features, and expressions, showing the versatility of our method in creating high-quality, photorealistic faces. The generated textures capture the unique facial features that distinguish each celebrity, such as specific bone structures, eye shapes, and other signature traits. By handling wide range of characteristics, these examples highlight how our approach maintains realism and consistency across different celebrity textures. This demonstrates the robustness of our method in accurately capturing detailed facial features and natural variations.
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Young B proesite fomad soldew-ere slónn
ww..-noo.... -netaaee Seeeeeae maiee Iteyrt ses-ge ski WuiaBlee dalll-----
Elderly Crisezi Feom even wooeeoooeee sidnn
Aol Javeea weol mnddiinn barawn skön Wisde - opg
Moceero sartitia mallo Bark-tton troood skin
Mathrr EiVilopian malee, rich daric skzin
med rend fair gla
Ellchi Seovedce
Figure 14: Photo-Rcaliste Results (II:: Rendered texture maps of the head are shown from three different views. The textures cover a range of ethnicities, ages, and facial features, demonstrating the versatility and effectiveness of our method in capturing diverse characteristics. These results correspond the figure "Photorealistic Rendering Results" in the main paper, illustrating the ability of our approach to produce high-quality, realistic faces with consistent detail across views.
A: Elderly Mongolian female, light olive skin B: Middle-aged Chilean male, warm tan skin
H: Young Brazilian female, golden-brown skin Elderly Swedish male, rosy fair skin J: Middle-aged Japanese male, light beige skin
C: Young Somali female, deep dark brown skin
D: Elderly Inii male, pale beige skin
K: Flderly Chinese female, even-toned skin
E: Middle-aged Turkish male, olive-brown skin
L: Middle-aged Indian male, medium- broot skin
Youthful That male, light golden skin
M: Youthful Haitian male, dark-toned skin
G: Elderly Moroccan female, sandy-toned skin
N: Mature Ethiopian male, rich dark skin
Table 4: Skin tones and demographic descriptions for the results shown in the main paper ("Photore-
alistic Rendering Results").
with at sonfietiiie inciecett since and Fine sceles along surface
Tale laverder sleln with A velveryy reschrre Failt glowing fredeles
Simoorth mne aallll - Jein wirhi goldon and Falnn: Silpoing velles
Se biine wertth alletic miballllkke marldssgs
Soft irddesi podeessonn scin Spifefii beitwenn pink and blkee hnnes
Soooaiyy Siiiie Falirte soniir
wertta aagleyy /eeeael
Figure 15: Abstract (I): Rendered textures generated from text prompts with "fantasy" elements, shown from three different views. These textures show the consistency and variety of our method in generating imaginative and stylistic facial features.
18

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stery saarttain and
cyom ligher
a rastrre rescealling apodi seean vurii Rueth Aacoeere aardeereett saln
softe rel Saderrau
sarelieee Mile reffaaiive sarffeee
chit Doowing
Saucee parrpese of aarrtanug
and pertetaunn prapeiss welhi Sabn
Marbic- Mos--caaan costeu od sión with rrades Woar 0ohii
Finda a warle
Seinn wirhh syeriing
iilo--oiii slin
Medtarn praaze
Galldan liles
Figure 16: Abstract (II): Rendered textures generated from text prompts with "fantasy" elements, shown from three different views. These textures show the consistency and variety of our method in generating imaginative and stylistic facial features.
19

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A face emnrrgig from the bold, swirting strokes and brightt hness of Vau Gogh's Sunflowors, creating a lively textured exprossion.
A face painted with thick, textured brushstrokes and vivid colors, evoking the energy and emotion of Jackson Tolleck's abstract expressionism.
face with vibrant, non-natuualisticc colors and exaggerated features, inspired by the expressive intersity of Edvard Mondh's "The Scream.
Fauvist face with bold, won-neturralstic colors and expressive brushwork.
A Noodlassical Face with idealited proportions and smooth, classical forms.
Figure 17: Painting(s) Styles (I): Rendered textures generated from text prompts with "fantasy" elements, shown from four different views. These textures show the consistency and variety of our method in generating imaginative and stylistic facial features.
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A Pop Art face with bold outtlness flat colors, and halftone patterus.
A serene woman with subtte smile and soft gaze, resembbling the famous Mona Lisa portrait.
scrone woman's 'ss face the swirling, vibrant colors and expressive brushstrokes of Van Goghtss 'Stary Night!
woman's 's face rendered intricate patterns and golden hues, inspired by Gustav Kliwtt's "The Kiss,' with flowing, oreate dotails.
A Youing Woman with a turbaa and pearl carring, capturing the essonce of 'The Girl with Pearl Earring.'
Figure 18: Painting(s) Styles (II): Rendered textures generated from text prompts with "fantasy" elements, shown from four different views. These textures show the consistency and variety of our method in generating imaginative and stylistic facial features.
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A crocodile with rough green skin, narrow yellow eyes, and a wide smout.
jawlnn A gorilla with coarse black fur, deep-set eyes, and a strong jawline.
A lion with golden mane, yellow eves, and strong jAW.
A tiger with orange for and Wlcc stripes, amber pyes, and a wide jaw.
An owl with mottled brown and white feathers, round eyes, and a sharp beok.
Figure 19: Animals: Rendered textures generated from text prompts with "animal" elements, shown from four different views.
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A face carved from malachite, deep green with swirling parteers, exuding trawsformation and vitality.
carved from twrawolse, with vierant blue-green tones, exading tranquility and protection.
face composed moonstone, with soft, laminons white hncs. embodying intuition and
A face composed of opel, shifting irieeseet colors, emboodying mystery.
A face composed of tameanite, rich blue violet hues, radiating creativity and vision.
Figure 20: Gemstones/Materials (I): Rendered textures generated from text prompts with "gem- stone/material" elements, shown from four different views. These textures show the consistency and variety of our method in generating imaginative and stylistic facial features.
23

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A fate crafted from rusted motall with corraded, weathored foatures and a deop, earthy patina, symbolizing histery and resilience.
A face crafted from sand, with soft, textured features that shift and change, evoking the transient, ever-changing nature of the desert.
A face crafted from spinol, deep red with a brilliant shine, sumbolizing strength th and resillence.
face formed from jadeite, deep green and polished, symbolizing prospority and protection.
A face formed from Labradorite, with shimmering blue and green hoess evoking mystery and transformation.
Figure 21: Gemstones/Materials (II): Rendered textures generated from text prompts with "gem- stone/material" elements, shown from four different views. These textures show the consistency and variety of our method in generating imaginative and stylistic facial features.
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A face Formed from lapis laeuli, deep blue with goldon flocks, exuding wisdom and truth.
face formed from ruby, with fiery red tones and glowing features, symbolizing passion.
A face wide alexandrite, changing, hues from green red. embodying versatility and magic.
face made of ametinust, with deep purple haes and soothing slow, exading calm.
face made garnet, rich red and glowing, radiating warmih and Vitality.
Figure 22: Gemstones/Material (III): Rendered textures generated from text prompts with "gem- stone/material" elements, shown from four different views. These textures show the consistency and variety of our method in generating imaginative and stylistic facial features.
25

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A face made of jade, soft green sereve, symbolieing harmony.
face made of peridot, light green and sparkding radiating optimism and renewal.
A fate mode of polished marble, with smooth, sculpted features and pristine, timeless beanty, symboliginn elogance endurance.
A face made of stone, with strong, features and weathered, timoless quality, exading durability and eudurance.
A face made of tourmalise, with mallti-cloree tones shiftinng across the face, symbolizing balance and harwory.
Figure 23: Gemstones/Materiak. (IV): Rendered textures generated from text prompts with "gem- stone/material" elements, shown from four different views. These textures show the consistency and variety of our method in generating imaginative and stylistic facial features.
26

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face made of tarquoise, vibrant blue-green and smooth, exuding screuity and protection.
A face sculpted from bloodstone, dark green with ined spooks, symbolleing courage and vitality.
A face sculpted from fluorite, with trauslucent purples and greens, exuding clarity and healing.
shazed From chrysoprase, light green with Sihoo textwre, radiating peace and renewal.
face shaped from enyx deep black with sleck contours, symbolizing strength.
Figure 24: Gemstones/Material. (V): Rendered textures generated from text prompts with "gem- stone/material" elements, shown from four different views. These textures show the consistency and variety of our method in generating imaginative and stylistic facial features.
27

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eg
og
Original Img
Ours
LDM Inpaint
HairMapper
HairCLIPv2
Figure 25: Comparisons with State-of-the-AAt 2D Bald Proxy Methods (I): Our approach (with
CS 0.45) shown alongside bald proxy and hairstyle editing methods. L.DM Inpaint (54 uses
the SD 2.1 inpainting model with full background mask, similar to ours. HairMapper 67 and
HairCLIPy2 (66] (prompt: "bald") are bald proxy and hairstyle editing methods, though both degrade
image quality; HairCLIPv2 generates hair due to limited bald data in training. Our method addresses this limitation, while preserving head shapes and poses more accurately.
28

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Eribune
Original Img
Ours
LDM Inpaint
HairMapper
HairCLIPv2
Figure 26: Comparisons with State-of-the-Art 2D Bald Proxy Methods (II): Our approach (with
CS 0.45) shown alongside bald proxy and hairstyle editing methods. L.DM Inpaint (54 uses
the SD 2.1 inpainting model with full background mask, similar to ours. HairMapper 67 and
HairCLIPy2 (66] (prompt: "bald") are bald proxy and hairstyle editing methods, though both degrade
image quality; HairCLIPv2 generates hair due to limited bald data in training. Our method addresses this limitation, while preserving head shapes and poses more accurately.
29

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Figure 27: Additional Texture Maps Generated Results: Examples of textures generated from text prompts of random celebrity names. The results include diverse range of skin tones and facial features, highlighting the effectiveness of our method in generating a large variety of faces.
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A.6 User Study
We ran perceptual evaluation on Amazon Mechanical Turk assess avatar plausibility, Each HIT presented a worker with four rendered views (frontal, two sides, and back) of avatars for two different identities, generated by 13 methods. Participants were asked, "Which head avatar looks most plausible?" and were paid $1 USD per completed subject. A screenshot of the study interface is shown in Figure 28
Which head avatar looks most plausible?
In this task, you will see reference image of celebrity and their name.
You will also see head avatars created by 13 diferent methods based on the reference image and text.
Each avatar shown from 4 different viewpoints.
Your task choose which method produced most plausible head avatar considering all different viewpoints.
You
the image by clicking
Once you have selected method for images, the 'SUBMIT' button will activated and you submit the HIT.
Image Text
Method 1
Method
Method
Method
Methed
Method
Which head avatar looks most plausible?
Method
Method
Method
Method
Method
2
4
5
Method
Method
Method
Method
Method
Method
Method
12
Next Image
SUBMIT
images left
Figure 28: Layout of our user study.
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38


Reviews:
[{"review_id": "9g86bjSdnb", "reviewer": "Reviewer_h4Gf", "summary": "This paper introduces a method for text-driven bald head texture synthesis based on 3D Morphable Models (3DMM). The approach proceeds in several steps:\n\n1. Fine-tune a Latent Diffusion Model (LDM) with ControlNet on FFHQ and CelebA-HQ datasets, conditioned on parsing maps, foreground masks, and 2D renderings of 3DMM.\n2. Train a replacement LDM conditioned on inpainting masks.\n3. Generate a frontal head image using LDM and ControlNet, conditioned on the parsing map rendered from 3DMM (only skin and ears), foreground mask, and image, then unwrap the image into an atlas.\n4. For each view, render a textured 3DMM and perform inpainting using the LDM, then unwrap the pixels into the final texture map.\n\nOverall, the paper provides a baseline for a valuable yet underexplored task. While there are minor mistakes or unclear parts in the paper, they do not detract from the significance and novelty of the proposed work.", "questions": "See the weakness above.", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n1. The method establishes a baseline for the task of text-driven bald head texture synthesis, which has practical applications but has not been extensively studied.\n2. The idea is clever. It leverages priors from FFHQ and CelebA-HQ datasets to fine-tune ControlNet conditioned on parsing maps, and uses 3DMM-rendered parsing maps with only skin and ears to guide novel face generation.\n3. The method shows good generalizability; it can generate non-human skin textures based on textual prompts.\n4. The pipeline is intuitive and reasonable. The experimental results are sufficient, including comparisons and diversified experiments that demonstrate the method's effectiveness and efficiency.\n\n### Weaknesses\n1. Miscellaneous Issues:\n    1. In Figure 2(A), why is the label for the last row \"RGB\"? Please clarify.\n    2. In Figure 2(B), does $\\beta$ denote ControlNet strength? Also, what does $\\epsilon$ represent?\n    3. In Figures 21 and 22, the legend labels appear to be flipped.\n    4. In Figure 6(B), the figure lacks a legend indicating that the first row corresponds to higher CS values, and the second to lower ones.\n2. In line 161: \"we render the mesh from 14 viewpoints by rotating a virtual camera around the head\" — Why was 14 chosen? An ablation study on the number of viewpoints would strengthen the justification.\n3. 1-to-many issues:\n    1. In Equation (9), is it possible for two pixels to map to the same UV texel? If so, how is this ambiguity resolved?\n    2. In Equation (11), can a single texel receive values from four different adjacent grids? How is this handled?\n4. In line 204: What is \"ControlNet Strength (CS)\"? This term does not appear in the original ControlNet paper and should be clearly defined.\n5. In lines 204–205: \"By reducing CS, we can generate more diverse examples, enabling the synthesis of novel head views that do not exist in the training data (Table 2).\" However, Table 2 shows that all metrics degrade as CS decreases. Therefore, the claim about improved diversity lacks sufficient evidence.\n6. The content in Section 5.1 \"Quantitative Evaluation\" and Section 5.2 \"Ablation - ControlNet Strength (CS)\" is somewhat redundant. Moreover, the conclusion of Ablation - ControlNet Strength (CS) to be more relevant to Section 5.1.\n7. In Section 5.2 \"Comparison with Inpainting Methods\": What does Content-Aware Fill (CAF) refer to? Please provide more technical details or cite relevant literature.", "was_revised": true, "final_justification": "I appreciate the authors' efforts and detailed response. All my previous concerns are addressed. Although the metrics issues regarding the insufficient number of bald head data for a comprehensive evaluation remain unsolved, this paper proposed a novel solution for an interesting research problem. I think this minor concern can be solved in the revision with more visualization. In summary, I would like to increase my score and suggest an acceptance recommendation.", "rebuttal": "We are thankful for the positive and constructive feedback! In the following, we will clarify the questions raised under ‘weaknesses’ and we will incorporate the suggestions and answers into the revised paper.\n\n\n**(1) Miscellaneous issues:**\n\n- *In Figure 2(A), why is the label for the last row \"RGB\"? Please clarify.*\n\n   - R, G, B refers to the three usual RGB channels. We will rename it to Channel 1, Channel 2, and Channel 3 for clarity.\n\n- *In Figure 2(B), does $\\beta$ denote ControlNet strength? Also, what does $\\epsilon$ represent?*\n\n   - Yes, $\\beta$ is ControlNet strength. $\\epsilon$ is a small neural network through which the input condition downsamping is learned (Zhang et al. [68]).\n\n- *In Figures 21 and 22, the legend labels appear to be flipped.*\n\n  - Thank you for noticing this, we will put them back on top!\n\n - *In Figure 6(B), the figure lacks a legend indicating that the first row corresponds to higher CS values, and the second to lower ones.*\n\n   - Yes, we will add the legend for clarity, thank you!\n\n\n**(2) An ablation study on the number of viewpoints would strengthen the justification.**\n\n\nWe empirically found that reducing the number of side-views may lead to the Janus artifact at the back of the head (similar to Figure 6(A)). We will add examples of this when fewer views are used to the revised version.\n\n\n**(3) One-to-many issues:**\n\n\nWe use a splatting-based approach to set the texel values given a specific pixel and corresponding UV coordinate. Thus, two pixels could be mapped to the same UV texels. Here, a distance based blending scheme could be used, but we found that assigning one of the values without blending yields good results. \n\n\n**(4) What is \"ControlNet Strength (CS)\"?**\n\n\nControlNet is a copy of the original LDM U-Net that processes the control (conditioning) image separately. Its outputs are then added to the original U-Net’s outputs at corresponding blocks during the denoising process. This allows the model to integrate external structural guidance (like pose, edges, or depth) without modifying the base U-Net. The degree to which this control input influences the final image can be adjusted using the ControlNet strength parameter. We will describe this in more detail in the revised paper. Specifically, let $F_i$ be the output from the original LDM U-Net at block $i$, $C_i$ be the corresponding ControlNet feature map at the same stage, and $\\beta$ be the ControlNet strength parameter. Then, the combined feature at each block during the denoising process is computed as: $F_i^{\\text{new}} = F_i + \\beta \\cdot C_i$\n\n\n**(5) In lines 204–205: \"By reducing CS, we can generate more diverse examples, enabling the synthesis of novel head views that do not exist in the training data (Table 2).\" However, Table 2 shows that all metrics degrade as CS decreases. Therefore, the claim about improved diversity lacks sufficient evidence.**\n\n\nTable 2 shows that all metrics degrade as CS decreases, considering the data distribution that the model was fine-tuned on. To remember, this data contains mostly frontal and some side-views of photo-realistic human faces with hair. In our case, we want to generate samples that are different from the original data distribution in three aspects (1) no hair (bald examples), (2) various camera views including top and back-views), (3) non-photorealistic (including non-human) examples. Therefore, the goal is for the metrics to decrease as ControlNet strength decreases which is what is shown in the table (because data distribution that we want to generate differs from the one that is present in data). In other words, metrics degrading as CS reduces (there is a trend, lower CS leads to worse metrics’ scores) signifies that the distribution of generated data shifts more and more from the original distribution, which is what we want.\n\n\n**(6) The content in Section 5.1 \"Quantitative Evaluation\" and Section 5.2 \"Ablation - ControlNet Strength (CS)\" is somewhat redundant. Moreover, the conclusion of Ablation - ControlNet Strength (CS) to be more relevant to Section 5.1.**\n\n\nIn Section 5.1 we were referring to quantitative evaluation of the CS parameter, while in section 5.2 this was shown qualitatively. We will merge those two in Section 5.1 in the revised version.\n\n\n**(7) In Section 5.2 \"Comparison with Inpainting Methods\": What does Content-Aware Fill (CAF) refer to? Please provide more technical details or cite relevant literature.**\n\n\nCAF is an “industrial standard” for inpainting in professional tools like PhotoShop.\n\nThank you again for your time and constructive feedback. We hope our responses have clarified the key points, and we would be glad to provide further details if needed."}, {"review_id": "939UjcuBaY", "reviewer": "Reviewer_9xQ3", "summary": "This paper proposes a compositional 2D head prior for text-driven 360^{o} bald texture synthesis. This method mainly includ etwo modules: finetuning controlnet with face condition and generate head texture with progressive uv mapping. Comparison with competitors in this work have shown the superiority of proposed pipeline.", "questions": "Please see weakness, A table or figure showing performance differences when each conditioning signal is removed would help clarify whether each is necessary. This would strengthen claims of methodological contribution. Also,  Could the authors provide implementation details such as architecture configs, training time, and compute resources needed for reproduction?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. This paper addresses an interesting and challeging task, allowing seamless integration with various assets like\ncustomizable 3D hair, eyeglasses, jewelry, etc. \n2. The method introduces a 2d head prior by training diffusion conditioned on skin masks, hair masks , ears and accessory masks, a 3D head mesh rendered in 2D and background information.\n3. The authors conduct experiments with both qualitative and quantitative comparisons to show the superiority of proposed method\n\nWeakness:\n1.  The proposed method heavily rely on existing components to do straightforward intergration without algorithmic innovation. The authors should provide a clearer explanation of the necessity and unique role of each included condition and module.\n2.  It is unclear how well the method generalizes across gender, hairstyles, and ethnicities, large expressions and whether failure cases (e.g., severe occlusion, long hair with background mixing) are handled. How about the performance on integration with various assets like customizable 3D hair, eyeglasses, jewelry, etc.\n3. How well does the proposed method handle reconstruction of bald heads when given a reference image? Since the method is titled “HairFree” and emphasizes decoupling accessories and hair, it would be important to know whether the model faithfully reproduces clean scalp geometry and texture in the absence of hair.", "was_revised": true, "final_justification": "Thanks for the response. Most of my concerns have been addressed. My remaining concern is the necessity of incorporating various signals. I believe there is redundant information among the skin and face mask, as well as the rendered 3D head mesh. While I appreciate the systematic design for tackling the human head texturing task, the composition of existing techniques still lacks strong methodological novelty. The authors are encouraged to release the code to make the design choices in each part clearly.", "rebuttal": "Thank you for the constructive feedback which we are happy to incorporate in the revised paper. Please find detailed responses and clarifications in the following paragraphs.\n\n\n**(1.1) Explanation of the necessity and unique role of the conditioning signals:**\n\n\nThe overall goal of the proposed conditioning strategy is to expose the model to both desired and undesired features during training. This enables the preservation of desired elements and the removal of unwanted ones at inference time. We will add a detailed description of the conditionings to the paper and will show the effect of removing each condition signal in an ablation study (thank you for the suggestion).\n\n\n*- Skin and face mask:*\nDuring training, the diffusion model is conditioned to learn the appearance of skin and facial regions where specified. At inference time, high-precision 3D skin and face masks are obtained using the FLAME model.\n\n*- Hair mask:*\nThe model is trained to recognize and predict hair regions when provided. However, no hair mask is applied during inference. When combined with text prompts indicating a bald head, this approach enables the removal of hair and the generation of clean skin. Without this, hair would be baked into the skin texture.\n\n*- Ear mask:*\nEar masks are used both during training and inference. During training, masks are generated using an off-the-shelf face parsing model. At inference, accurate 3D ear masks are obtained from FLAME. This ensures consistency between generated and FLAME ears, improving texture fidelity.\n\n*- Accessory mask:*\nSimilar to hair masks, accessory masks (e.g., eyeglasses, earrings, clothing) are used during training to guide the model’s learning. These are omitted during inference, allowing for the removal of such accessories and the synthesis of unobstructed skin regions.\n\n*- Rendered 3D head mesh:*\nConditioning on a rendered 3D head mesh guides the model to generate images consistent with the target geometry and ensures accurate pixel-to-UV space mapping. Training meshes are predicted using an off-the-shelf model, while inference-time meshes can be explicitly defined for texturing. Appendix Figures 21 and 22 (the “LDM Inpaint” row) demonstrate the impact of removing the 3D head mesh conditioning, which leads to reduced geometric consistency and poorer UV mapping.\n\n*- RGB background:*\nWithout conditioning on the background, generated images often include undesired elements (e.g., hands, microphones, miscellaneous artifacts) or unnatural shadows. At inference time, setting the background to a uniform color helps prevent such artifacts.\n\n\n**(1.2) Explanation of the necessity and unique role of the different modules:**\n\n\nThe presented texture generation approach consists mainly of two modules:\n\n(i) a compositional, 3D-controllable prior which can be used for inpainting in image space, and\n\n(ii) a progressive texture merging/fusion module.\n\nBoth components are key contributions of our method to produce bald human textures that can be combined with other 3D assets. The progressive texture generation (ii) iteratively generates a complete texture, by leveraging the prior (i) using the conditionings explained above. The conditioning allows for a good alignment between the 3D model and the generated images which is important to project the colors to the texture space of the FLAME model accurately. The iterative inpainting in image space guarantees that the resulting texture will be seam-less (as the generated images will not have seams) - this is in contrast to methods that do inpainting in texture space. Additionally, Figure 8 shows why inpainting in image space using (i) is important, while Figure 6(A) shows why (ii) is important.\n\n\n**(2) Generalization of the method:**\n\n\nThe goal of our method is a text-based generation of bald human head textures for a given 3D FLAME mesh. Based on this mesh and the generated texture, additional assets can be added by the user. For generating the texture, we condition our approach on the given 3D FLAME mesh with a neutral expression.\nIn the paper, we show that our method is able to generate textures with a large variation of different attributes and that it also generalizes to the non-photorealistic setting. \nIn the photorealistic setting, Figure 4 presents generated results across a range of visual attributes, including gender, age, and ethnicity. Additional examples highlighting diversity in skin tone and nationality are provided in the Appendix (Figures 9 and 10). Figure 4 also includes several celebrity cases, while Appendix Figure 23 offers an extended collection of 360-degree texture maps for various celebrities.\nIn the non-photorealistic setting, representative results are shown in Figure 5, with further diverse examples included in the Appendix (Figures 11-20).\n\n\n**(3) Usage of the proposed method for reconstruction:**\n\n\nThe proposed method is a generative model that based on a text prompt generates a complete texture of a human head. However, we believe that some of our contributions can also benefit the reconstruction given a reference image. Specifically, our 2D diffusion prior is capable of removing hair from a reference image, enabling it to serve as a bald proxy as shown in Figures 21 and 22 in the Appendix. These generated images could be used as input to a face reconstruction method.\n\n\n**(4) Implementation and training details:**\n\n\nOur method is based on Stable Diffusion 2.1 LDM, which we fine-tune using ControlNet. At inference time, we use the corresponding inpainting prior from Stability AI, both of which are publicly available. The model is fine-tuned for 1500 GPU-hours on a single H100 GPU (see line 144). As mentioned in the submission, we will release the code, pre-trained model, and a dataset of generated samples.\n\n\nThank you again for your time and constructive feedback. We hope our responses have clarified the key points, and we would be glad to provide further details if needed."}, {"review_id": "0n6DZBPPj6", "reviewer": "Reviewer_gR5R", "summary": "This paper proposes a method to synthesize RGB head textures from text prompts. The proposed method first trains a ControlNet, conditioned on masks and a rendered face mesh. It then generates multiview images from a text prompt. It starts from the frontal image and sequentially infills the remaining texels from the other views. The paper conducts a visual comparison with a dozen related works and a user study on two samples. An ablation study measures the effect of the guidance scale.", "questions": "- What do Person 1 and Person 2 refer to in the user study? How were these examples chosen? Are these two samples representative of the output distribution?\n- How would the proposed texturing aggregation pipeline compare to classical texture fusion approaches (Ebert et al. 2002) or more recent methods like Haoran et al. (2023) and Zheng et al. (2023)?\n- Why does the proposed texture aggregation method handle seams better than related works? It would be valuable to provide more and higher resolution examples of the back of the head, where FLAME has a major seam. There are a few examples in Fig. 3 but they are very small. Providing higher resolution images and a discussion why this approach improves over existing methods would be appreciated.\n\n\nReferences: See weaknesses", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strengths\n\n- The paper compares with 12 recently published works.\n- The visual results look promising, in particular for the back of the head where FLAME has a major seam.\n- Quantitatively evaluating results for the given problem (texture generation from text prompts) is difficult, and I appreciate that the authors made an effort to conduct a user study.\n\n## Weaknesses\n- Limited ablation study. \n     - The only ablation provided measures the influence of the guidance scale. It concludes that the guidance scale trades visual fidelity with diversity---a fact already established in previous works (Ho and Salimans 2022). I would have liked to see an ablation study that explains why the proposed texture aggregation method handles seams better than existing techniques like (weighted) averaging or interpolations (Ebert et al. 2002).\n- Minor contributions\n    - The paper makes two contributions: a ControlNet-based 2D head prior and an iterative texturing approach. The first contribution is a straightforward application of ControlNet. The second contribution is an interactive texture-infilling, which is very closely related to existing literature (Ebert et al. 2002, Haoran et al 2023, Zheng et al. 2023). It would be great if the paper could describe the differences to the above texturing unprojection techniques and, ideally, include a comparison.\n- References\n    - Ho, Jonathan, and Tim Salimans. \"Classifier-free diffusion guidance.\" __arXiv preprint arXiv:2207.12598__ (2022).\n    - Ebert, David S., et al. __Texturing and modeling: a procedural approach__. Elsevier, 2002.\n    - Bai, Haoran, et al. \"Ffhq-uv: Normalized facial uv-texture dataset for 3d face reconstruction.\" __Proceedings of the IEEE/CVF conference on computer vision and pattern recognition__. 2023.\n    - Zheng, Wenzhuo, et al. \"Multi-view 3d face reconstruction based on flame.\" __CoRR__ (2023).", "was_revised": true, "final_justification": "The contributions are not very innovative, but the contributions are technically solid and carefully evaluated.", "rebuttal": "Thank you for your valuable feedback and highlighting the strengths of our work!\n\n\nIn the following, we give additional insights and clarify questions raised:\n\n\n**(1) Our contributions:**\n\n\nAs mentioned, our approach is able to generate diverse textures without any major seams. This is achieved through our proposed compositional 2D prior and a progressive texture merging module that ensures consistency across views. While both components build on prior ideas, they are combined and adapted in a novel way to enable fully automatic, 3D-consistent 360° head texturing for both human and non-human skin—without requiring reference images, multi-view data, or bald human head data.\n\n- The ControlNet-based 2D head prior is trained to generate semantically aligned RGB views, conditioned on 3D geometry and face parsing. As noted by Reviewer ZFQT, this component acts as a strong foundation model for face generation, supporting a wide range of skin types and enabling robust hair-skin separation for bald texture synthesis.\n\n- The progressive texture merging module incrementally completes the UV texture by re-rendering and infilling texels from new viewpoints, using previously generated texels as a reference.\n\nThese design choices are key to the robustness and generality of our method across identities, viewpoints, and texture styles. Moreover, quantitative and qualitative evaluations show that our method outperforms all recent state-of-the-art techniques in terms of texture quality, consistency, and realism. \n\n\n\n**(2) What do Person 1 and Person 2 refer to in the user study? How were these examples chosen? Are these two samples representative of the output distribution?**\n\n\nPerson 1 and Person 2 refer to the individuals shown in Figure 3. We will clarify this in the paper. These examples represent photorealistic results and were selected as a random male and female celebrity. For additional examples of this type, please refer to Figure 4. For a broader set of celebrity texture maps, see Appendix Figure 23.\n\n\n**(3) Why does the proposed texture aggregation handle seams better than existing techniques?**\n\n\n- The related methods mentioned are based on fixed inputs, i.e., multiple captured images that might have variations in shading or even facial expressions. In contrast, HairFree is generating its views with a technique that ensures consistency in image space which results in consistent and seam-less uv textures. Specifically, our proposed inpainting technique ensures to use the already existing colors from the texture and generates new color values for regions that are becoming visible when rotating the 3D head in image space. In the paper, we are visualizing the views that are iteratively generated with a major order of front, sides, back. This order also contributes to the consistent merging of the left and the right sides.\n\n\n- Note that we have conducted qualitative ablation studies on inpainting in UV space (inpainting in UV space leads to the seams/discontinuities at the back/top of the head, see Figure 8) and inpainting in RGB space (without our progressive re-rendering approach, it is not possible to generate 3D head-consistent extreme side views and back view; notably, Janus effect will appear at the back, see Figure 6(A)).\n\n\n- We will add additional zoom-ins to the figures that show the head from the back. Note that larger examples, additional texture maps, and more extreme viewpoints are provided in the Appendix. We recommend zooming in on the back view samples for a clearer inspection.\n\n\n**(4) Influence of guidance scale:**\n\n\nHo and Salimans do not measure the influence of guidance scale when the large diffusion prior is fine-tuned on a smaller dataset. In our case, we fine-tune our prior on photorealistic frontal views of faces only, and at inference time, we show that we can generate back-views, bald samples, and non-photorealistic examples that are out of the fine-tuning distribution, emphasizing strong generalization properties of our approach.\n\nThank you again for your time and constructive feedback. We hope our responses have clarified the key points, and we would be glad to provide further details if needed."}, {"review_id": "WuxV2kjlGX", "reviewer": "Reviewer_ZFQT", "summary": "This paper proposes a novel method for generating bald texture from text conditions. To this end, it trains a compositional 2D head prior that can generate face images given a background image, a set of face parsing masks, and the FLAME mesh as the condition. They further develop a pipeline to create the texture using this 2D prior.", "questions": "* About the inpainting network:\n  * From the current version, it is very hard for me to figure out the implementation and usage of the inpainting network.\n  * In L140, the paper says \"we replace the locked LDM with an inpainting variant, allowing for guided completion of missing texture regions\" and gives a loss function in Eq.(8). Does this inpainting need training? What is the training data?\n  * The word \"inpainting network\" does not even appear in Sec.4.2. What's the role of the inpainting network in the 3D texturing pipeline?\n  * In L207, the paper mentions a baseline called \"the model without inpainting\". What is it?\n\n* It seems that there is a gap between the training and texturing phase of the 2D prior. In the training phase, there is always a hair mask, but in the texturing phase, the hair mask is set to zero. What about the effects of this gap? \n\n* The training dataset, i.e. FFHQ and CelebA-HQ, does not contain the top view of bald people. How does the method synthesize the top view of the bald head? In addition, how does the method synthesize the texture of the back head?\n\nAs there is so much missing information in the current version, I lean towards rejecting this paper.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths:\n* The comparison experiments are solid. Many relevant works are involved for comparison. The proposed method indeed obtains the best results.\n* The proposed compositional 2D head prior will serve as a foundation model in the field of face generation, if the model weights can be released.\n\n### Weaknesses\n* The paper writing does not meet the bar for publication. See the questions in the section below.\n* The dataset is one of the main contributions listed in the introduction. However, there are no descriptions for this dataset in the main paper! Thus, it is hard for me to evaluate the significance of this dataset. There are also many datasets in the community, including the FFHQ-UV and the FFHQ-UV-Intrinsics dataset. If the paper claims its dataset is a contribution, it should be compared to existing datasets explicitly and clarify what the benefit of their dataset is over the existing ones.", "was_revised": true, "final_justification": "All my concerns are addressed in the rebuttal. I'm now leaning positively toward this paper. I do not give a higher score as I think the technical contribution is somewhat limited.", "rebuttal": "Thank you for providing constructive feedback on how to improve the presentation and writing of the paper.\n\n\nWe are grateful to hear that the compositional 2D head prior will be useful for the community (yes, we will release the model weights, see line 19) and that the experiments are solid, showing that the proposed method obtains the best results.\n\n\nIn the following, we will provide the additional requested details which we will also add to the revised paper.\n\n\n**(1) Implementation / Usage of the inpainting network:**\n\n\n- Does this inpainting need training? What is the training data?\n\n  - The inpainting LDM prior is functionally equivalent to the locked (frozen) LDM prior, with the key distinction that the inpainting model was additionally conditioned on masks during training. In our setup, we do not retrain the inpainting LDM; rather, we use a pre-trained version and replace the original LDM prior with it. At inference time, we attach ControlNet to the inpainting LDM instead of the original LDM, allowing us to perform inpainting using both mask-based conditioning and ControlNet guidance. For further details on the training of prior LDMs, we refer the reader to Rombach et al. [51] (Stable Diffusion).\n\n\n- What's the role of the inpainting network in the 3D texturing pipeline?\n\n  - At inference time, we use the inpainting LDM prior in combination with our ControlNet to generate head images conditioned on view-dependent 3D head mesh and skin/ear appearance cues. As the camera gradually rotates around the head, previously unseen head regions become visible and are inpainted by the model. The visible regions that have already been synthesized are stored in UV space and later re-rendered and reused for inpainting as the camera continues to move, ensuring spatial and temporal consistency across views. (See Figure 2 (system figure) and its description, and Sec. 4.2).\n\n\n- What is the baseline called \"the model without inpainting\"?\n\n\n  - The \"model without inpainting\" generates all head views at the same time, without accumulating previously seen regions in UV space or inpainting newly visible areas. In contrast, our progressive texturing approach is iterative: views are generated one after another and depend on previously generated content. As the camera rotates, visible regions are stored in UV space, and newly revealed areas are inpainted based on both the current view and prior results. This approach better handles extreme angles outside the fine-tuning distribution.\n\n\n**(2) It seems that there is a gap between the training and texturing phase of the 2D prior. In the training phase, there is always a hair mask, but in the texturing phase, the hair mask is set to zero. What about the effects of this gap?**\n\n\nYes, the mask distribution in the training and texturing phase is different. During training, ControlNet learns hair appearance conditioned on the hair mask. At test time, by setting the hair mask to zero, reducing the ControlNet strength to rely more on the inpainting prior, and specifying via text that the head should be bald, the model efficiently removes hair. Combined with our progressive texturing approach, this enables plausible bald head generation mitigating artifacts coming from the training/inference conditioning gap.\n\n\n**(3) The training dataset, i.e. FFHQ and CelebA-HQ, does not contain the top view of bald people. How does the method synthesize the top view of the bald head? In addition, how does the method synthesize the texture of the back head?**\n\n\nSince the training data lacks top and back views of bald heads, we reduce the ControlNet strength to enable the generation of these views. This allows the model to rely more on the inpainting LDM prior, which was trained on larger, more generic datasets, making the fine-tuned dataset less critical. Combined with our progressive texturing approach and a text prompt specifying a bald head, this helps produce plausible textures for extreme angles (See lines 237-238 and Figure 6(C) for Controlnet strength; lines 140-143 for the inpainting LDM; lines 168-189 and Figures 6(A) and Figure 8 for progressive texturing and related ablations).\n\n\n**(4) Dataset contribution:**\n\n\nWe would like to clarify that the dataset itself is not the primary contribution of our work. Rather, our key contribution lies in the methodology that enables the generation of high-quality, 360-degree bald head textures. This method can serve as a powerful tool for data generation in downstream tasks. Photorealistic examples from the resulting dataset are presented throughout the paper and in the Appendix. Importantly, our dataset is generated entirely automatically using our method and can be scaled or reproduced using only text prompts and 3D head meshes. This makes it fundamentally different from datasets such as FFHQ-UV, which rely on GAN-based models, produce lower-quality textures, and include baked-in hair in the generated outputs. Additionally, FFHQ-UV requires a reference image and does not allow control over the underlying head geometry.\n\n\nThank you again for your time and constructive feedback. We hope our responses have clarified the key points, and we would be glad to provide further details if needed."}]

Author rebuttal:

Meta-review: The paper has initially received thorough reviews. The reviewers also discussed the rebuttal in depth.

The rebuttal has helped address a lot of concerns, and the reviewers agreed to positively score the paper.

In the current form, we recommend Accept. Congratulations!
We encourage the authors to take into account the reviewers' comments for the final version (see below).

* Although the metrics issues regarding the insufficient number of bald head data for a comprehensive evaluation remain unsolved, this paper proposed a novel solution for an interesting research problem. A Reviewer thinks this minor concern can be solved in the revision with more visualization.

* One Reviewer remaining concern is the necessity of incorporating various signals. They believe there is redundant information among the skin and face mask, as well as the rendered 3D head mesh. While they appreciate the systematic design for tackling the human head texturing task, the composition of existing techniques still lacks strong methodological novelty. The authors are encouraged to release the code to make the design choices in each part clearly.

* Two Reviewers find that technical contribution is somewhat limited. We encourage the Authors to further clarify their contributions.
Decision: Accept (poster)