Title: HairFree: Compositional 2D Head Prior for Text-Driven 360° Bald Texture Synthesis
Venue: NeurIPS 2025
Accepted: yes
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
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/


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.\n\nQUESTIONS:\n* 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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).\n\nQUESTIONS:\n- 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\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-17"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nPlease 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.\n\nQUESTIONS:\nSee the weakness above.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-23"}