# Spherical Geometry Diffusion: Generating High-quality 3D Face Geometry via Sphere-anchored Representations

arXiv: https://arxiv.org/abs/2601.13371 · HTML: https://mlsanity.com/paper/2601.13371/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 51 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2012.00926/) (2020)
- [Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion](https://mlsanity.com/paper/2212.06135/) (2022)
- [Generating 3D faces using Convolutional Mesh Autoencoders](https://mlsanity.com/paper/1807.10267/) (2018)
- [FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction](https://mlsanity.com/paper/2003.13989/) (2020)
- [Nonlinear 3D Face Morphable Model](https://mlsanity.com/paper/1804.03786/) (2018)
- [i3DMM: Deep Implicit 3D Morphable Model of Human Heads](https://mlsanity.com/paper/2011.14143/) (2020)
- [HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation](https://mlsanity.com/paper/2310.01406/) (2023)
- [AvatarCraft: Transforming Text into Neural Human Avatars with Parameterized Shape and Pose Control](https://mlsanity.com/paper/2303.17606/) (2023)
- [DreamFace: Progressive Generation of Animatable 3D Faces under Text Guidance](https://mlsanity.com/paper/2304.03117/) (2023)
- [Towards Understanding Camera Motions in Any Video](https://mlsanity.com/paper/2504.15376/) (2025)
- [ClipFace: Text-guided Editing of Textured 3D Morphable Models](https://mlsanity.com/paper/2212.01406/) (2022)
- [IDOL: Instant Photorealistic 3D Human Creation from a Single Image](https://mlsanity.com/paper/2412.14963/) (2024)
- [CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language](https://mlsanity.com/paper/2211.01427/) (2022)
- [DiffusionAvatars: Deferred Diffusion for High-fidelity 3D Head Avatars](https://mlsanity.com/paper/2311.18635/) (2023)
- [High-Fidelity 3D Face Generation from Natural Language Descriptions](https://mlsanity.com/paper/2305.03302/) (2023)
- [HeadArtist: Text-conditioned 3D Head Generation with Self Score Distillation](https://mlsanity.com/paper/2312.07539/) (2023)
- [InPO: Inversion Preference Optimization with Reparametrized DDIM for Efficient Diffusion Model Alignment](https://mlsanity.com/paper/2503.18454/) (2025)
- [Learning Feature Aggregation for Deep 3D Morphable Models](https://mlsanity.com/paper/2105.02173/) (2021)
- [Text-Guided 3D Face Synthesis -- From Generation to Editing](https://mlsanity.com/paper/2312.00375/) (2023)
- [Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance](https://mlsanity.com/paper/2208.14263/) (2022)
- [RapVerse: Coherent Vocals and Whole-Body Motions Generations from Text](https://mlsanity.com/paper/2405.20336/) (2024)
- [A Decoupled 3D Facial Shape Model by Adversarial Training](https://mlsanity.com/paper/1902.03619/) (2019)

## Built on by (most cited first, 1 of 1)

- [DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models](https://mlsanity.com/paper/2605.03877/) (2026)

## Abstract (arXiv)

A fundamental challenge in text-to-3D face generation is achieving high-quality geometry. The core difficulty lies in the arbitrary and intricate distribution of vertices in 3D space, making it challenging for existing models to establish clean connectivity and resulting in suboptimal geometry. To address this, our core insight is to simplify the underlying geometric structure by constraining the distribution onto a simple and regular manifold, a topological sphere. Building on this, we first propose the Spherical Geometry Representation, a novel face representation that anchors geometric signals to uniform spherical coordinates. This guarantees a regular point distribution, from which the mesh connectivity can be robustly reconstructed. Critically, this canonical sphere can be seamlessly unwrapped into a 2D map, creating a perfect synergy with powerful 2D generative models. We then introduce Spherical Geometry Diffusion, a conditional diffusion framework built upon this 2D map. It enables diverse and controllable generation by jointly modeling geometry and texture, where the geometry explicitly conditions the texture synthesis process. Our method's effectiveness is demonstrated through its success in a wide range of tasks: text-to-3D generation, face reconstruction, and text-based 3D editing. Extensive experiments show that our approach substantially outperforms existing methods in geometric quality, textual fidelity, and inference efficiency.