# SPHEAR: Spherical Head Registration for Complete Statistical 3D Modeling

arXiv: https://arxiv.org/abs/2311.02461 · HTML: https://mlsanity.com/paper/2311.02461/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 97 references that are in the map.
- First posted on arXiv 4 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Attention Mesh: High-fidelity Face Mesh Prediction in Real-time](https://mlsanity.com/paper/2006.10962/) (2020)
- [HSPACE: Synthetic Parametric Humans Animated in Complex Environments](https://mlsanity.com/paper/2112.12867/) (2021)
- [Variational Auto-Decoder: A Method for Neural Generative Modeling from Incomplete Data](https://mlsanity.com/paper/1903.00840/) (2019)
- [PhoMoH: Implicit Photorealistic 3D Models of Human Heads](https://mlsanity.com/paper/2212.07275/) (2022)
- [Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions](https://mlsanity.com/paper/2209.04436/) (2022)
- [One Ring to Rule Them All: a simple solution to multi-view 3D-Reconstruction of shapes with unknown BRDF via a small Recurrent ResNet](https://mlsanity.com/paper/2104.05014/) (2021)
- [SCULPTOR: Skeleton-Consistent Face Creation Using a Learned Parametric Generator](https://mlsanity.com/paper/2209.06423/) (2022)

## Abstract (arXiv)

We present SPHEAR, an accurate, differentiable parametric statistical 3D human head model, enabled by a novel 3D registration method based on spherical embeddings. We shift the paradigm away from the classical Non-Rigid Registration methods, which operate under various surface priors, increasing reconstruction fidelity and minimizing required human intervention. Additionally, SPHEAR is a complete model that allows not only to sample diverse synthetic head shapes and facial expressions, but also gaze directions, high-resolution color textures, surface normal maps, and hair cuts represented in detail, as strands. SPHEAR can be used for automatic realistic visual data generation, semantic annotation, and general reconstruction tasks. Compared to state-of-the-art approaches, our components are fast and memory efficient, and experiments support the validity of our design choices and the accuracy of registration, reconstruction and generation techniques.