# Disentangled Representation Learning for 3D Face Shape

arXiv: https://arxiv.org/abs/1902.09887 · HTML: https://mlsanity.com/paper/1902.09887/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

## Built on by (most cited first, 30 of 48)

- [Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation](https://mlsanity.com/paper/2104.11116/) (2021)
- [FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset](https://mlsanity.com/paper/2203.14057/) (2022)
- [MoFaNeRF: Morphable Facial Neural Radiance Field](https://mlsanity.com/paper/2112.02308/) (2021)
- [Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer](https://mlsanity.com/paper/2403.13570/) (2024)
- [Portrait4D: Learning One-Shot 4D Head Avatar Synthesis using Synthetic Data](https://mlsanity.com/paper/2311.18729/) (2023)
- [StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval](https://mlsanity.com/paper/2103.15706/) (2021)
- [FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction](https://mlsanity.com/paper/2211.13874/) (2022)
- [FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction](https://mlsanity.com/paper/2111.01082/) (2021)
- [Towards Practical Capture of High-Fidelity Relightable Avatars](https://mlsanity.com/paper/2309.04247/) (2023)
- [SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction](https://mlsanity.com/paper/2106.03021/) (2021)
- [Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild](https://mlsanity.com/paper/2305.08296/) (2023)
- [Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learning](https://mlsanity.com/paper/2108.01069/) (2021)
- [Faces that Speak: Jointly Synthesising Talking Face and Speech from Text](https://mlsanity.com/paper/2405.10272/) (2024)
- [Learning Facial Representations from the Cycle-consistency of Face](https://mlsanity.com/paper/2108.03427/) (2021)
- [Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image](https://mlsanity.com/paper/2111.03098/) (2021)
- [SketchLattice: Latticed Representation for Sketch Manipulation](https://mlsanity.com/paper/2108.11636/) (2021)
- [VRMM: A Volumetric Relightable Morphable Head Model](https://mlsanity.com/paper/2402.04101/) (2024)
- [Inverting Generative Adversarial Renderer for Face Reconstruction](https://mlsanity.com/paper/2105.02431/) (2021)
- [Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection](https://mlsanity.com/paper/2106.07852/) (2021)
- [4D Facial Expression Diffusion Model](https://mlsanity.com/paper/2303.16611/) (2023)
- [Deep Deformable 3D Caricatures with Learned Shape Control](https://mlsanity.com/paper/2207.14593/) (2022)
- [FaceTuneGAN: Face Autoencoder for Convolutional Expression Transfer Using Neural Generative Adversarial Networks](https://mlsanity.com/paper/2112.00532/) (2021)
- [Head360: Learning a Parametric 3D Full-Head for Free-View Synthesis in 360deg](https://mlsanity.com/paper/2408.00296/) (2024)
- [JoyVASA: Portrait and Animal Image Animation with Diffusion-Based Audio-Driven Facial Dynamics and Head Motion Generation](https://mlsanity.com/paper/2411.09209/) (2024)
- [Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance](https://mlsanity.com/paper/2208.14263/) (2022)
- [That's What I Said: Fully-Controllable Talking Face Generation](https://mlsanity.com/paper/2304.03275/) (2023)
- [ReliableSwap: Boosting General Face Swapping Via Reliable Supervision](https://mlsanity.com/paper/2306.05356/) (2023)
- [3D Generative Model Latent Disentanglement via Local Eigenprojection](https://mlsanity.com/paper/2302.12798/) (2023)
- [Deep Deformation Detail Synthesis for Thin Shell Models](https://mlsanity.com/paper/2102.11541/) (2021)
- [Towards Native Generative Model for 3D Head Avatar](https://mlsanity.com/paper/2410.01226/) (2024)

## Abstract (arXiv)

In this paper, we present a novel strategy to design disentangled 3D face shape representation. Specifically, a given 3D face shape is decomposed into identity part and expression part, which are both encoded and decoded in a nonlinear way. To solve this problem, we propose an attribute decomposition framework for 3D face mesh. To better represent face shapes which are usually nonlinear deformed between each other, the face shapes are represented by a vertex based deformation representation rather than Euclidean coordinates. The experimental results demonstrate that our method has better performance than existing methods on decomposing the identity and expression parts. Moreover, more natural expression transfer results can be achieved with our method than existing methods.