# Joint Face Detection and Facial Motion Retargeting for Multiple Faces

arXiv: https://arxiv.org/abs/1902.10744 · HTML: https://mlsanity.com/paper/1902.10744/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 18 of 18)

- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [High-resolution Face Swapping via Latent Semantics Disentanglement](https://mlsanity.com/paper/2203.15958/) (2022)
- [FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction](https://mlsanity.com/paper/2111.01082/) (2021)
- [Going Deeper Into Face Detection: A Survey](https://mlsanity.com/paper/2103.14983/) (2021)
- [Robust Egocentric Photo-realistic Facial Expression Transfer for Virtual Reality](https://mlsanity.com/paper/2104.04794/) (2021)
- [ReliableSwap: Boosting General Face Swapping Via Reliable Supervision](https://mlsanity.com/paper/2306.05356/) (2023)
- [CRFace: Confidence Ranker for Model-Agnostic Face Detection Refinement](https://mlsanity.com/paper/2103.07017/) (2021)
- [Weakly-Supervised Multi-Face 3D Reconstruction](https://mlsanity.com/paper/2101.02000/) (2021)
- [JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution](https://mlsanity.com/paper/2103.13676/) (2021)
- [Perspective Reconstruction of Human Faces by Joint Mesh and Landmark Regression](https://mlsanity.com/paper/2208.07142/) (2022)
- [An Implicit Physical Face Model Driven by Expression and Style](https://mlsanity.com/paper/2401.15414/) (2024)
- [ExtSwap: Leveraging Extended Latent Mapper for Generating High Quality Face Swapping](https://mlsanity.com/paper/2310.12736/) (2023)
- [Facial Expression Re-targeting from a Single Character](https://mlsanity.com/paper/2306.12188/) (2023)
- [Rank-based No-reference Quality Assessment for Face Swapping](https://mlsanity.com/paper/2406.01884/) (2024)
- [Deep Learning Based Facial Retargeting Using Local Patches](https://mlsanity.com/paper/2601.08429/) (2026)
- [Improving Facial Emotion Recognition through Dataset Merging and Balanced Training Strategies](https://mlsanity.com/paper/2604.20307/) (2026)
- [Multi-Domain Norm-referenced Encoding Enables Data Efficient Transfer Learning of Facial Expression Recognition](https://mlsanity.com/paper/2304.02309/) (2023)
- [MSMA: Multi-Scale Feature Fusion For Multi-Attribute 3D Face Reconstruction From Unconstrained Images](https://mlsanity.com/paper/2509.11763/) (2025)

## Abstract (arXiv)

Facial motion retargeting is an important problem in both computer graphics and vision, which involves capturing the performance of a human face and transferring it to another 3D character. Learning 3D morphable model (3DMM) parameters from 2D face images using convolutional neural networks is common in 2D face alignment, 3D face reconstruction etc. However, existing methods either require an additional face detection step before retargeting or use a cascade of separate networks to perform detection followed by retargeting in a sequence. In this paper, we present a single end-to-end network to jointly predict the bounding box locations and 3DMM parameters for multiple faces. First, we design a novel multitask learning framework that learns a disentangled representation of 3DMM parameters for a single face. Then, we leverage the trained single face model to generate ground truth 3DMM parameters for multiple faces to train another network that performs joint face detection and motion retargeting for images with multiple faces. Experimental results show that our joint detection and retargeting network has high face detection accuracy and is robust to extreme expressions and poses while being faster than state-of-the-art methods.