OPT: One-shot Pose-Controllable Talking Head Generation
Not on the map. Why?
Abstract (arXiv)
One-shot talking head generation produces lip-sync talking heads based on arbitrary audio and one source face. To guarantee the naturalness and realness, recent methods propose to achieve free pose control instead of simply editing mouth areas. However, existing methods do not preserve accurate identity of source face when generating head motions. To solve the identity mismatch problem and achieve high-quality free pose control, we present One-shot Pose-controllable Talking head generation network (OPT). Specifically, the Audio Feature Disentanglement Module separates content features from audios, eliminating the influence of speaker-specific information contained in arbitrary driving audios. Later, the mouth expression feature is extracted from the content feature and source face, during which the landmark loss is designed to enhance the accuracy of facial structure and identity preserving quality. Finally, to achieve free pose control, controllable head pose features from reference videos are fed into the Video Generator along with the expression feature and source face to generate new talking heads. Extensive quantitative and qualitative experimental results verify that OPT generates high-quality pose-controllable talking heads with no identity mismatch problem, outperforming previous SOTA methods.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 19 references that are in the map. First posted on arXiv 16 Feb 2023.
Builds on
13 of 19 references.
References · 13
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2017Squeeze-and-Excitation Networkscited by 4,763
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
- 2020A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wildcited by 669
- 2019Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Setcited by 481
- 2021AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesiscited by 339
- 2018Deep Audio-Visual Speech Recognitioncited by 318
- 2021Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representationcited by 288
- 2019Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Losscited by 252
- 2021Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head Motioncited by 148
- 2021FACIAL: Synthesizing Dynamic Talking Face with Implicit Attribute Learningcited by 90
1 more
Built on by
Citing papers per year: 2023: 3; 2024: 1; 2025: 1.
5 papers.
- 2023MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Modelcited by 16
- 2023OSM-Net: One-to-Many One-shot Talking Head Generation with Spontaneous Head Motionscited by 4
- 2024A Comprehensive Taxonomy and Analysis of Talking Head Synthesis: Techniques for Portrait Generation, Driving Mechanisms, and Editingcited by 4
- 2023A Survey on Deep Multi-modal Learning for Body Language Recognition and Generationcited by 3
- 2025Mask-Free Audio-driven Talking Face Generation for Enhanced Visual Quality and Identity Preservationcited by 1
- 2025Mask-Free Audio-driven Talking Face Generation for Enhanced Visual Quality and Identity Preservation28 Jul 2025
- 2024A Comprehensive Taxonomy and Analysis of Talking Head Synthesis: Techniques for Portrait Generation, Driving Mechanisms, and Editing15 Jun 2024
- 2023OSM-Net: One-to-Many One-shot Talking Head Generation with Spontaneous Head Motions28 Sep 2023
- 2023MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model31 Aug 2023
- 2023A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation17 Aug 2023