# FLNet: Landmark Driven Fetching and Learning Network for Faithful Talking Facial Animation Synthesis

arXiv: https://arxiv.org/abs/1911.09224 · HTML: https://mlsanity.com/paper/1911.09224/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 28 of 28)

- [DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation](https://mlsanity.com/paper/2301.03786/) (2023)
- [Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert](https://mlsanity.com/paper/2303.17480/) (2023)
- [Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis](https://mlsanity.com/paper/2207.11770/) (2022)
- [Progressive Disentangled Representation Learning for Fine-Grained Controllable Talking Head Synthesis](https://mlsanity.com/paper/2211.14506/) (2022)
- [One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field](https://mlsanity.com/paper/2304.05097/) (2023)
- [VividTalk: One-Shot Audio-Driven Talking Head Generation Based on 3D Hybrid Prior](https://mlsanity.com/paper/2312.01841/) (2023)
- [DPE: Disentanglement of Pose and Expression for General Video Portrait Editing](https://mlsanity.com/paper/2301.06281/) (2023)
- [Implicit Warping for Animation with Image Sets](https://mlsanity.com/paper/2210.01794/) (2022)
- [Structure-Aware Motion Transfer with Deformable Anchor Model](https://mlsanity.com/paper/2204.05018/) (2022)
- [Learned Spatial Representations for Few-shot Talking-Head Synthesis](https://mlsanity.com/paper/2104.14557/) (2021)
- [Sparse to Dense Motion Transfer for Face Image Animation](https://mlsanity.com/paper/2109.00471/) (2021)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [Flow Guided Transformable Bottleneck Networks for Motion Retargeting](https://mlsanity.com/paper/2106.07771/) (2021)
- [VirtualCube: An Immersive 3D Video Communication System](https://mlsanity.com/paper/2112.06730/) (2021)
- [Make Your Actor Talk: Generalizable and High-Fidelity Lip Sync with Motion and Appearance Disentanglement](https://mlsanity.com/paper/2406.08096/) (2024)
- [Universal Facial Encoding of Codec Avatars from VR Headsets](https://mlsanity.com/paper/2407.13038/) (2024)
- [ConsistentAvatar: Learning to Diffuse Fully Consistent Talking Head Avatar with Temporal Guidance](https://mlsanity.com/paper/2411.15436/) (2024)
- [Motion and Appearance Adaptation for Cross-Domain Motion Transfer](https://mlsanity.com/paper/2209.14529/) (2022)
- [Expressive Talking Head Video Encoding in StyleGAN2 Latent-Space](https://mlsanity.com/paper/2203.14512/) (2022)
- [Emotional Conversation: Empowering Talking Faces with Cohesive Expression, Gaze and Pose Generation](https://mlsanity.com/paper/2406.07895/) (2024)
- [FD2Talk: Towards Generalized Talking Head Generation with Facial Decoupled Diffusion Model](https://mlsanity.com/paper/2408.09384/) (2024)
- [Dual-MTGAN: Stochastic and Deterministic Motion Transfer for Image-to-Video Synthesis](https://mlsanity.com/paper/2102.13329/) (2021)
- [GaFET: Learning Geometry-aware Facial Expression Translation from In-The-Wild Images](https://mlsanity.com/paper/2308.03413/) (2023)
- [Solutions to Deepfakes: Can Camera Hardware, Cryptography, and Deep Learning Verify Real Images?](https://mlsanity.com/paper/2407.04169/) (2024)
- [Removing Averaging: Personalized Lip-Sync Driven Characters Based on Identity Adapter](https://mlsanity.com/paper/2503.06397/) (2025)
- [Follow Your Motion: A Generic Temporal Consistency Portrait Editing Framework with Trajectory Guidance](https://mlsanity.com/paper/2503.22225/) (2025)
- [Face Animation with Multiple Source Images](https://mlsanity.com/paper/2212.00256/) (2022)
- [Talking Head Synthesis with Facial Landmark Guidance via 3D Gaussian Splatting](https://mlsanity.com/paper/2609.17422/) (2026)

## Abstract (arXiv)

Talking face synthesis has been widely studied in either appearance-based or warping-based methods. Previous works mostly utilize single face image as a source, and generate novel facial animations by merging other person's facial features. However, some facial regions like eyes or teeth, which may be hidden in the source image, can not be synthesized faithfully and stably. In this paper, We present a landmark driven two-stream network to generate faithful talking facial animation, in which more facial details are created, preserved and transferred from multiple source images instead of a single one. Specifically, we propose a network consisting of a learning and fetching stream. The fetching sub-net directly learns to attentively warp and merge facial regions from five source images of distinctive landmarks, while the learning pipeline renders facial organs from the training face space to compensate. Compared to baseline algorithms, extensive experiments demonstrate that the proposed method achieves a higher performance both quantitatively and qualitatively. Codes are at https://github.com/kgu3/FLNet_AAAI2020.