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Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss
Lele Chen, Ross K. Maddox, Zhiyao Duan, Chenliang Xu
TL;DR
Talking-face generation requires natural synchronization and temporal smoothness despite visual variation unrelated to speech and noisy audio. The paper uses a cascade that predicts facial landmarks before generating video, augmented by dynamic attention-based pixel loss and a regression-based discriminator. ATVGnet achieves the best reported performance across several datasets and qualitative and quantitative comparisons, while producing robust faces under head movements and noisy audio.
Problem
Talking-face generation must preserve natural lip synchronization and smooth temporal transitions while handling face variation, sound-irrelevant dynamics, and noisy speech.
Method
A cascade network predicts high-level facial landmarks from audio, generates landmark-conditioned video, and adds dynamic attention-based pixel loss plus a regression-based discriminator.
Results
ATVGnet achieves the best performance on several popular datasets in qualitative and quantitative comparisons, including image quality and audiovisual synchronization metrics.
Takeaways & Limitations
The approach captures informative lip and cheek movements while generating robust talking faces under significant head movements and noisy audio conditions.
Abstract
from arXiv · showhide
We devise a cascade GAN approach to generate talking face video, which is robust to different face shapes, view angles, facial characteristics, and noisy audio conditions. Instead of learning a direct mapping from audio to video frames, we propose first to transfer audio to high-level structure, i.e., the facial landmarks, and then to generate video frames conditioned on the landmarks. Compared to a direct audio-to-image approach, our cascade approach avoids fitting spurious correlations between audiovisual signals that are irrelevant to the speech content. We, humans, are sensitive to temporal discontinuities and subtle artifacts in video. To avoid those pixel jittering problems and to enforce the network to focus on audiovisual-correlated regions, we propose a novel dynamically adjustable pixel-wise loss with an attention mechanism. Furthermore, to generate a sharper image with well-synchronized facial movements, we propose a novel regression-based discriminator structure, which considers sequence-level information along with frame-level information. Thoughtful experiments on several datasets and real-world samples demonstrate significantly better results obtained by our method than the state-of-the-art methods in both quantitative and qualitative comparisons.
1. Introduction
Talking-face generation must synchronize speech with smooth facial video while handling visual variation unrelated to audio. The paper addresses these challenges with a cascade architecture, dynamic attention-based loss, and regression-based discrimination.
- The task generates a target subject speaking arbitrary audio while preserving natural lip synchronization and smooth facial transitions.
- Temporal discontinuities, pixel jittering, and slight audio–facial misalignment make video generation harder than independent image generation.
- The regression-based discriminator combines sequence-level temporal information with frame-level pixel variations to improve synchronization and facial-transition smoothness.
- The cascade first estimates facial landmarks from audio, then generates pixel variations conditioned on those landmarks to reduce sound-irrelevant visual dynamics.
- A dynamically adjustable pixel-wise loss with attention emphasizes audiovisual-correlated regions and alleviates temporal discontinuities and subtle artifacts.
- Experiments on GRID, LRW, VoxCeleb, and TCD report that ATVGnet outperforms compared methods quantitatively and qualitatively, including under head movements and noisy audio.
2. Related Work
Earlier talking-face systems either targeted specific speakers or generated arbitrary faces from arbitrary audio, while related video-generation work used high-level representations and weighted losses. This paper combines these directions in its audio-to-landmarks and landmark-conditioned video architecture.
- Traditional talking-face methods were limited to specific speakers and could require extensive footage of the target person.
- More recent GAN and encoder-decoder methods generate arbitrary faces from arbitrary input audio.
- The proposed architecture separates an audio transformation network from a visual generation network, using landmarks as an intermediate representation.
- The attention mechanism motivates a dynamically adjustable loss that emphasizes audiovisual regions instead of using fixed loss weights.
3. Architecture
The architecture decouples audio-to-landmark prediction from landmark-conditioned video synthesis, while combining attention-based pixel weighting with a regression discriminator for temporal and frame-level quality.
- Cascade Structure: The model uses an audio transformation network and visual generation network as separate cascade components.The AT-net predicts facial landmarks from audio and identity landmarks; the VG-net uses those landmarks and an example image to synthesize frames.
- Cascade Structure: The AT-net is a conditional LSTM encoder-decoder, while the VG-net is a multi-modal convolutional recurrent network.The two networks are trained in a decoupled way, with ground-truth landmarks used for VG-net teacher forcing during training.
- Cascade Structure: PCA-based landmark representations reduce the effect of visual factors that are not correlated with audio.The AT-net combines audio MFCCs with target-identity landmark PCA components and predicts paired landmark components.
- Attention-Based Dynamic Pixel-wise Loss: The attention mechanism separates motion from audiovisual-non-correlated regions, stabilizing pixels where speech provides little relevant information.Attention is computed from landmark differences and used with image features and motion features in the recurrent visual generator.
- Attention-Based Dynamic Pixel-wise Loss: The dynamic pixel-wise loss weights each pixel using attention while retaining base weight β, with β = 0.5 in all experiments.The attention weight is detached during backpropagation to prevent trivial solutions, while β ensures every pixel remains optimized.
- Regression-Based Discriminator: The regression-based discriminator combines frame-level landmark regression with sequence-level discrimination.Dp regresses landmarks from example landmarks and video frames, while Ds aggregates LSTM outputs to score the overall sequence.
- Regression-Based Discriminator: The discriminator's regression loss encourages accurate face shapes, while its sequence score supports high-quality generated video.A predefined mask can penalize lip regions more strongly, and discriminator weights are fixed during generator training.
- Objective Function: The full objective linearly combines GAN and pixel-wise losses, using λ = 10.0 in the experiments.The coefficient λ controls the relative importance of the loss terms.
4. Experiments
The experiments evaluate ATVGnet’s generation quality, efficiency, synchronization, robustness, and component contributions across datasets, user studies, and qualitative comparisons.
- Inference Efficiency: 34.5 FPS inference is slightly faster than real time and exceeds the speeds of three compared methods.Inference speed is measured in frames per second.
- Quantitative Results: ATVGnet achieves the best image quality and audiovisual synchronization among compared methods on LRW and GRID.The reported metrics are SSIM, PSNR, and Landmarks Distance (LMD).
- User Studies: User studies rate ATVGnet highest for lip synchronization across all datasets and highest for authenticity on most datasets.Authenticity is slightly lower than Chung et al. on the VoxCeleb testing set, which the authors attribute to audio noise.
- Ablation Studies: Each proposed component contributes to the full model, with MMCRNN and DMA identified as critical for smoother transitions between adjacent frames.The ablation studies are conducted on LRW and GRID using ground-truth landmarks.
- Qualitative Results: Qualitative results show mouth opening closer to ground truth than Chung et al. and Zhou et al., including a sample with loud background noise.The comparison uses LRW and VoxCeleb samples.
- Robustness: Image quality decreases as noise on generated landmarks increases, while experiments with different pose angles demonstrate robustness.The pose experiments use different example images with the same audio.
5. Conclusion and Discussion
The paper concludes that ATVGnet combines landmark-based cascading, temporal modeling, dynamically adjustable loss, and regression-based discrimination for talking-face generation. It reports best performance across several datasets and identifies unconsciously generated head movements and expressions as future work.
- Conclusion: ATVGnet uses facial landmarks as intermediate high-level representations and models adjacent-frame correlations with a Multi-Modal Convolutional-RNN.The approach also includes dynamically adjustable loss and a regression-based discriminator.
- Conclusion: The final model achieves the best performance on several popular datasets in qualitative and quantitative comparisons.
- Future Work: Future work could enable unconscious head movements and expressions, which the current approach bypasses.