Implicit Identity Representation Conditioned Memory Compensation Network for Talking Head video Generation (2307.09906v3)

Abstract: Talking head video generation aims to animate a human face in a still image with dynamic poses and expressions using motion information derived from a target-driving video, while maintaining the person's identity in the source image. However, dramatic and complex motions in the driving video cause ambiguous generation, because the still source image cannot provide sufficient appearance information for occluded regions or delicate expression variations, which produces severe artifacts and significantly degrades the generation quality. To tackle this problem, we propose to learn a global facial representation space, and design a novel implicit identity representation conditioned memory compensation network, coined as MCNet, for high-fidelity talking head generation.~Specifically, we devise a network module to learn a unified spatial facial meta-memory bank from all training samples, which can provide rich facial structure and appearance priors to compensate warped source facial features for the generation. Furthermore, we propose an effective query mechanism based on implicit identity representations learned from the discrete keypoints of the source image. It can greatly facilitate the retrieval of more correlated information from the memory bank for the compensation. Extensive experiments demonstrate that MCNet can learn representative and complementary facial memory, and can clearly outperform previous state-of-the-art talking head generation methods on VoxCeleb1 and CelebV datasets. Please check our \href{https://github.com/harlanhong/ICCV2023-MCNET}{Project}.

• The paper introduces a novel global meta-memory bank that accumulates facial priors to enhance the quality of talking head animations.
• It employs an implicit identity representation derived from source keypoints to condition and refine the compensation process.
• Experimental results on VoxCeleb1 and CelebV demonstrate significant gains in SSIM, PSNR, and LPIPS, confirming improved motion and identity fidelity.

Overview of Memory Compensation Network for Talking Head Video Generation

This paper introduces a novel model called the Memory Compensation Network (MCNet) for improving the fidelity of talking head video generation. The central challenge in this domain is generating realistic animations from a static image, driven by the motion captured in a dynamic video. The task becomes particularly challenging when the driving video involves complex, dramatic motions that the single source image can't fully represent, leading to artifacts and degraded output quality.

MCNet proposes an innovative approach that leverages a global meta-memory bank to enhance the generation process. The model builds upon an implicit identity representation conditioned memory compensation mechanism. The aim is to provide a comprehensive spatial facial structure and appearance compensation that significantly improves the generation quality over previous methodologies.

Key Contributions

1. Global Meta-Memory Bank: The model introduces a novel global meta-memory bank. This bank is trained to learn global facial priors by accumulating the optimization gradients from all training samples. It serves as a rich repository of facial structure and appearance patterns, which are utilized to compensate for ambiguous facial details during generation.
2. Implicit Identity Representation: MCNet devises an implicit identity representation derived from source image keypoints and warped feature maps. This identity representation conditions the retrieval from the global meta-memory, allowing the model to generate source-aware compensations that are more correlated with the unique traits of the target individual.
3. Memory Compensation Framework: The paper includes a dynamic cross-attention mechanism, housed within a novel Memory Compensation Module (MCM), which spatially compensates the warped source feature maps. This mechanism is notable for enhancing the realism of generated facial identities by using the identity-conditioned memories.

Experimental Evaluation

The researchers conducted extensive experiments on the VoxCeleb1 and CelebV datasets, comparing MCNet with multiple state-of-the-art methods. In terms of quantitative results, MCNet consistently outperformed competitive approaches across various metrics, including SSIM, PSNR, and LPIPS, which represent different aspects of image quality and structural similarity. Notably, MCNet showed significant improvements in both identity preservation and motion estimation metrics, such as AED and AKD, respectively.

Qualitative evaluations further supported these findings. The generated outputs of MCNet better maintained facial coherence, particularly under conditions of large motion or occlusion, when compared with other leading methods such as FOMM and TPSM.

Implications and Future Directions

The introduction of the memory bank approach opens new avenues for addressing generating tasks that suffer from input sparsity. This paradigm shows promise in leveraging extensive prior knowledge to overcome challenges associated with ambiguous input. The idea of a memory bank could be extrapolated to other domains, such as unsupervised learning, where rich prior knowledge from sparse data can significantly improve the task performance.

As the methodology matures, we can anticipate developments in areas such as personalized avatars and virtual humans, where preserving identity and expression dynamics is critical. Furthermore, integrating this framework with emerging neural architectures might offer promising results in other domains like dynamic video editing and enhancement.

In conclusion, the MCNet framework provides a significant advancement in talking head generation by implementing a structure that learns and utilizes global facial priors through an embedded memory system. This approach not only leads to enhanced generation quality but also sets the groundwork for future innovations in memory-augmented neural networks.