Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (2104.11116v1)

Abstract: While accurate lip synchronization has been achieved for arbitrary-subject audio-driven talking face generation, the problem of how to efficiently drive the head pose remains. Previous methods rely on pre-estimated structural information such as landmarks and 3D parameters, aiming to generate personalized rhythmic movements. However, the inaccuracy of such estimated information under extreme conditions would lead to degradation problems. In this paper, we propose a clean yet effective framework to generate pose-controllable talking faces. We operate on raw face images, using only a single photo as an identity reference. The key is to modularize audio-visual representations by devising an implicit low-dimension pose code. Substantially, both speech content and head pose information lie in a joint non-identity embedding space. While speech content information can be defined by learning the intrinsic synchronization between audio-visual modalities, we identify that a pose code will be complementarily learned in a modulated convolution-based reconstruction framework. Extensive experiments show that our method generates accurately lip-synced talking faces whose poses are controllable by other videos. Moreover, our model has multiple advanced capabilities including extreme view robustness and talking face frontalization. Code, models, and demo videos are available at https://hangz-nju-cuhk.github.io/projects/PC-AVS.

• The paper introduces PC-AVS, a framework that disentangles identity, speech content, and head pose using an implicit low-dimensional representation.
• It leverages contrastive learning with InfoNCE loss to map audio inputs to synchronized lip movements, achieving high lip-sync accuracy.
• Experimental results on LRW and VoxCeleb2 demonstrate superior image quality and pose realism compared to state-of-the-art methods.

Summary of Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation

The paper presented in the paper entitled "Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation" outlines a methodological advance in the domain of audio-driven talking face generation. Despite existing progress in generating accurate mouth movements synchronized with audio inputs, the control of head poses has remained a challenging aspect of rendering lifelike talking faces. This paper advances the field by introducing the Pose-Controllable Audio-Visual System (PC-AVS), a framework capable of controlling head poses independently of mouth synchronization using audio inputs.

Core Methodology

The core of the PC-AVS framework is the implicit modularization of audio-visual representations, thereby disentangling the identity, speech content, and head pose into separate feature spaces. This modularization is achieved by devising a low-dimensional pose code through a modulated convolution-based reconstruction framework. Importantly, the pose information is encoded without reliance on structural intermediates such as landmarks or 3D models, which are prone to inaccuracies under extreme visual conditions.

Technical Contributions

1. Implicit Pose Encoding: The paper proposes using an implicit low-dimensional pose code informed by prior knowledge on 3D pose parameters. This approach avoids explicit pose estimation, which can suffer from inaccuracies in challenging viewing conditions.
2. Audio-Visual Synchronization: By leveraging the natural synchronization between auditory data and visual mouth movements, the framework utilizes contrastive learning techniques with InfoNCE loss to effectively map audio inputs to synchronized visual speech content, thus enhancing lip-sync accuracy.
3. Generator Design: The framework employs a generator with modulated convolution layers, where the learned features modulate the filter weights. This design choice contrasts with previous methods reliant on skip connections, allowing for more expressive modulation of identity and pose information in the generation process.

Experimental Validation

Extensive experiments are conducted across the LRW and VoxCeleb2 datasets, demonstrating substantial improvements over state-of-the-art methods such as Wav2Lip and MakeitTalk in terms of lip-sync accuracy, image quality, and pose realism. Notably, the system achieves high robustness across varying viewing angles and challenging conditions without the requirement for structural preprocessing, which is a common bottleneck in other approaches.

Implications and Future Directions

The proposed framework contributes significantly to the theoretical and practical domains of audio-visual generation. The disentangled space facilitates the direct manipulation of pose independently of mouth sync, offering new avenues for applications like digital human animation and visual dubbing. The implicit learning of pose codes based on low-dimensional representations could inspire further research into unsupervised and semi-supervised learning strategies that do not rely on handcrafted features or explicit labels. Looking toward future advancements, integrating this approach with dynamic identity adaptation or extending it to support multilingual audio inputs could broaden its applicability.

The PC-AVS thus represents a meaningful step forward in generating high-fidelity audio-visual content, balancing the multiple demands of identity preservation, lip synchronization, and pose variability in a computationally efficient manner.