Head2Head: Video-based Neural Head Synthesis (2005.10954v1)

Abstract: In this paper, we propose a novel machine learning architecture for facial reenactment. In particular, contrary to the model-based approaches or recent frame-based methods that use Deep Convolutional Neural Networks (DCNNs) to generate individual frames, we propose a novel method that (a) exploits the special structure of facial motion (paying particular attention to mouth motion) and (b) enforces temporal consistency. We demonstrate that the proposed method can transfer facial expressions, pose and gaze of a source actor to a target video in a photo-realistic fashion more accurately than state-of-the-art methods.

• The paper Head2Head introduces a neural architecture for video-based head synthesis that emphasizes temporal consistency and comprehensive facial motion transfer.
• The methodology combines 3D facial reconstruction and tracking with a GAN-based video rendering network to generate realistic and temporally stable footage.
• Head2Head demonstrates superior quantitative performance with lower pixel error rates and qualitative results showing higher realism compared to prior methods.

Head2Head: Video-Based Neural Head Synthesis

The paper, "Head2Head: Video-Based Neural Head Synthesis," introduces a novel machine learning architecture aimed at enhancing the facial reenactment process in video synthesis. The approach diverges from traditional methods by exploiting the unique structure of facial motion and enforcing temporal consistency, offering improvements over current state-of-the-art techniques.

Key Contributions

The paper presents several significant contributions:

1. Neural Architecture: Unlike model-based approaches or recent deep convolutional neural networks (DCNNs) that generate individual frames, the method proposed emphasizes the temporal coherence of video synthesis, particularly in dynamic facial elements like mouth movements.
2. Comprehensive Facial Motion Transfer: The method demonstrates enhanced realism by converging facial expression, pose, and gaze, fully transferring motion from a source actor to a target video. This integrated approach mitigates the common issues of mismatched head movements seen in many reenactment systems.
3. Video Rendering Pipeline: The proposed architecture uniquely combines 3D facial reconstruction and tracking with a sophisticated GAN-based video rendering structure, showcasing improved accuracy in synthesizing realistic facial performances.

Technical Overview

The methodology is segmented into two stages:

• 3D Facial Reconstruction and Tracking: Using 3D Morphable Models (3DMMs) and sparse-landmark-based strategies, this stage efficiently estimates shape and pose parameters across sequences. The approach circumvents the complex analysis-by-synthesis frameworks, ensuring robustness and swiftness in parameter estimation.
• Video Rendering Neural Network: Trained in a self-reenactment setting using conditional input images (Normalised Mean Face Coordinates - NMFC), this GAN-based rendering network generates temporally stable, high-quality videos. Image and dynamics discriminators are employed to ensure realism and temporal coherence.

Comparative Performance

Quantitative analysis indicates lower average pixel error rates compared to previous methods, substantiating the improved fidelity of the Head2Head approach. Qualitative assessments demonstrate superior image quality and realistic synthesis in applications like self-reenactment tasks, outperforming techniques such as pix2pix, vid2vid, and Deep Video Portraits.

Implications and Future Directions

The practical implications of this research are substantial, spanning domains like video editing, virtual reality, and telepresence. Theoretical advancements lie in the use of temporal coherence in video synthesis, presenting opportunities to refine methodologies in AI-driven image and video rendering. Future work may explore enhancements in the accuracy of facial flow estimation and the incorporation of additional neural discriminators to refine specific facial elements further.

Given the robust and efficient results achieved, Head2Head positions itself as a compelling method in the arena of facial reenactment, leveraging machine learning's capabilities to render highly realistic and temporally coherent synthetic videos. Continued exploration in this area holds potential for even greater advancements, particularly in real-time applications and generalized settings beyond controlled environments.