Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction (2012.03065v1)

Abstract: We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face. Digitally modeling and reconstructing a talking human is a key building-block for a variety of applications. Especially, for telepresence applications in AR or VR, a faithful reproduction of the appearance including novel viewpoints or head-poses is required. In contrast to state-of-the-art approaches that model the geometry and material properties explicitly, or are purely image-based, we introduce an implicit representation of the head based on scene representation networks. To handle the dynamics of the face, we combine our scene representation network with a low-dimensional morphable model which provides explicit control over pose and expressions. We use volumetric rendering to generate images from this hybrid representation and demonstrate that such a dynamic neural scene representation can be learned from monocular input data only, without the need of a specialized capture setup. In our experiments, we show that this learned volumetric representation allows for photo-realistic image generation that surpasses the quality of state-of-the-art video-based reenactment methods.

• The paper introduces a novel method for creating photorealistic 4D facial avatars by learning dynamic neural radiance fields from monocular video input.
• Empirical results demonstrate this dynamic NeRF approach significantly outperforms prior methods in photorealism and versatility across key metrics.
• The research simplifies 4D avatar creation for AR/VR and telepresence, enabling photorealistic results from simple monocular video input.

Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction: An Essay

This paper introduces an innovative approach to reconstructing 4D facial avatars utilizing dynamic neural radiance fields (NeRFs), highlighting significant advancements in the field of computer vision and graphics. The research presents a method capable of photo-realistic reproduction of dynamic human faces using only monocular input data.

Methodological Approach

The paper delineates a novel technique for capturing and rendering the dynamics and appearance of a human face with an implicit neural scene representation, leveraging the power of neural rendering combined with volumetric integration. Different from traditional explicit modeling of geometry and material properties, such as albedo and reflectance, this method encodes the face into a dynamic neural radiance field that is conditioned on low-dimensional morphable model parameters.

Features and Implementation

Key to the approach is the integration of a scene representation network with volumetric rendering, enabling the neural network to learn from monocular sequences without intricate capture setups. This hybrid rendering method, inspired by Mildenhall et al.'s earlier static scene NeRFs, is adapted for dynamic content with monocular video input, which poses substantial challenges in terms of depth and motion capture.

• Dynamic Conditioning: The system conditions the neural radiance fields on expression parameters derived from a low-dimensional morphable model, allowing ease of pose and expression manipulation.
• Volumetric Rendering: The research leverages volumetric rendering to simulate novel head poses and expressions, tackling intricacies in rendering hair, mouth interiors, and other visually complex features.
• Learning from Monocular Data: The accomplishment of learning detailed, photorealistic 3D avatars from single-view data marks a considerable achievement, potentially democratizing avatar creation using minimal hardware.

Empirical Validation

The paper demonstrates that the proposed approach surpasses contemporary methods in photorealism and versatility through quantitative and qualitative comparisons. Key metrics such as PSNR, SSIM, and LPIPS underscore the advantages of this dynamic representation over existing state-of-the-art methods like Deep Video Portraits and Deferred Neural Rendering. These metrics evidence the system's ability to maintain high fidelity especially during large pose changes with dynamic expression alterations.

Implications and Future Directions

The implications of this research are profound, offering substantial value for applications in AR/VR, telepresence, and digital content creation. By simplifying the capture setup required for 4D avatar creation, it opens paths for personal and scalable avatar generation.

Future research could explore extending this model's capability to full-body dynamics, incorporating more sophisticated morphable models that include eye movements and expressions. Additionally, the potential to enhance real-time processing and optimization of network parameters could make this approach even more practical for real-world applications.

In conclusion, this paper provides an insightful contribution to the growing body of work on neural rendering and dynamic scene representation, showcasing robust methods to push the boundaries of human digital modeling in a scalable manner. The ability to generate high-quality avatars from monocular video inputs marks a significant step towards realistic avatar-driven communication and interaction in virtual spaces.