HeadNeRF: A Real-time NeRF-based Parametric Head Model (2112.05637v3)

Abstract: In this paper, we propose HeadNeRF, a novel NeRF-based parametric head model that integrates the neural radiance field to the parametric representation of the human head. It can render high fidelity head images in real-time on modern GPUs, and supports directly controlling the generated images' rendering pose and various semantic attributes. Different from existing related parametric models, we use the neural radiance fields as a novel 3D proxy instead of the traditional 3D textured mesh, which makes that HeadNeRF is able to generate high fidelity images. However, the computationally expensive rendering process of the original NeRF hinders the construction of the parametric NeRF model. To address this issue, we adopt the strategy of integrating 2D neural rendering to the rendering process of NeRF and design novel loss terms. As a result, the rendering speed of HeadNeRF can be significantly accelerated, and the rendering time of one frame is reduced from 5s to 25ms. The well designed loss terms also improve the rendering accuracy, and the fine-level details of the human head, such as the gaps between teeth, wrinkles, and beards, can be represented and synthesized by HeadNeRF. Extensive experimental results and several applications demonstrate its effectiveness. The trained parametric model is available at https://github.com/CrisHY1995/headnerf.

• The paper introduces HeadNeRF, a NeRF-based parametric head model that enables independent control over pose, identity, expression, and appearance.
• It achieves real-time performance at over 40 FPS by combining 2D neural and volume rendering, reducing frame time from 5 s to 25 ms.
• The model’s latent code separation facilitates semantic editing such as facial expression transfer, outperforming existing GAN-based and parametric approaches.

An Expert Overview of "HeadNeRF: A Real-time NeRF-based Parametric Head Model"

The paper presented delineates a significant contribution to the field of computer vision and graphics through the introduction of HeadNeRF, a NeRF-based parametric model for realistic head rendering. The paper builds upon the neural radiance fields (NeRF), which have emerged as a powerful approach for 3D scene representation and novel view synthesis. Unlike traditional parametric head models that rely on 3D textured meshes, HeadNeRF utilizes NeRF as a 3D proxy, offering enhanced fidelity in rendering and intrinsic multi-view consistency.

Key Contributions

1. NeRF-based Parametric Model: HeadNeRF is distinguished as one of the first to integrate NeRF into a parametric head model. This integration facilitates the independent control of rendering pose, identity, expression, and appearance, allowing for high-fidelity head image generation.
2. Efficient Training and Rendering Strategy: The proposal effectively addresses the computational challenges inherent in NeRF-based models by combining 2D neural rendering with volume rendering. This approach notably enhances rendering speed and permits real-time operation at over 40 frames per second without notable losses in rendering quality.
3. Semantic Attribute Manipulation: By disentangling identity, expression, and appearance into latent codes, the model supports explicit semantic editing of rendered images. This separation of attributes enables novel applications such as facial expression transfer between individuals in images.

Numerical Results and Model Performance

The rendering improvements facilitated by HeadNeRF are quantified through a dramatic reduction in frame rendering time from five seconds to approximately 25 milliseconds, enabling real-time performance. The PSNR values obtained in experimental evaluations range from 23.3 to 30.6 across different datasets, highlighting HeadNeRF's robust fidelity in rendering tasks. Comparative results indicate superior multi-view consistency and quality compared to existing state-of-the-art NeRF-based GANs and parametric models like pi-GAN and GIRAFFE, particularly in multi-view settings and semantic editing applications.

Implications and Future Directions

Practically, HeadNeRF presents a tool with applications extending to real-time rendering for entertainment, gaming, and virtual reality. Its ability to perform robustly with mere 2D images simplifies data requirements compared to conventional methods demanding 3D scans. Theoretically, it ties together attributes of both 2D GANs and NeRF to provide a versatile framework for 3D-aware image synthesis, offering possibilities for future work in complex scene rendering.

Future research directions might explore increasing the diversity of training datasets to enhance the model's representational capacity and robustness to a broader array of headgear and illumination conditions. Additionally, employing self-supervised learning strategies may further bolster HeadNeRF's versatility in capturing and modeling increasingly diverse face/head renditions.

Conclusion

In summation, the development of HeadNeRF represents a unique stride in parametric modeling by leveraging the strengths of neural radiance fields for dynamic and high-quality head rendering. It opens the avenue for a paradigm shift in how parametric models are constructed and utilized, offering real-time capabilities, aesthetic quality, and an extensive range of applications. This work underscores the continual advancement and integration of machine learning techniques and computer graphics, propelling forward the boundaries of digital human representation and synthesis.