MoFaNeRF: Morphable Facial Neural Radiance Field

We propose a parametric model that maps free-view images into a vector space of coded facial shape, expression and appearance with a neural radiance field, namely Morphable Facial NeRF. Specifically, MoFaNeRF takes the coded facial shape, expression and appearance along with space coordinate and view direction as input to an MLP, and outputs the radiance of the space point for photo-realistic image synthesis. Compared with conventional 3D morphable models (3DMM), MoFaNeRF shows superiority in directly synthesizing photo-realistic facial details even for eyes, mouths, and beards. Also, continuous face morphing can be easily achieved by interpolating the input shape, expression and appearance codes. By introducing identity-specific modulation and texture encoder, our model synthesizes accurate photometric details and shows strong representation ability. Our model shows strong ability on multiple applications including image-based fitting, random generation, face rigging, face editing, and novel view synthesis. Experiments show that our method achieves higher representation ability than previous parametric models, and achieves competitive performance in several applications. To the best of our knowledge, our work is the first facial parametric model built upon a neural radiance field that can be used in fitting, generation and manipulation. The code and data is available at https://github.com/zhuhao-nju/mofanerf.Comment: accepted to ECCV2022; code available at http://github.com/zhuhao-nju/mofaner

NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination

Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lighting models such as environment maps to represent non-distant illumination, or using a network to fit indirect light modeling without a solid basis, can lead to an undesirable decomposition between lighting and material. To address this, we propose a fully differentiable framework named neural ambient illumination (NeAI) that uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Together with integral lobe encoding for roughness-adaptive specular lobe and leveraging the pre-convoluted background for accurate decomposition, the proposed method represents a significant step towards integrating physically based rendering into the NeRF representation. The experiments demonstrate the superior performance of novel-view rendering compared to previous works, and the capability to re-render objects under arbitrary NeRF-style environments opens up exciting possibilities for bridging the gap between virtual and real-world scenes. The project and supplementary materials are available at https://yiyuzhuang.github.io/NeAI/.Comment: Project page: <a class="link-external link-https" href="https://yiyuzhuang.github.io/NeAI/" rel="external noopener nofollow">https://yiyuzhuang.github.io/NeAI/</a

Surviving an infectious disease outbreak: How does nurse calling influence performance during the COVID‐19 fight?

Aim: To assess the performance of frontline nurses, who believed they were living out their calling, during the Coronavirus Disease 2019 (COVID-19) pandemic. Background: Although as a profession nursing generally requires high levels of performance, the disruption arising from an infectious disease outbreak increases the work stress and decreases the performance of frontline nurses. How this situation can be improved has yet to be thoroughly examined. Method: We used a snowball sampling technique to recruit 339 nurses who were originally from outside of Hubei but volunteered to join medical teams going to Hubei to tackle COVID-19. Results: Drawing on the theory of work as a calling, we found that living a calling had a positive effect on frontline nurses’ performance through the clinical and relational care they provided. Perceived supervisor support strengthened these mediated relationships. Conclusion: Our findings indicate that despite the constraints associated with pandemics, frontline nurses who are living a calling are able to provide better clinical and relational care to infected patients, which in turn improves their performance. Implications for Nursing Management: The findings of this study suggest that hospitals can introduce career education interventions to enhance nurses’ ability to discern and live out their calling to improve their performance