English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recently 3D-aware GAN methods with neural radiance field have developedrapidly. However, current methods model the whole image as an overall neuralradiance field, which limits the partial semantic editability of syntheticresults. Since NeRF renders an image pixel by pixel, it is possible to splitNeRF in the spatial dimension. We propose a Compositional Neural Radiance Field(CNeRF) for semantic 3D-aware portrait synthesis and manipulation. CNeRFdivides the image by semantic regions and learns an independent neural radiancefield for each region, and finally fuses them and renders the complete image.Thus we can manipulate the synthesized semantic regions independently, whilefixing the other parts unchanged. Furthermore, CNeRF is also designed todecouple shape and texture within each semantic region. Compared tostate-of-the-art 3D-aware GAN methods, our approach enables fine-grainedsemantic region manipulation, while maintaining high-quality 3D-consistentsynthesis. The ablation studies show the effectiveness of the structure andloss function used by our method. In addition real image inversion and cartoonportrait 3D editing experiments demonstrate the application potential of ourmethod.

Semantic 3D-aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field