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

We introduce ShowRoom3D, a three-stage approach for generating high-quality3D room-scale scenes from texts. Previous methods using 2D diffusion priors tooptimize neural radiance fields for generating room-scale scenes have shownunsatisfactory quality. This is primarily attributed to the limitations of 2Dpriors lacking 3D awareness and constraints in the training methodology. Inthis paper, we utilize a 3D diffusion prior, MVDiffusion, to optimize the 3Droom-scale scene. Our contributions are in two aspects. Firstly, we propose aprogressive view selection process to optimize NeRF. This involves dividing thetraining process into three stages, gradually expanding the camera samplingscope. Secondly, we propose the pose transformation method in the second stage.It will ensure MVDiffusion provide the accurate view guidance. As a result,ShowRoom3D enables the generation of rooms with improved structural integrity,enhanced clarity from any view, reduced content repetition, and higherconsistency across different perspectives. Extensive experiments demonstratethat our method, significantly outperforms state-of-the-art approaches by alarge margin in terms of user study.

ShowRoom3D: Text to High-Quality 3D Room Generation Using 3D Priors