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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Solving image-to-3D from a single view is an ill-posed problem, and currentneural reconstruction methods addressing it through diffusion models still relyon scene-specific optimization, constraining their generalization capability.To overcome the limitations of existing approaches regarding generalization andconsistency, we introduce a novel neural rendering technique. Our approachemploys the signed distance function as the surface representation andincorporates generalizable priors through geometry-encoding volumes andHyperNetworks. Specifically, our method builds neural encoding volumes fromgenerated multi-view inputs. We adjust the weights of the SDF networkconditioned on an input image at test-time to allow model adaptation to novelscenes in a feed-forward manner via HyperNetworks. To mitigate artifactsderived from the synthesized views, we propose the use of a volume transformermodule to improve the aggregation of image features instead of processing eachviewpoint separately. Through our proposed method, dubbed as Hyper-VolTran, weavoid the bottleneck of scene-specific optimization and maintain consistencyacross the images generated from multiple viewpoints. Our experiments show theadvantages of our proposed approach with consistent results and rapidgeneration.

Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks