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

Implicit neural fields, typically encoded by a multilayer perceptron (MLP)that maps from coordinates (e.g., xyz) to signals (e.g., signed distances),have shown remarkable promise as a high-fidelity and compact representation.However, the lack of a regular and explicit grid structure also makes itchallenging to apply generative modeling directly on implicit neural fields inorder to synthesize new data. To this end, we propose HyperDiffusion, a novelapproach for unconditional generative modeling of implicit neural fields.HyperDiffusion operates directly on MLP weights and generates new neuralimplicit fields encoded by synthesized MLP parameters. Specifically, acollection of MLPs is first optimized to faithfully represent individual datasamples. Subsequently, a diffusion process is trained in this MLP weight spaceto model the underlying distribution of neural implicit fields. HyperDiffusionenables diffusion modeling over a implicit, compact, and yet high-fidelityrepresentation of complex signals across 3D shapes and 4D mesh animationswithin one single unified framework.

HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion