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 present the Locally Adaptive Morphable Model (LAMM), a highly flexibleAuto-Encoder (AE) framework for learning to generate and manipulate 3D meshes.We train our architecture following a simple self-supervised training scheme inwhich input displacements over a set of sparse control vertices are used tooverwrite the encoded geometry in order to transform one training sample intoanother. During inference, our model produces a dense output that adhereslocally to the specified sparse geometry while maintaining the overallappearance of the encoded object. This approach results in state-of-the-artperformance in both disentangling manipulated geometry and 3D meshreconstruction. To the best of our knowledge LAMM is the first end-to-endframework that enables direct local control of 3D vertex geometry in a singleforward pass. A very efficient computational graph allows our network to trainwith only a fraction of the memory required by previous methods and run fasterduring inference, generating 12k vertex meshes at $>$60fps on a single CPUthread. We further leverage local geometry control as a primitive for higherlevel editing operations and present a set of derivative capabilities such asswapping and sampling object parts. Code and pretrained models can be found athttps://github.com/michaeltrs/LAMM.

Locally Adaptive Neural 3D Morphable Models