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 As-Plausible-as-Possible (APAP) mesh deformation technique thatleverages 2D diffusion priors to preserve the plausibility of a mesh underuser-controlled deformation. Our framework uses per-face Jacobians to representmesh deformations, where mesh vertex coordinates are computed via adifferentiable Poisson Solve. The deformed mesh is rendered, and the resulting2D image is used in the Score Distillation Sampling (SDS) process, whichenables extracting meaningful plausibility priors from a pretrained 2Ddiffusion model. To better preserve the identity of the edited mesh, wefine-tune our 2D diffusion model with LoRA. Gradients extracted by SDS and auser-prescribed handle displacement are then backpropagated to the per-faceJacobians, and we use iterative gradient descent to compute the finaldeformation that balances between the user edit and the output plausibility. Weevaluate our method with 2D and 3D meshes and demonstrate qualitative andquantitative improvements when using plausibility priors overgeometry-preservation or distortion-minimization priors used by previoustechniques.

As-Plausible-As-Possible: Plausibility-Aware Mesh Deformation Using 2D Diffusion Priors