  We present As-Plausible-as-Possible (APAP) mesh deformation technique that
leverages 2D diffusion priors to preserve the plausibility of a mesh under
user-controlled deformation. Our framework uses per-face Jacobians to represent
mesh deformations, where mesh vertex coordinates are computed via a
differentiable Poisson Solve. The deformed mesh is rendered, and the resulting
2D image is used in the Score Distillation Sampling (SDS) process, which
enables extracting meaningful plausibility priors from a pretrained 2D
diffusion model. To better preserve the identity of the edited mesh, we
fine-tune our 2D diffusion model with LoRA. Gradients extracted by SDS and a
user-prescribed handle displacement are then backpropagated to the per-face
Jacobians, and we use iterative gradient descent to compute the final
deformation that balances between the user edit and the output plausibility. We
evaluate our method with 2D and 3D meshes and demonstrate qualitative and
quantitative improvements when using plausibility priors over
geometry-preservation or distortion-minimization priors used by previous
techniques. Our project page is at: https://as-plausible-aspossible.github.io/
