LEMON: Localized Editing With Mesh Optimization and Neural Shaders

Furkan Mert Algan, Umut Yazgan, Driton Salihu, Cem Eteke, Eckehard Steinbach

Abstract: We present LEMON, a mesh editing pipeline that integrates neural deferred shading with localized mesh optimization to enable fast and precise editing of polygonal meshes guided by text prompts. Existing solutions for this problem tend to focus on a single task, either geometry or novel view synthesis, which often leads to disjointed results between the mesh and view. Our approach starts by identifying the most important vertices in the mesh for editing, using a segmentation model to focus on these key regions. Given multi-view images of an object, we optimize a neural shader and a polygonal mesh while extracting the normal map and the rendered image from each view. Using these outputs as conditioning data, we edit the input images with a text-to-image diffusion model and iteratively update our dataset while deforming the mesh. This process results in a polygonal mesh that is edited according to the given text instruction, preserving the geometric characteristics of the initial mesh while focusing on the most significant areas. We evaluate our pipeline on the DTU dataset, demonstrating that it generates finely-edited meshes more rapidly than the current state-of-the-art methods. We include our code and additional results in the supplementary material.

External IDs:doi:10.1109/ojsp.2025.3627123