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With the help of Score Distillation Sampling(SDS) and the rapid developmentof various trainable 3D representations, Text-to-Image(T2I) diffusion modelshave been applied to 3D generation tasks and achieved considerable results.There are also some attempts toward the task of editing 3D objects leveragingthis Text-to-3D pipeline. However, most methods currently focus on addingadditional geometries, overwriting textures or both. But few of them canperform non-rigid transformation of 3D objects. For those who can performnon-rigid editing, on the other hand, suffer from low-resolution, lack offidelity and poor flexibility. In order to address these issues, we present:Plasticine3D, a general, high-fidelity, photo-realistic and controllablenon-rigid editing pipeline. Firstly, our work divides the editing process intoa geometry editing stage and a texture editing stage to achieve more detailedand photo-realistic results ; Secondly, in order to perform non-rigidtransformation with controllable results while maintain the fidelity towardsoriginal 3D models in the same time, we propose a multi-view-embedding(MVE)optimization strategy to ensure that the diffusion model learns the overallfeatures of the original object and an embedding-fusion(EF) to control thedegree of editing by adjusting the value of the fusing rate. We also design ageometry processing step before optimizing on the base geometry to cope withdifferent needs of various editing tasks. Further more, to fully leverage thegeometric prior from the original 3D object, we provide an optional replacementof score distillation sampling named score projection sampling(SPS) whichenables us to directly perform optimization from the origin 3D mesh in mostcommon median non-rigid editing scenarios. We demonstrate the effectiveness ofour method on both the non-rigid 3D editing task and general 3D editing task.

Plasticine3D: Non-rigid 3D editting with text guidance