SK-Adapter: Skeleton-Based Structural Control for Native 3D Generation
作者: Anbang Wang, AO Yuzhuo, Elliott (Shangzhe) Wu, Chi-Keung Tang
摘要
Native 3D generative models have achieved remarkable _x001C_-delity and speed, yet they su_x001B_er from a critical limitation: inability toprescribe precise structural articulations, where precise structural con-trol within the native 3D space remains underexplored. This paper pro-poses SK-Adapter, a simple yet e_x001E_cient and e_x001B_ective framework thatunlocks precise skeletal manipulation for native 3D generation. Mov-ing beyond text or image prompts, which can be ambiguous for precisestructure, we treat the 3D skeleton as a _x001C_rst-class control signal. SK-Adapter is a lightweight structural adapter network that encodes jointcoordinates and topology into learnable tokens, which are injected intothe frozen 3D generation backbone via cross-attention. This design al-lows the model to not only e_x001B_ectively _x0010_attend_x0011_ to speci_x001C_c 3D structuralconstraints but also preserve its original generative priors. To bridgethe data gap, we contribute the Objaverse-TMS dataset, a large-scale⋆ Equal contribution. Equal advising.dataset of 24k text-mesh-skeleton pairs. Extensive experiments con_x001C_rmthat our method achieves robust structural control while preserving thegeometry and texture quality of the foundation model, signi_x001C_cantly out-performing existing baselines. Furthermore, we extend this capability tolocal 3D editing, enabling region-speci_x001C_c editing of existing assets withskeletal guidance, which is unattainable by previous methods. Projectpage: https://sk-adapter.github.io/