AI RESEARCH

SIC3D: Style Image Conditioned Text-to-3D Gaussian Splatting Generation

arXiv CS.CV

ArXi:2604.08760v1 Announce Type: new Recent progress in text-to-3D object generation enables the synthesis of detailed geometry from text input by leveraging 2D diffusion models and differentiable 3D representations. However, the approaches often suffer from limited controllability and texture ambiguity due to the limitation of the text modality. To address this, we present SIC3D, a controllable image-conditioned text-to-3D generation pipeline with 3D Gaussian Splatting (3DGS). There are two stages in SIC3D.