Traditional approaches of 3D content generation rely on generative models to produce RGB images, but these fall short when it comes to the nuances of physically-based rendering (PBR). This research explores a new solution proposed: direct modeling of the PBR image distribution. By sidestepping the pitfalls of converting RGB to PBR, this method promises more accurate results and smoother integration into modern graphics pipelines.

Cross-modal finetuning faces hurdles due to sparse data and the complexity of PBR output. However, the proposed method tackles these issues head-on by maintaining a frozen RGB model and establishing tight communication with a newly trained PBR model. This approach not only ensures stability during finetuning but also facilitates compatibility with various control methods, offering versatility in real-world applications.

Robust experimentation demonstrates its effectiveness in sparse data scenarios and compares it favorably against existing paradigms. Moreover, its plug-and-play compatibility with control methods like IPAdapter broadens its practical utility, promising a new era in 3D content generation where accuracy and flexibility converge seamlessly.