  Recent advancements in text-to-3D generation have significantly contributed
to the automation and democratization of 3D content creation. Building upon
these developments, we aim to address the limitations of current methods in
generating 3D models with creative geometry and styles. We introduce multi-view
ControlNet, a novel depth-aware multi-view diffusion model trained on generated
datasets from a carefully curated text corpus. Our multi-view ControlNet is
then integrated into our two-stage pipeline, ControlDreamer, enabling
text-guided generation of stylized 3D models. Additionally, we present a
comprehensive benchmark for 3D style editing, encompassing a broad range of
subjects, including objects, animals, and characters, to further facilitate
research on diverse 3D generation. Our comparative analysis reveals that this
new pipeline outperforms existing text-to-3D methods as evidenced by human
evaluations and CLIP score metrics.
