  Recent advances in 3D generation have been remarkable, with methods such as
DreamFusion leveraging large-scale text-to-image diffusion-based models to
supervise 3D object generation. These methods enable the synthesis of detailed
and photorealistic textured objects. However, the appearance of 3D objects
produced by these text-to-3D models is unpredictable, and it is hard for the
single-image-to-3D methods to deal with complex images, thus posing a challenge
in generating appearance-controllable 3D objects. To achieve controllable
complex 3D object synthesis, we propose IPDreamer, a novel approach that
incorporates image prompt adaption to extract detailed and comprehensive
appearance features from complex images, which are then utilized for 3D object
generation. Our results demonstrate that IPDreamer effectively generates
high-quality 3D objects that are consistent with both the provided text and the
appearance of complex image prompts, demonstrating its promising capability in
appearance-controllable 3D object generation. Our code is available at
https://github.com/zengbohan0217/IPDreamer.
