IPDreamer: Appearance-Controllable 3D Object Generation with Complex Image Prompts
Bohan Zeng, Shanglin Li, Yutang Feng, Ling Yang, Juan Zhang, Hong Li, Jiaming Liu, Conghui He
Abstract
Recent advances in 3D generation have been remarkable, with methods such as DreamFusion leveraging large-scale text-to-image diffusion-based models to guide 3D object generation. These methods enable the synthesis of detailed and photorealistic textured objects. However, the appearance of 3D objects produced by such text-to-3D models is often unpredictable, and it is hard for single-image-to-3D methods to deal with images lacking a clear subject, complicating the generation of appearance-controllable 3D objects from complex images. To address these challenges, we present IPDreamer, a novel method that captures intricate appearance features from complex **I**mage **P**rompts and aligns the synthesized 3D object with these extracted features, enabling high-fidelity, appearance-controllable 3D object generation. Our experiments demonstrate that IPDreamer consistently generates high-quality 3D objects that align with both the textual and complex image prompts, highlighting its promising capability in appearance-controlled, complex 3D object generation.
BibTeX
@inproceedings{
zeng2025ipdreamer,
title={{IPD}reamer: Appearance-Controllable 3D Object Generation with Complex Image Prompts},
author={Bohan Zeng and Shanglin Li and Yutang Feng and Ling Yang and Juan Zhang and Hong Li and Jiaming Liu and Conghui He and Wentao Zhang and Jianzhuang Liu and Baochang Zhang and Shuicheng YAN},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=3PguviI7Uf}
}