IPDreamer: Appearance-Controllable 3D Object Generation with Complex Image Prompts

• URL: http://arxiv.org/abs/2310.05375v6
• Date: Tue, 22 Oct 2024 09:52:42 GMT
• Title: IPDreamer: Appearance-Controllable 3D Object Generation with Complex Image Prompts
• Authors: Bohan Zeng, Shanglin Li, Yutang Feng, Ling Yang, Hong Li, Sicheng Gao, Jiaming Liu, Conghui He, Wentao Zhang, Jianzhuang Liu, Baochang Zhang, Shuicheng Yan,
• Abstract summary: We present IPDreamer, a novel method that captures intricate appearance features from complex $textbfI$mage $textbfP$rompts and aligns the synthesized 3D object with these extracted features. Our experiments demonstrate that IPDreamer consistently generates high-quality 3D objects that align with both the textual and complex image prompts.
• Score: 90.49024750432139
• License:
• 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 $\textbf{I}$mage $\textbf{P}$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. Our code is available at https://github.com/zengbohan0217/IPDreamer.

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