IPDreamer: Appearance-Controllable 3D Object Generation with Image Prompts
arXiv (Cornell University)(2023)
摘要
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 generation. These methods enable the synthesis of detailed and
photorealistic textured objects. However, the appearance of 3D objects produced
by these text-to-3D methods 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 introduce IPDreamer, a novel approach that
incorporates Image Prompts to provide specific and
comprehensive appearance information 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 avaliable at
https://github.com/zengbohan0217/IPDreamer.
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关键词
3d,generation,image
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