DreamView: Injecting View-specific Text Guidance into Text-to-3D
  Generation
Junkai Yan Yipeng Gao Qize Yang Xihan Wei Xuansong Xie Ancong Wu Wei-Shi Zheng
Text-to-3D generation, which synthesizes 3D assets according to an overall
text description, has significantly progressed. However, a challenge arises
when the specific appearances need customizing at designated viewpoints but
referring solely to the overall description for generating 3D objects. For
instance, ambiguity easily occurs when producing a T-shirt with distinct
patterns on its front and back using a single overall text guidance. In this
work, we propose DreamView, a text-to-image approach enabling multi-view
customization while maintaining overall consistency by adaptively injecting the
view-specific and overall text guidance through a collaborative text guidance
injection module, which can also be lifted to 3D generation via score
distillation sampling. DreamView is trained with large-scale rendered
multi-view images and their corresponding view-specific texts to learn to
balance the separate content manipulation in each view and the global
consistency of the overall object, resulting in a dual achievement of
customization and consistency. Consequently, DreamView empowers artists to
design 3D objects creatively, fostering the creation of more innovative and
diverse 3D assets. Code and model will be released at
https://github.com/iSEE-Laboratory/DreamView.