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
this https URL

Text-to-3D 生成已经取得了显著的进展，但当需要在指定视点自定义特定外观时，仅仅依靠整体描述来生成 3D 对象时会出现挑战。在这个工作中，我们提出了 DreamView，一种文本到图像的方法，通过协作的文本指导注入模块灵活地注入视点特定和整体文本指导，从而实现多视图自定义同时保持整体一致性，在每个视图中平衡分离的内容操作和整体对象的全局一致性，从而实现自定义和一致性的双重成就。因此，DreamView 使艺术家能够创造性地设计 3D 对象，促进更具创新性和多样性的 3D 资产的创作。