  In the evolving landscape of text-to-3D technology, Dreamfusion has showcased
its proficiency by utilizing Score Distillation Sampling (SDS) to optimize
implicit representations such as NeRF. This process is achieved through the
distillation of pretrained large-scale text-to-image diffusion models. However,
Dreamfusion encounters fidelity and efficiency constraints: it faces the
multi-head Janus issue and exhibits a relatively slow optimization process. To
circumvent these challenges, we introduce OrientDream, a camera orientation
conditioned framework designed for efficient and multi-view consistent 3D
generation from textual prompts. Our strategy emphasizes the implementation of
an explicit camera orientation conditioned feature in the pre-training of a 2D
text-to-image diffusion module. This feature effectively utilizes data from
MVImgNet, an extensive external multi-view dataset, to refine and bolster its
functionality. Subsequently, we utilize the pre-conditioned 2D images as a
basis for optimizing a randomly initialized implicit representation (NeRF).
This process is significantly expedited by a decoupled back-propagation
technique, allowing for multiple updates of implicit parameters per
optimization cycle. Our experiments reveal that our method not only produces
high-quality NeRF models with consistent multi-view properties but also
achieves an optimization speed significantly greater than existing methods, as
quantified by comparative metrics.
