  This work aims to address the multi-view perspective RGB generation from text
prompts given Bird-Eye-View(BEV) semantics. Unlike prior methods that neglect
layout consistency, lack the ability to handle detailed text prompts, or are
incapable of generalizing to unseen view points, MVPbev simultaneously
generates cross-view consistent images of different perspective views with a
two-stage design, allowing object-level control and novel view generation at
test-time. Specifically, MVPbev firstly projects given BEV semantics to
perspective view with camera parameters, empowering the model to generalize to
unseen view points. Then we introduce a multi-view attention module where
special initialization and de-noising processes are introduced to explicitly
enforce local consistency among overlapping views w.r.t. cross-view homography.
Last but not least, MVPbev further allows test-time instance-level
controllability by refining a pre-trained text-to-image diffusion model. Our
extensive experiments on NuScenes demonstrate that our method is capable of
generating high-resolution photorealistic images from text descriptions with
thousands of training samples, surpassing the state-of-the-art methods under
various evaluation metrics. We further demonstrate the advances of our method
in terms of generalizability and controllability with the help of novel
evaluation metrics and comprehensive human analysis. Our code, data, and model
can be found in \url{https://github.com/kkaiwwana/MVPbev}.
