  In this paper, we introduce Era3D, a novel multiview diffusion method that
generates high-resolution multiview images from a single-view image. Despite
significant advancements in multiview generation, existing methods still suffer
from camera prior mismatch, inefficacy, and low resolution, resulting in
poor-quality multiview images. Specifically, these methods assume that the
input images should comply with a predefined camera type, e.g. a perspective
camera with a fixed focal length, leading to distorted shapes when the
assumption fails. Moreover, the full-image or dense multiview attention they
employ leads to an exponential explosion of computational complexity as image
resolution increases, resulting in prohibitively expensive training costs. To
bridge the gap between assumption and reality, Era3D first proposes a
diffusion-based camera prediction module to estimate the focal length and
elevation of the input image, which allows our method to generate images
without shape distortions. Furthermore, a simple but efficient attention layer,
named row-wise attention, is used to enforce epipolar priors in the multiview
diffusion, facilitating efficient cross-view information fusion. Consequently,
compared with state-of-the-art methods, Era3D generates high-quality multiview
images with up to a 512*512 resolution while reducing computation complexity by
12x times. Comprehensive experiments demonstrate that Era3D can reconstruct
high-quality and detailed 3D meshes from diverse single-view input images,
significantly outperforming baseline multiview diffusion methods. Project page:
https://penghtyx.github.io/Era3D/.
