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.

Era3D 是一种多视角扩散方法，通过单视角图像生成高分辨率的多视角图像。它提出了扩散式的摄像机预测模块以准确估计输入图像的焦距和仰角，使用行向注意力层来加强多视角扩散中的极线先验，实现高质量的多视角图像生成和具体的 3D 网格重建。

Era3D：高分辨率多视角扩散利用高效分行注意力

Era3D: High-Resolution Multiview Diffusion using Efficient Row-wise  Attention

We present a learning framework that learns to recover the 3D shape, pose and
texture from a single image, trained on an image collection without any ground
truth 3D shape, multi-view, camera viewpoints or keypoint supervision. We
approach this highly under-constrained problem in a "analysis by synthesis"
framework where the goal is to predict the likely shape, texture and camera
viewpoint that could produce the image with various learned category-specific
priors. Our particular contribution in this paper is a representation of the
distribution over cameras, which we call "camera-multiplex". Instead of picking
a point estimate, we maintain a set of camera hypotheses that are optimized
during training to best explain the image given the current shape and texture.
We call our approach Unsupervised Category-Specific Mesh Reconstruction
(U-CMR), and present qualitative and quantitative results on CUB, Pascal 3D and
new web-scraped datasets. We obtain state-of-the-art camera prediction results
and show that we can learn to predict diverse shapes and textures across
objects using an image collection without any keypoint annotations or 3D ground
truth. Project page: this https URL

本文提出了一个无监督类别特定网格重构学习框架，可以从一组没有 3D 地面真实值的图像中恢复出物体的 3D 形状、姿态和纹理，并维护了一个优化的摄像机假设集以更好地解释给定形状和纹理的图像，并在 CUB、Pascal 3D 和新的网页抓取数据集上获得了最先进的摄像机预测结果。