Generative Adversarial Networks (GANs) have been widely used to recover vivid
textures in image super-resolution (SR) tasks. In particular, one discriminator
is utilized to enable the SR network to learn the distribution of real-world
high-quality images in an adversarial training manner. However, the
distribution learning is overly coarse-grained, which is susceptible to virtual
textures and causes counter-intuitive generation results. To mitigate this, we
propose the simple and effective Semantic-aware Discriminator (denoted as SeD),
which encourages the SR network to learn the fine-grained distributions by
introducing the semantics of images as a condition. Concretely, we aim to
excavate the semantics of images from a well-trained semantic extractor. Under
different semantics, the discriminator is able to distinguish the real-fake
images individually and adaptively, which guides the SR network to learn the
more fine-grained semantic-aware textures. To obtain accurate and abundant
semantics, we take full advantage of recently popular pretrained vision models
(PVMs) with extensive datasets, and then incorporate its semantic features into
the discriminator through a well-designed spatial cross-attention module. In
this way, our proposed semantic-aware discriminator empowered the SR network to
produce more photo-realistic and pleasing images. Extensive experiments on two
typical tasks, i.e., SR and Real SR have demonstrated the effectiveness of our
proposed methods.

我们提出了一种简单而有效的语义感知鉴别器，通过引入图像的语义作为条件，鉴别器能够个别且自适应地区分真实 - 伪造图像，从而引导超分辨网络学习细粒度的语义感知纹理，进而生成更照片般真实和令人愉悦的图像。

SeD: 图像超分辨率的语义感知鉴别器

SeD: Semantic-Aware Discriminator for Image Super-Resolution

The 3D CAD shapes in current 3D benchmarks are mostly collected from online
model repositories. Thus, they typically have insufficient geometric details
and less informative textures, making them less attractive for comprehensive
and subtle research in areas such as high-quality 3D mesh and texture recovery.
This paper presents 3D Furniture shape with TextURE (3D-FUTURE): a
richly-annotated and large-scale repository of 3D furniture shapes in the
household scenario. At the time of this technical report, 3D-FUTURE contains
20,240 clean and realistic synthetic images of 5,000 different rooms. There are
9,992 unique detailed 3D instances of furniture with high-resolution textures.
Experienced designers developed the room scenes, and the 3D CAD shapes in the
scene are used for industrial production. Given the well-organized 3D-FUTURE,
we provide baseline experiments on several widely studied tasks, such as joint
2D instance segmentation and 3D object pose estimation, image-based 3D shape
retrieval, 3D object reconstruction from a single image, and texture recovery
for 3D shapes, to facilitate related future researches on our database.

本文介绍了 3D-FUTURE：一个丰富注释和大规模的 3D 家具形状库，提供了几项基准实验以便相关研究在我们的数据库上进行。

3D-FUTURE：3D 家具外形及纹理

3D-FUTURE: 3D Furniture shape with TextURE

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。