Optical coherence tomography angiography (OCTA) is a new imaging modality to
visualize retinal microvasculature and has been readily adopted in clinics.
High-resolution OCT angiograms are important to qualitatively and
quantitatively identify potential biomarkers for different retinal diseases
accurately. However, one significant problem of OCTA is the inevitable decrease
in resolution when increasing the field-of-view given a fixed acquisition time.
To address this issue, we propose a novel reference-based super-resolution
(RefSR) framework to preserve the resolution of the OCT angiograms while
increasing the scanning area. Specifically, textures from the normal RefSR
pipeline are used to train a learnable texture generator (LTG), which is
designed to generate textures according to the input. The key difference
between the proposed method and traditional RefSR models is that the textures
used during inference are generated by the LTG instead of being searched from a
single reference image. Since the LTG is optimized throughout the whole
training process, the available texture space is significantly enlarged and no
longer limited to a single reference image, but extends to all textures
contained in the training samples. Moreover, our proposed LTGNet does not
require a reference image at the inference phase, thereby becoming invulnerable
to the selection of the reference image. Both experimental and visual results
show that LTGNet has superior performance and robustness over state-of-the-art
methods, indicating good reliability and promise in real-life deployment. The
source code will be made available upon acceptance.

提出了一种基于参考的超分辨率（RefSR）框架，用于保留 OCT 血管造影图的分辨率并扩大扫描区域，通过使用训练出的纹理生成器生成纹理，提高了精度和稳健性，具有应用前景。

基于参考的 OCT 血管造影超分辨率图像重建与可学习群纹理生成

Reference-based OCT Angiogram Super-resolution with Learnable Texture  Generation

Generative Neural Radiance Field (GNeRF) models, which extract implicit 3D
representations from 2D images, have recently been shown to produce realistic
images representing rigid/semi-rigid objects, such as human faces or cars.
However, they usually struggle to generate high-quality images representing
non-rigid objects, such as the human body, which is of a great interest for
many computer graphics applications. This paper proposes a 3D-aware
Semantic-Guided Generative Model (3D-SGAN) for human image synthesis, which
combines a GNeRF with a texture generator. The former learns an implicit 3D
representation of the human body and outputs a set of 2D semantic segmentation
masks. The latter transforms these semantic masks into a real image, adding a
realistic texture to the human appearance. Without requiring additional 3D
information, our model can learn 3D human representations with a
photo-realistic, controllable generation. Our experiments on the DeepFashion
dataset show that 3D-SGAN significantly outperforms the most recent baselines.
The code is available at this https URL

提出了一种基于 3D-SGAN 的语义指导生成模型，利用生成神经辐射场（GNeRF）提取人体的隐式 3D 表示，并用纹理生成器将语义掩码转换为真实图像，为计算机图形学应用中的非刚性物体生成具有照片级真实感的图像。在 DeepFashion 数据集上，比最新的基准表现显著优秀。

人体综合的三维感知语义引导生成模型

3D-Aware Semantic-Guided Generative Model for Human Synthesis

Neural Cellular Automata (NCA) have shown a remarkable ability to learn the
required rules to "grow" images, classify morphologies, segment images, as well
as to do general computation such as path-finding. We believe the inductive
prior they introduce lends itself to the generation of textures. Textures in
the natural world are often generated by variants of locally interacting
reaction-diffusion systems. Human-made textures are likewise often generated in
a local manner (textile weaving, for instance) or using rules with local
dependencies (regular grids or geometric patterns). We demonstrate learning a
texture generator from a single template image, with the generation method
being embarrassingly parallel, exhibiting quick convergence and high fidelity
of output, and requiring only some minimal assumptions around the underlying
state manifold. Furthermore, we investigate properties of the learned models
that are both useful and interesting, such as non-stationary dynamics and an
inherent robustness to damage. Finally, we make qualitative claims that the
behaviour exhibited by the NCA model is a learned, distributed, local algorithm
to generate a texture, setting our method apart from existing work on texture
generation. We discuss the advantages of such a paradigm.

本文利用神经元细胞自动机（NCA）算法实现了基于单个模板图像的纹理生成器，其中神经元细胞自动机算法提供了一种学习分布式本地算法生成纹理的范例，并具有高保真度和鲁棒性。

神经元细胞自动机纹理生成

Texture Generation with Neural Cellular Automata

We present a new method for few-shot human motion transfer that achieves
realistic human image generation with only a small number of appearance inputs.
Despite recent advances in single person motion transfer, prior methods often
require a large number of training images and take long training time. One
promising direction is to perform few-shot human motion transfer, which only
needs a few of source images for appearance transfer. However, it is
particularly challenging to obtain satisfactory transfer results. In this
paper, we address this issue by rendering a human texture map to a surface
geometry (represented as a UV map), which is personalized to the source person.
Our geometry generator combines the shape information from source images, and
the pose information from 2D keypoints to synthesize the personalized UV map. A
texture generator then generates the texture map conditioned on the texture of
source images to fill out invisible parts. Furthermore, we may fine-tune the
texture map on the manifold of the texture generator from a few source images
at the test time, which improves the quality of the texture map without
over-fitting or artifacts. Extensive experiments show the proposed method
outperforms state-of-the-art methods both qualitatively and quantitatively. Our
code is available at this https URL

本文提出了用于少样本人体运动转移的新方法，使用少量的外观输入实现逼真的人物图像生成。该方法通过将人类纹理映射到表面几何（表示为 UV 贴图）上来解决这个问题，并生成个性化的 UV 贴图。