Neural Radiance Field (NeRF) is a framework that represents a 3D scene in the
weights of a fully connected neural network, known as the Multi-Layer
Perception(MLP). The method was introduced for the task of novel view synthesis
and is able to achieve state-of-the-art photorealistic image renderings from a
given continuous viewpoint. NeRFs have become a popular field of research as
recent developments have been made that expand the performance and capabilities
of the base framework. Recent developments include methods that require less
images to train the model for view synthesis as well as methods that are able
to generate views from unconstrained and dynamic scene representations.

Neural Radiance Field (NeRF) is a framework representing a 3D scene with MLP, achieving state-of-the-art photorealistic image renderings for view synthesis and expanding capabilities to generate views from dynamic scene representations with fewer images for training.

神经辐射场（NeRFs）：综述与一些最近的发展

Neural Radiance Fields (NeRFs): A Review and Some Recent Developments

Generating a photorealistic image with intended human pose is a promising yet
challenging research topic for many applications such as smart photo editing,
movie making, virtual try-on, and fashion display. In this paper, we present a
novel deep generative model to transfer an image of a person from a given pose
to a new pose while keeping fashion item consistent. In order to formulate the
framework, we employ one generator and two discriminators for image synthesis.
The generator includes an image encoder, a pose encoder and a decoder. The two
encoders provide good representation of visual and geometrical context which
will be utilized by the decoder in order to generate a photorealistic image.
Unlike existing pose-guided image generation models, we exploit two
discriminators to guide the synthesis process where one discriminator
differentiates between generated image and real images (training samples), and
another discriminator verifies the consistency of appearance between a target
pose and a generated image. We perform end-to-end training of the network to
learn the parameters through back-propagation given ground-truth images. The
proposed generative model is capable of synthesizing a photorealistic image of
a person given a target pose. We have demonstrated our results by conducting
rigorous experiments on two data sets, both quantitatively and qualitatively.

本文提出了一种基于新颖的深度生成模型的图像迁移方法，可以在保持服装一致的情况下将一个人的图像从一个给定的姿势转移至一个新的姿势，使用图像编码器、姿势编码器和解码器的结构，同时利用两个鉴别器来指导产生过程。经过严格的实验，在两个数据集上定量和定性地验证了该方法的效果。

使用深度生成模型进行姿势引导的时尚图像合成

Pose Guided Fashion Image Synthesis Using Deep Generative Model

We present a novel approach for the task of human pose transfer, which aims
at synthesizing a new image of a person from an input image of that person and
a target pose. We address the issues of limited correspondences identified
between keypoints only and invisible pixels due to self-occlusion. Unlike
existing methods, we propose to estimate dense and intrinsic 3D appearance flow
to better guide the transfer of pixels between poses. In particular, we wish to
generate the 3D flow from just the reference and target poses. Training a
network for this purpose is non-trivial, especially when the annotations for 3D
appearance flow are scarce by nature. We address this problem through a flow
synthesis stage. This is achieved by fitting a 3D model to the given pose pair
and project them back to the 2D plane to compute the dense appearance flow for
training. The synthesized ground-truths are then used to train a feedforward
network for efficient mapping from the input and target skeleton poses to the
3D appearance flow. With the appearance flow, we perform feature warping on the
input image and generate a photorealistic image of the target pose. Extensive
results on DeepFashion and Market-1501 datasets demonstrate the effectiveness
of our approach over existing methods. Our code is available at
this http URL

该研究提出了一种人体姿势转换的新方法，通过估计稠密和内部的 3D 外观流来更好地指导姿态之间像素的转移，并通过该方法生成了真实的目标姿态图像。

稠密内在外观流用于人体姿态转移

Dense Intrinsic Appearance Flow for Human Pose Transfer

Recent work has shown impressive success in transferring painterly style to
images. These approaches, however, fall short of photorealistic style transfer.
Even when both the input and reference images are photographs, the output still
exhibits distortions reminiscent of a painting. In this paper we propose an
approach that takes as input a stylized image and makes it more photorealistic.
It relies on the Screened Poisson Equation, maintaining the fidelity of the
stylized image while constraining the gradients to those of the original input
image. Our method is fast, simple, fully automatic and shows positive progress
in making a stylized image photorealistic. Our results exhibit finer details
and are less prone to artifacts than the state-of-the-art.

该论文提出了一种使用屏蔽泊松方程约束图像梯度，以实现样式迁移中输入图像转换为逼真照片的快速解决方法，结果表现出更精细的细节和更少的伪影比目前的艺术水平技术要好。