Neural Radiance Fields (NeRF) have achieved great success in the task of
synthesizing novel views that preserve the same resolution as the training
views. However, it is challenging for NeRF to synthesize high-quality
high-resolution novel views with low-resolution training data. To solve this
problem, we propose a zero-shot super-resolution training framework for NeRF.
This framework aims to guide the NeRF model to synthesize high-resolution novel
views via single-scene internal learning rather than requiring any external
high-resolution training data. Our approach consists of two stages. First, we
learn a scene-specific degradation mapping by performing internal learning on a
pretrained low-resolution coarse NeRF. Second, we optimize a super-resolution
fine NeRF by conducting inverse rendering with our mapping function so as to
backpropagate the gradients from low-resolution 2D space into the
super-resolution 3D sampling space. Then, we further introduce a temporal
ensemble strategy in the inference phase to compensate for the scene estimation
errors. Our method is featured on two points: (1) it does not consume
high-resolution views or additional scene data to train super-resolution NeRF;
(2) it can speed up the training process by adopting a coarse-to-fine strategy.
By conducting extensive experiments on public datasets, we have qualitatively
and quantitatively demonstrated the effectiveness of our method.

我们提出了一种 NeRF 的零样本超分辨率训练框架，通过单场景内部学习指导 NeRF 模型合成高分辨率的新颖视角，而无需外部高分辨率训练数据。我们的方法采用两个阶段，首先通过内部学习在预训练的低分辨率粗糙 NeRF 上学习场景特定的退化映射，然后通过反向渲染使用映射函数优化超分辨率细致 NeRF，并通过推理阶段引入时间集成策略来补偿场景估计错误。通过在公共数据集上进行大量实验，我们定性和定量地验证了我们方法的有效性。

ZS-SRT：神经辐射场的高效零样本超分辨率训练方法

ZS-SRT: An Efficient Zero-Shot Super-Resolution Training Method for  Neural Radiance Fields

Video inpainting is the task of filling a desired region in a video in a
visually convincing manner. It is a very challenging task due to the high
dimensionality of the signal and the temporal consistency required for
obtaining convincing results. Recently, diffusion models have shown impressive
results in modeling complex data distributions, including images and videos.
Diffusion models remain nonetheless very expensive to train and perform
inference with, which strongly restrict their application to video. We show
that in the case of video inpainting, thanks to the highly auto-similar nature
of videos, the training of a diffusion model can be restricted to the video to
inpaint and still produce very satisfying results. This leads us to adopt an
internal learning approch, which also allows for a greatly reduced network
size. We call our approach "Infusion": an internal learning algorithm for video
inpainting through diffusion. Due to our frugal network, we are able to propose
the first video inpainting approach based purely on diffusion. Other methods
require supporting elements such as optical flow estimation, which limits their
performance in the case of dynamic textures for example. We introduce a new
method for efficient training and inference of diffusion models in the context
of internal learning. We split the diffusion process into different learning
intervals which greatly simplifies the learning steps. We show qualititative
and quantitative results, demonstrating that our method reaches
state-of-the-art performance, in particular in the case of dynamic backgrounds
and textures.

视频修复方法基于扩散模型的内部学习算法，通过简化学习步骤并针对动态背景和纹理，在视频修复任务中达到了最先进的性能。

视频修复的内部扩散技术

Infusion: Internal Diffusion for Video Inpainting

Convolutional neural networks (CNNs) have shown dramatic improvements in
single image super-resolution (SISR) by using large-scale external samples.
Despite their remarkable performance based on the external dataset, they cannot
exploit internal information within a specific image. Another problem is that
they are applicable only to the specific condition of data that they are
supervised. For instance, the low-resolution (LR) image should be a "bicubic"
downsampled noise-free image from a high-resolution (HR) one. To address both
issues, zero-shot super-resolution (ZSSR) has been proposed for flexible
internal learning. However, they require thousands of gradient updates, i.e.,
long inference time. In this paper, we present Meta-Transfer Learning for
Zero-Shot Super-Resolution (MZSR), which leverages ZSSR. Precisely, it is based
on finding a generic initial parameter that is suitable for internal learning.
Thus, we can exploit both external and internal information, where one single
gradient update can yield quite considerable results. (See Figure 1). With our
method, the network can quickly adapt to a given image condition. In this
respect, our method can be applied to a large spectrum of image conditions
within a fast adaptation process.

本文提出了基于元传递学习的零样本超分辨率，利用泛化的初始参数适合内部学习，能够利用外部和内部信息来适应各种数据条件，从而可以大大减少推理时间。