你为什么要提出这个方法？一般说过去的方法存在什么问题
通常采用如下方式
- 介绍背景，阐述问题，然后我们提出了一个新方法来解决问题。
- 什么方法对TaskA有效，我们想应用到TaskB。
- （开门见山）我们提出了一个新方法，为什么要提出呢？
| xx问题正面临一个困境，要么<质量好>，要么<可编辑性强>，我们提出的方法正好能同时实现。 |
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| Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution, or high quality ones with no editing flexibility. |
| In this work, we propose a new approach that brings the best of both worlds together. |
| 先说xx是最近一个大红大热的方向，简要写一下它的原理（是做什么），它的好处；然而，现有方法还存在一些问题；为了解决它，我们提出了… |
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| xx is a promising new avenue of …, xx ensure many advantages over …; However, the current approach is dufficult to …; To address this issue, we propose … |
| Recently, xx has achieved great empirical success, especially …; However, a key problem of xx is that …; It's necessary to … |
| 3D-aware image synthesis aims to generate images of objects from multiple views by learning a 3D representation. However, one key challenge remains: existing approaches lack geometry constraints, hence usually fail to generate multi-view consistent images. |
| We are witnessing an explosion of neural implicit representations in computer vision and graphics. Their applicability has recently expanded beyond tasks such as xx. However, existing methods … We introduce a new method that enables xx. |
| Despite the rapid advancement of semantic discovery in the latent space of GANs, existing approaches either are limited to finding global attributes or … |
| 某技术或者领域在实现TaskA上已经做得很好了，但TaskB还不能很好解决 |
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| While generating realistic images is no longer a difficult task, producing the corresponding 3D structure such that they can be rendered from different views is non-trivial. |
| Neural Radiance Fields (NeRF) have recently demonstrated photo- realistic results for the task of novel view synthesis. In this paper, we propose to apply novel view synthesis to the robot relocalization problem: [details] |
| However, such advancements have not been fully migrated to the community of xx. |
| 第一句直接说我们的名称，做了什么 |
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| We present a method for learning a generative 3D model based on nerf, trained solely from data with only single views of each object. |
| 我们在最近一些比较好的技术基础上，做了什么 |
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| Recent advances in Neural Radiance Fields (NeRFs) treat the problem of novel view synthesis as Sparse Radiance Field (SRF) optimization using sparse voxels for efficient and fast rendering. In order to leverage xx, we present xx. |
描述你的方法
| 前面描述存在什么问题，在这里我们希望解决他 |
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| To this end, this paper proposes xx with three distinct novelties. |
| With the proposed framework, we discover that existing works usually utilize xx, and we further develop two novel objective functions considering xx showing [ad1] or [ad2] capabilities respectively. |
| 直接说我们提出了什么算法，它有什么用；它的核心是什么 |
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| In this work, we present/ xxx algorithm, which can xx |
| A key part of our approach is to xx |
| The key to our approach is to utilize xx |
| Moreover, we provide the convergence proofs and expressive power comparisons for the proposed models. |
| 拆分成一个个部分的写法 |
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| Our system consists of three major components: (1) a xx model that...; (2) a xx approach that...; (3) a xx |
| Specifically, our model/system first adapts. Then, we Finally, |
| 补充一句，我们的方法没有牺牲之前好的性质，反正增加了什么 |
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| we show that our method does not sacrifice the, improving the xx |
描述你的实验结果多好多好，有点卖弄 (show off) 的意味在里面。
| 夸就完事了 |
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| Both quantitative and qualitative results show that our method reaches the state-of-the-art in terms of photorealism, faithfulness and efficiency. |
| We show promising result on xx |
| Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art. |
| Experiments show that our proposed framework significantly outperforms SOTA methods on xx dataset |
| In contrast to existing works on neural 3D scene representation learning, this paper approaches the problem from a new perspective |
| Extensive experiments demonstrate that our framework can achieve ideal editing results not only on synthetic data, but also on realscenes captured by users. |
| We empirically demonstrate that xx achieve a much smaller loss than xx. |
| Experiments on xx tasks conducted on a range of datasets substantiate the generalizability of our [model] as well as its substantial improvement over the baselines. |
| 说在附录里也放了点东西 |
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| Finally, we supplement our empirical results with a careful analysis of each component of xx. |
While NeRF has shown great success for neural reconstruction and rendering, its limited MLP capacity and long per-scene optimization times make it challenging to model large-scale indoor scenes. In contrast, classical 3D reconstruction methods can handle large-scale scenes but do not produce realistic renderings. We propose NeRFusion, a method that combines the advantages of NeRF and TSDF-based fusion techniques to achieve efficient large-scale reconstruction and photo-realistic rendering.
先说一个新技术很成功，但依旧存在一些问题；(in contrast) 传统方法可以解决这些问题，但也有一些问题；因此提出一个新方法，融合两种，实现了xxx
This work aims to integrate two learning paradigms MTL and Meta Learning