diff --git a/docs/install.md b/docs/install.md
index de227359bc..37afe5d399 100644
--- a/docs/install.md
+++ b/docs/install.md
@@ -1,16 +1,18 @@
-## Installation
+# Installation
 
 <!-- TOC -->
 
 - [Requirements](#requirements)
+- [Prepare Environment](#prepare-environment)
 - [Install MMPose](#install-mmpose)
+- [Install with CPU only](#install-with-cpu-only)
 - [A from-scratch setup script](#a-from-scratch-setup-script)
 - [Another option: Docker Image](#another-option-docker-image)
 - [Developing with multiple MMPose versions](#developing-with-multiple-mmpose-versions)
 
 <!-- TOC -->
 
-### Requirements
+## Requirements
 
 - Linux (Windows is not officially supported)
 - Python 3.6+
@@ -30,7 +32,7 @@ Optional:
 - [pyrender](https://pyrender.readthedocs.io/en/latest/install/index.html) (to run 3d mesh demos)
 - [smplx](https://github.com/vchoutas/smplx) (to run 3d mesh demos)
 
-### Install MMPose
+## Prepare environment
 
 a. Create a conda virtual environment and activate it.
 
@@ -48,35 +50,36 @@ conda install pytorch torchvision -c pytorch
 Note: Make sure that your compilation CUDA version and runtime CUDA version match.
 You can check the supported CUDA version for precompiled packages on the [PyTorch website](https://pytorch.org/).
 
-`E.g.1` If you have CUDA 10.1 installed under `/usr/local/cuda` and would like to install PyTorch 1.5,
-you need to install the prebuilt PyTorch with CUDA 10.1.
+`E.g.1` If you have CUDA 10.2 installed under `/usr/local/cuda` and would like to install PyTorch 1.8.0,
+you need to install the prebuilt PyTorch with CUDA 10.2.
 
 ```shell
-conda install pytorch cudatoolkit=10.1 torchvision -c pytorch
+conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.2 -c pytorch
 ```
 
-`E.g.2` If you have CUDA 9.2 installed under `/usr/local/cuda` and would like to install PyTorch 1.3.1.,
+`E.g.2` If you have CUDA 9.2 installed under `/usr/local/cuda` and would like to install PyTorch 1.7.0.,
 you need to install the prebuilt PyTorch with CUDA 9.2.
 
 ```shell
-conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch
+conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=9.2 -c pytorch
 ```
 
-If you build PyTorch from source instead of installing the prebuilt package, you can use more CUDA versions such as 9.0.
+If you build PyTorch from source instead of installing the pre-built package, you can use more CUDA versions such as 9.0.
 
-c. Install mmcv, we recommend you to install the pre-build mmcv as below.
+## Install MMPose
+
+a. Install mmcv, we recommend you to install the pre-built mmcv as below.
 
 ```shell
 pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
 ```
 
-Please replace ``{cu_version}`` and ``{torch_version}`` in the url to your desired one. For example, to install the latest ``mmcv-full`` with ``CUDA 11`` and ``PyTorch 1.7.0``, use the following command:
+Please replace ``{cu_version}`` and ``{torch_version}`` in the url to your desired one. For example, to install the latest ``mmcv-full`` with ``CUDA 10.2`` and ``PyTorch 1.8.0``, use the following command:
 
 ```shell
-pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html
 ```
 
-If it compiles during installation, then please check that the cuda version and pytorch version **exactly"" matches the version in the mmcv-full installation command. For example, pytorch 1.7.0 and 1.7.1 are treated differently.
 See [here](https://github.com/open-mmlab/mmcv#installation) for different versions of MMCV compatible to different PyTorch and CUDA versions.
 
 Optionally you can choose to compile mmcv from source by the following command
@@ -89,32 +92,38 @@ MMCV_WITH_OPS=1 pip install -e .  # package mmcv-full, which contains cuda ops,
 cd ..
 ```
 
-Or directly run
-
-```shell
-pip install mmcv-full
-# alternative: pip install mmcv
-```
-
 **Important:** You need to run `pip uninstall mmcv` first if you have mmcv installed. If mmcv and mmcv-full are both installed, there will be `ModuleNotFoundError`.
 
-d. Clone the mmpose repository
+b. Clone the mmpose repository
 
 ```shell
 git clone git@github.com:open-mmlab/mmpose.git
 cd mmpose
 ```
 
-e. Install build requirements and then install mmpose
+c. Install build requirements and then install mmpose
 
 ```shell
 pip install -r requirements.txt
-python setup.py develop
+pip install -v -e .  # or "python setup.py develop"
 ```
 
+If you build MMPose on macOS, replace the last command with
+
+```shell
+CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e .
+```
+
+d. Install optional modules
+
+- [mmdet](https://github.com/open-mmlab/mmdetection) (to run pose demos)
+- [mmtrack](https://github.com/open-mmlab/mmtracking) (to run pose tracking demos)
+- [pyrender](https://pyrender.readthedocs.io/en/latest/install/index.html) (to run 3d mesh demos)
+- [smplx](https://github.com/vchoutas/smplx) (to run 3d mesh demos)
+
 Note:
 
-1. The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models.
+1. The git commit id will be written to the version number with step c, e.g. 0.6.0+2e7045c. The version will also be saved in trained models.
    It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.
 
 1. Following the above instructions, mmpose is installed on `dev` mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).
@@ -124,7 +133,18 @@ Note:
 
 1. If you have `mmcv` installed, you need to firstly uninstall `mmcv`, and then install `mmcv-full`.
 
-### A from-scratch setup script
+1. Some dependencies are optional. Running `python setup.py develop` will only install the minimum runtime requirements.
+   To use optional dependencies like `smplx`, either install them with `pip install -r requirements/optional.txt`
+   or specify desired extras when calling `pip` (e.g. `pip install -v -e .[optional]`,
+   valid keys for the `[optional]` field are `all`, `tests`, `build`, and `optional`) like `pip install -v -e .[tests,build]`.
+
+## Install with CPU only
+
+The code can be built for CPU only environment (where CUDA isn't available).
+
+In CPU mode you can run the demo/demo.py for example.
+
+## A from-scratch setup script
 
 Here is a full script for setting up mmpose with conda and link the dataset path (supposing that your COCO dataset path is $COCO_ROOT).
 
@@ -135,8 +155,10 @@ conda activate open-mmlab
 # install latest pytorch prebuilt with the default prebuilt CUDA version (usually the latest)
 conda install -c pytorch pytorch torchvision -y
 
-# install the latest mmcv-full or mmcv, here we take mmcv-full as example
-pip install mmcv-full
+# install the latest mmcv-full
+# Please replace ``{cu_version}`` and ``{torch_version}`` in the url to your desired one.
+# See [here](https://github.com/open-mmlab/mmcv#installation) for different versions of MMCV compatible to different PyTorch and CUDA versions.
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
 
 # install mmpose
 git clone git@github.com:open-mmlab/mmpose.git
@@ -144,9 +166,11 @@ cd mmpose
 pip install -r requirements.txt
 python setup.py develop
 
+mkdir data
+ln -s $COCO_ROOT data/coco
 ```
 
-### Another option: Docker Image
+## Another option: Docker Image
 
 We provide a [Dockerfile](/docker/Dockerfile) to build an image.
 
@@ -165,7 +189,7 @@ docker run --gpus all\
  -it -v {DATA_DIR}:/mmpose/data mmpose
 ```
 
-### Developing with multiple MMPose versions
+## Developing with multiple MMPose versions
 
 The train and test scripts already modify the `PYTHONPATH` to ensure the script use the MMPose in the current directory.
 
diff --git a/docs_zh-CN/install.md b/docs_zh-CN/install.md
index 9b7824adcf..5048c47c72 100644
--- a/docs_zh-CN/install.md
+++ b/docs_zh-CN/install.md
@@ -1,3 +1,204 @@
-## 安装
+# 安装
 
-内容建设中……
+本文档提供了安装 MMPose 的相关步骤。
+
+<!-- TOC -->
+
+- [安装依赖包](#安装依赖包)
+- [准备环境](#准备环境)
+- [MMPose 的安装步骤](#MMPose-的安装步骤)
+- [CPU 环境下的安装步骤](#CPU-环境下的安装步骤)
+- [利用 Docker 镜像安装 MMPose](#利用-Docker-镜像安装-MMPose)
+- [源码安装 MMPose](#源码安装-MMPose)
+- [在多个 MMPose 版本下进行开发](#在多个-MMPose-版本下进行开发)
+
+<!-- TOC -->
+
+## 安装依赖包
+
+- Linux (Windows 系统暂未有官方支持)
+- Python 3.6+
+- PyTorch 1.3+
+- CUDA 9.2+ (如果从源码编译 PyTorch,则可以兼容 CUDA 9.0 版本)
+- GCC 5+
+- [mmcv](https://github.com/open-mmlab/mmcv) 请安装最新版本的 mmcv-full
+- Numpy
+- cv2
+- json_tricks
+- [xtcocotools](https://github.com/jin-s13/xtcocoapi)
+
+可选项：
+
+- [mmdet](https://github.com/open-mmlab/mmdetection) (用于“姿态估计”)
+- [mmtrack](https://github.com/open-mmlab/mmtracking) (用于“姿态跟踪”)
+- [pyrender](https://pyrender.readthedocs.io/en/latest/install/index.html) (用于“三维人体形状恢复”)
+- [smplx](https://github.com/vchoutas/smplx) (用于“三维人体形状恢复”)
+
+## 准备环境
+
+a. 创建并激活 conda 虚拟环境，如：
+
+```shell
+conda create -n open-mmlab python=3.7 -y
+conda activate open-mmlab
+```
+
+b. 参考 [官方文档](https://pytorch.org/) 安装 PyTorch 和 torchvision ，如：
+
+```shell
+conda install pytorch torchvision -c pytorch
+```
+
+**注**：确保 CUDA 的编译版本和 CUDA 的运行版本相匹配。
+用户可以参照 [PyTorch 官网](https://pytorch.org/) 对预编译包所支持的 CUDA 版本进行核对。
+
+`例 1`：如果用户的 `/usr/local/cuda` 文件夹下已安装 CUDA 10.2 版本，并且想要安装 PyTorch 1.8.0 版本，
+则需要安装 CUDA 10.2 下预编译的 PyTorch。
+
+```shell
+conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.2 -c pytorch
+```
+
+`例 2`：如果用户的 `/usr/local/cuda` 文件夹下已安装 CUDA 9.2 版本，并且想要安装 PyTorch 1.7.0 版本，
+则需要安装 CUDA 9.2 下预编译的 PyTorch。
+
+```shell
+conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=9.2 -c pytorch
+```
+
+如果 PyTorch 是由源码进行编译安装（而非直接下载预编译好的安装包），则可以使用更多的 CUDA 版本（如 9.0 版本）。
+
+## MMPose 的安装步骤
+
+a. 安装最新版本的 mmcv-full。MMPose 推荐用户使用如下的命令安装预编译好的 mmcv。
+
+```shell
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
+```
+
+其中，命令里 url 的 ``{cu_version}`` 和 ``{torch_version}`` 变量需由用户进行指定。
+例如，如果想要安装 ``CUDA 10.2`` 和 ``PyTorch 1.8.0`` 下的最新版 ``mmcv-full``，可使用以下命令：
+
+```shell
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html
+```
+
+可查阅 [这里](https://github.com/open-mmlab/mmcv#installation) 以参考不同版本的 MMCV 所兼容的 PyTorch 和 CUDA 版本。
+
+另外，用户也可以通过使用以下命令从源码进行编译：
+
+```shell
+git clone https://github.com/open-mmlab/mmcv.git
+cd mmcv
+MMCV_WITH_OPS=1 pip install -e .  # mmcv-full 包含一些 cuda 算子，执行该步骤会安装 mmcv-full（而非 mmcv）
+# 或者使用 pip install -e .  # 这个命令安装的 mmcv 将不包含 cuda ops，通常适配 CPU（无 GPU）环境
+cd ..
+```
+
+**注意**：如果之前安装过 mmcv，那么需要先使用 `pip uninstall mmcv` 命令进行卸载。如果 mmcv 和 mmcv-full 同时被安装, 会报 `ModuleNotFoundError` 的错误。
+
+b. 克隆 MMPose 库。
+
+```shell
+git clone https://github.com/open-mmlab/mmpose.git
+cd mmpose
+```
+
+c. 安装依赖包和 MMPose。
+
+```shell
+pip install -r requirements.txt
+pip install -v -e .  # or "python setup.py develop"
+```
+
+如果是在 macOS 环境安装 MMPose，则需使用如下命令：
+
+```shell
+CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e .
+```
+
+d. 安装其他可选依赖。
+
+如果用户不需要做相关任务，这部分步骤可以选择跳过。
+
+可选项：
+
+- [mmdet](https://github.com/open-mmlab/mmdetection) (用于“姿态估计”)
+- [mmtrack](https://github.com/open-mmlab/mmtracking) (用于“姿态跟踪”)
+- [pyrender](https://pyrender.readthedocs.io/en/latest/install/index.html) (用于“三维人体形状恢复”)
+- [smplx](https://github.com/vchoutas/smplx) (用于“三维人体形状恢复”)
+
+注意：
+
+1. 在步骤 c 中，git commit 的 id 将会被写到版本号中，如 0.6.0+2e7045c。这个版本号也会被保存到训练好的模型中。
+   这里推荐用户每次在步骤 b 中对本地代码和 github 上的源码进行同步。如果 C++/CUDA 代码被修改，就必须进行这一步骤。
+
+1. 根据上述步骤，MMPose 就会以 `dev` 模式被安装，任何本地的代码修改都会立刻生效，不需要再重新安装一遍（除非用户提交了 commits，并且想更新版本号）。
+
+1. 如果用户想使用 `opencv-python-headless` 而不是 `opencv-python`，可再安装 MMCV 前安装 `opencv-python-headless`。
+
+1. 如果 mmcv 已经被安装，用户需要使用 `pip uninstall mmcv` 命令进行卸载。如果 mmcv 和 mmcv-full 同时被安装, 会报 `ModuleNotFoundError` 的错误。
+
+1. 一些依赖包是可选的。运行 `python setup.py develop` 将只会安装运行代码所需的最小要求依赖包。
+   要想使用一些可选的依赖包，如 `smplx`，用户需要通过 `pip install -r requirements/optional.txt` 进行安装，
+   或者通过调用 `pip`（如 `pip install -v -e .[optional]`，这里的 `[optional]` 可替换为 `all`，`tests`，`build` 或 `optional`） 指定安装对应的依赖包，如 `pip install -v -e .[tests,build]`。
+
+## CPU 环境下的安装步骤
+
+MMPose 可以在只有 CPU 的环境下安装（即无法使用 GPU 的环境）。
+
+在 CPU 模式下，用户可以运行 `demo/demo.py` 的代码。
+
+## 源码安装 MMPose
+
+这里提供了 conda 下安装 MMPose 并链接 COCO 数据集路径的完整脚本（假设 COCO 数据的路径在 $COCO_ROOT）。
+
+```shell
+conda create -n open-mmlab python=3.7 -y
+conda activate open-mmlab
+
+# 安装最新的，使用默认版本的 CUDA 版本（一般为最新版本）预编译的 PyTorch 包
+conda install -c pytorch pytorch torchvision -y
+
+# 安装 mmcv-full。其中，命令里 url 的 ``{cu_version}`` 和 ``{torch_version}`` 变量需由用户进行指定。
+# 可查阅 [这里](https://github.com/open-mmlab/mmcv#installation) 以参考不同版本的 MMCV 所兼容的 PyTorch 和 CUDA 版本。
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
+
+# 安装 mmpose
+git clone git@github.com:open-mmlab/mmpose.git
+cd mmpose
+pip install -r requirements.txt
+python setup.py develop
+
+mkdir data
+ln -s $COCO_ROOT data/coco
+```
+
+## 利用 Docker 镜像安装 MMPose
+
+MMPose 提供一个 [Dockerfile](/docker/Dockerfile) 用户创建 docker 镜像。
+
+```shell
+# 创建拥有 PyTorch 1.6.0, CUDA 10.1, CUDNN 7 配置的 docker 镜像.
+docker build -f ./docker/Dockerfile --rm -t mmpose .
+```
+
+**注意**：用户需要确保已经安装了 [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)。
+
+运行以下命令：
+
+```shell
+docker run --gpus all\
+ --shm-size=8g \
+ -it -v {DATA_DIR}:/mmpose/data mmpose
+```
+
+## 在多个 MMPose 版本下进行开发
+
+MMPose 的训练和测试脚本已经修改了 `PYTHONPATH` 变量，以确保其能够运行当前目录下的 MMPose。
+
+如果想要运行环境下默认的 MMPose，用户需要在训练和测试脚本中去除这一行：
+
+```shell
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
+```