AnimationGPT is a project focused on generating combat style character animations based on text. This project is trained on the MotionGPT and has produced the first character animation dataset dedicated to combat styles, named CombatMotion, which comes with textual descriptions.
Compare to current text-to-motion dataset
Dataset | Motions | Texts | Style | Source |
---|---|---|---|---|
KIT-ML | 3,911 | 6,278 | Daily Life | Motion Capture |
HumanML3D | 14,616 | 44,970 | Daily Life | Motion Capture |
Motion-X | 81,084 | 81,084 | Daily Life | Video Reconstruction |
CMP | 8,700 | 26,100 | Combat | Game |
CMR | 14,883 | 14,883 | Combat | Game |
Compared to the current text-to-motion datasets, CombatMotion has the following characteristics:
Obtain game assets in FBX format, redirect them to SMPL, and read the coordinates of human body joints (refer to Fbx2SMPL);
Add textual annotations. For each animation, manually annotate it from the following aspects: action type, weapon type, attack type, locational words, power descriptor words, speed descriptor words, and confusion descriptor words. A partial list of terms is shown below:
Action type | Weapon type | Attack type | Locative words | Power | Speed | Fuzzy |
---|---|---|---|---|---|---|
Idle | Bare Hand | Left-Handed | In-Place | Light-Weighted | Swift | Piercing |
Get Hit | Sacred Seal | Right-Handed | Towards Left | Steady | Relative Fast | Slash |
Death | Fist | One-Handed | Towards Right | Heavy-Weighted | Uniform Speed | Blunt |
… | … | … | … | … | … | … |
Then, use GPT-4 to combine these annotations into sentences.
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The diagram above outlines our annotation process. Initially, we fill in seven key descriptive words based on the characteristics of the animation, followed by writing posture description sentences. Subsequently, we use a large language model to integrate these elements into several complete natural language sentences. Finally, we select the sentence that best meets our requirements as the annotation result.
Process the animation and annotated data into a format compatible with HumanML3D.
Download: google drive
CombatMotionProcessed(CMP) is a refined dataset that, in terms of character animation, retains 8,700 high-quality animations with a strong fighting style. In terms of textual annotations, we provide three text annotations for each animation: a concise description, a concise description with sensory details, and a detailed description.
Taking CMP008388
as an example, its corresponding text annotations are:
weapon attack a man holding a Katana,executing a Charged Heavy Attack,Dual Wielding,root motion get Forward, Steady,Powerful and Relative Slow,First slow then fast,Cleanly.
weapon attack a man holding a Katana,executing a Charged Heavy Attack,Dual Wielding,root motion get Forward, Steady,Powerful and Relative Slow,First slow then fast,Cleanly,which make a sense of Piercing,Wide Open,Charged,Accumulating strength.
The character grips the wedge with both hands and charges for a powerful strike. They firmly lower their body, twist to the left, lunge forward with a bow step, and stab with the sword held in both hands.
Download: google drive
CombatMotionRaw (CMR) is an unrefined dataset containing 14,883 animation entries (CMP is a subset of CMR), but each animation is only provided with one textual annotation. Moreover, the textual annotations in CMR consist of simple concatenations of annotated words. It was found during project development that models trained with this type of annotation performed poorly, thus this format was ultimately not adopted.
Example of textual annotation:
weapon attack curved sword curved greatsword right-handed one-handed charged heavy attack forward steady powerful charged accumulating strength cleanly first slow then fast slash smooth and coherent wide open featherlike roundabout lean over and twist your waist to the left step forward with your right leg store your right hand from the left back swing it diagonally downward and swing two circles.
CMR has a richer set of animation data, unfortunately, the annotations are not detailed enough. You can read the textual annotations from the dataset yourself and refine them.
Here are models trained on the CMP dataset using different algorithms:
Evaluation on CMP
Metric | MotionGPT | MLD | MDM |
---|---|---|---|
Matching Score↓ | 5.426 ± 0.017 | 5.753 ± 0.019 | 7.220 ± 0.018 |
Matching Score (Ground Truth)↓ | 5.166 ± 0.012 | 5.177 ± 0.018 | 5.179 ± 0.013 |
R_precision (top 1)↑ | 0.044 ± 0.002 | 0.048 ± 0.002 | 0.030 ± 0.001 |
R_precision (top 2)↑ | 0.084 ± 0.003 | 0.089 ± 0.003 | 0.063 ± 0.002 |
R_precision (top 3)↑ | 0.122 ± 0.003 | 0.126 ± 0.003 | 0.096 ± 0.002 |
R_precision (top 1)(Ground Truth)↑ | 0.050 ± 0.002 | 0.051 ± 0.002 | 0.053 ± 0.002 |
R_precision (top 2)(Ground Truth)↑ | 0.094 ± 0.002 | 0.095 ± 0.003 | 0.097 ± 0.003 |
R_precision (top 3)(Ground Truth)↑ | 0.133 ± 0.003 | 0.134 ± 0.004 | 0.136 ± 0.004 |
FID↓ | 0.531 ± 0.018 | 1.240 ± 0.036 | 40.395 ± 0.424 |
Diversity→ | 5.143 ± 0.052 | 5.269 ± 0.044 | 3.364 ± 0.080 |
Diversity (Ground Truth)→ | 5.188 ± 0.070 | 5.200 ± 0.049 | 5.191 ± 0.036 |
MultiModality ↑ | 1.793 ± 0.094 | 2.618 ± 0.115 | 2.463 ± 0.102 |
If you need to train a model, please download the CMP dataset. Then, follow the tutorials for MotionGPT or other text-to-motion algorithms to set up the environment and train your model.
If you only need to use the AGPT model trained on the CMP dataset, please follow these steps:
Set up the environment
Our experimental environment is Ubuntu 22.04, NVIDIA GeForce RTX 4090, and CUDA 11.8
git clone https://github.com/OpenMotionLab/MotionGPT.git
cd MotionGPT
conda create python=3.10 --name mgpt
conda activate mgpt
pip install -r requirements.txt
python -m spacy download en_core_web_sm
mkdir deps
cd deps
bash prepare/prepare_t5.sh
bash prepare/download_t2m_evaluators.sh
Download the CMP dataset
Unzip the dataset into the datasets/humanml3d
directory.
.
└── humanml3d
├── new_joint_vecs
├── new_joints
└── texts
Generate animations using the model
git clone https://github.com/fyyakaxyy/AnimationGPT.git
Copy the tools
folder and config_AGPT.yaml
into the MotionGPT
directory
Download the AGPT model, place it in the MotionGPT
directory
Save the prompt in input.txt
Run python demo.py --cfg ./config_AGPT.yaml --example ./input.txt
The generated result is id_out.npy
, stored in results/mgpt/debug--AGPT/
File format conversion
tools/animation.py
, then run: python animation.py
tools/npy2bvh/joints2bvh.py
, then run: python joints2bvh.py
Note: The code for npy2bvh is sourced from MomaskUse the AGPT model trained on the CMP dataset under Windows10:
When configuring the environment for MotionGPT (step 1), some packages may still be missing after using python=3.10.6 and installing requirements.txt, just follow the instructions to install them manually.
Windows file path separator and linux are different, some path errors need to be changed to the Win system separator, such as the separator '/'
change to os.sep
in the config.py
Convert the generated npy files to mp4 files under python=3.10 environment may report errors. The matplotlib library requires version 3.3.3, but the minimum supported library version of cp310 is 3.5.0. If you use a library version higher than 3.5.0, you will encounter the following error:
ax.lines = [] AttributeError: can't set attribute
,
ax.collections = [] AttributeError: can't set attribute
,
ani.save "ValueError: unknown file extension: .mp4
.
If you encounter only the first two errors when executing with matplotlib>=3.5.0, you can refer to this issue https://github.com/GuyTevet/motion-diffusion-model/issues/6.
If you are also experiencing unrecognized mp4 files, you need to additionally download ffmpeg, unzip it and modify these contents in tools/animation.py
:
import matplotlib.pyplot as plt
plt.rcParams['animation.ffmpeg_path'] = r'D:\\ffmpeg\\bin\\ffmpeg.exe' #ffmpeg floder
from mpl_toolkits.mplot3d import Axes3D
If you have successfully generated a video file after resolving the error, but the video only has a white screen, please try switching to another python version to do the npy file format conversion. tools/requirements.txt
provides the necessary dependencies for python=3.9.19 to work properly.
The following problems may be encountered when converting the generated npy files to bvh files
Some packages are missing or numpy is reporting errors. Prioritize using python=3.9.19 and install the dependencies in tools/requirements.txt.
tools/npy2bvh/joints2bvh.py
is missing some package imports. Add this code:
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import mpl_toolkits.mplot3d.axes3d as p3
No such file or directory: './visualization/data/template.bvh'
. Modify the following path to use the commented out version:
self.template = BVH.load('./visualization/data/template.bvh', need_quater=True)
#self.template = BVH.load(os.path.dirname(__file__) + '\\visualization\\data\\template.bvh', need_quater=True)
index 1 is out of bounds for axis 1 with size 1
. Make sure there is no _in.npy
file in the path of the file you want to convert, just keep _out.npy
to solve the problem.
During the process of dataset creation and model training/tuning, you might encounter some issues in aspects like textual annotations, model training, and data augmentation. Based on our experience, we offer the following suggestions:
If you process data using the HumanML3D pipeline, you might encounter the following issues, which can lead to model training crashes:
Mixing the HumanML3D, KIT-ML, and CMP datasets for model training can result in significant improvements in evaluation metrics.
However, evaluation metrics and visual effects are not equivalent. For some generated results, models trained on a mixed dataset perform worse than those trained solely on the CMP dataset. Because differences in action styles between datasets change the data distribution, thereby affecting model performance.
You can try converting Motion-X into the HumanML3D format for pre-training the model, and then fine-tuning it on the CMP dataset.
Our code is partially borrowing from them.
If you find this repository useful, please consider citing it as follows:
@misc{CombatMotion,
title={AnimationGPT:An AIGC tool for generating game combat motion assets},
author={Yihao Liao, Yiyu Fu, Ziming Cheng, Jiangfeiyang Wang},
year={2024},
howpublished={\url{https://github.com/fyyakaxyy/AnimationGPT}}
}
@InProceedings{Guo_2022_CVPR,
author = {Guo, Chuan and Zou, Shihao and Zuo, Xinxin and Wang, Sen and Ji, Wei and Li, Xingyu and Cheng, Li},
title = {Generating Diverse and Natural 3D Human Motions From Text},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {5152-5161}
}
@article{jiang2024motiongpt,
title={Motiongpt: Human motion as a foreign language},
author={Jiang, Biao and Chen, Xin and Liu, Wen and Yu, Jingyi and Yu, Gang and Chen, Tao},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2024}
}
AnimationGPT是一个基于文本生成格斗风格角色动画的项目。本项目基于MotionGPT训练模型,并且制作了首个专注于格斗风格、并配备文本描述的角色动画数据集CombatMotion。
对比现有文本-动作数据集
数据集 | 动作数量 | 文本数量 | 风格 | 来源 |
---|---|---|---|---|
KIT-ML | 3,911 | 6,278 | 日常 | 动作捕捉 |
HumanML3D | 14,616 | 44,970 | 日常 | 动作捕捉 |
Motion-X | 81,084 | 81,084 | 日常 | 视频重建 |
CMP | 8,700 | 26,100 | 格斗 | 游戏 |
CMR | 14,883 | 14,883 | 格斗 | 游戏 |
与现有文本-动作数据集相比,CombatMotion具有如下特点:
获取fbx格式的游戏资产,重定向到SMPL,并读取人体关节点坐标(参考Fbx2SMPL);
添加文本标注。对于每一条动画,首先从动作类型、武器类型、攻击类型、方位词、力量感描述词、速度描述词和困惑描述词这几个方面添加人工标注,部分词表如下所示:
Action type | Weapon type | Attack type | Locative words | Power | Speed | Fuzzy |
---|---|---|---|---|---|---|
Idle | Bare Hand | Left-Handed | In-Place | Light-Weighted | Swift | Piercing |
Get Hit | Sacred Seal | Right-Handed | Towards Left | Steady | Relative Fast | Slash |
Death | Fist | One-Handed | Towards Right | Heavy-Weighted | Uniform Speed | Blunt |
… | … | … | … | … | … | … |
然后通过GPT-4将这些标注连接成句子。
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上图概述了我们制作标注的过程,首先基于动作动画的特性填写七大类关键描述词,再撰写姿势描述句,随后利用大语言模型将这些要素融合成为数个完整的自然语言句子,最后从中挑选最符合需求的作为标注结果。
将动画和标注数据处理成HumanML3D格式的数据。
下载链接:google drive
CombatMotionProcessed(CMP)是精加工的数据集,在角色动画方面,我们保留了高质量、格斗风格强的8,700个动画,在文本标注方面,我们为每一条动画提供了3条文本标注,分别是精简版描述、带有感觉描述的精简版描述和详细版描述。
以CMP008388
为例,其对应的文本标注是:
weapon attack a man holding a Katana,executing a Charged Heavy Attack,Dual Wielding,root motion get Forward, Steady,Powerful and Relative Slow,First slow then fast,Cleanly.
weapon attack a man holding a Katana,executing a Charged Heavy Attack,Dual Wielding,root motion get Forward, Steady,Powerful and Relative Slow,First slow then fast,Cleanly,which make a sense of Piercing,Wide Open,Charged,Accumulating strength.
The character grips the wedge with both hands and charges for a powerful strike. They firmly lower their body, twist to the left, lunge forward with a bow step, and stab with the sword held in both hands.
下载链接:google drive
CombatMotionRaw(CMR)是未经过精加工的数据集,具备14,883个的动画数据(CMP是CMR的子集),但每条动画只提供一个文本标注。另外,CMR中的文本标注是标注词的简单连接,在项目研发中发现这种标注训练的模型性能较差,因此最终未采用这种格式。
文本标注示例:
weapon attack curved sword curved greatsword right-handed one-handed charged heavy attack forward steady powerful charged accumulating strength cleanly first slow then fast slash smooth and coherent wide open featherlike roundabout lean over and twist your waist to the left step forward with your right leg store your right hand from the left back swing it diagonally downward and swing two circles.
CMR具备更丰富的动画数据,可惜标注不够精细,您可以自行读取数据集中的文本标注并优化。
以下分别是在CMP数据集上用不同算法训练的模型:
CMP数据集上的评估结果
Metric | MotionGPT | MLD | MDM |
---|---|---|---|
Matching Score↓ | 5.426 ± 0.017 | 5.753 ± 0.019 | 7.220 ± 0.018 |
Matching Score (Ground Truth)↓ | 5.166 ± 0.012 | 5.177 ± 0.018 | 5.179 ± 0.013 |
R_precision (top 1)↑ | 0.044 ± 0.002 | 0.048 ± 0.002 | 0.030 ± 0.001 |
R_precision (top 2)↑ | 0.084 ± 0.003 | 0.089 ± 0.003 | 0.063 ± 0.002 |
R_precision (top 3)↑ | 0.122 ± 0.003 | 0.126 ± 0.003 | 0.096 ± 0.002 |
R_precision (top 1)(Ground Truth)↑ | 0.050 ± 0.002 | 0.051 ± 0.002 | 0.053 ± 0.002 |
R_precision (top 2)(Ground Truth)↑ | 0.094 ± 0.002 | 0.095 ± 0.003 | 0.097 ± 0.003 |
R_precision (top 3)(Ground Truth)↑ | 0.133 ± 0.003 | 0.134 ± 0.004 | 0.136 ± 0.004 |
FID↓ | 0.531 ± 0.018 | 1.240 ± 0.036 | 40.395 ± 0.424 |
Diversity→ | 5.143 ± 0.052 | 5.269 ± 0.044 | 3.364 ± 0.080 |
Diversity (Ground Truth)→ | 5.188 ± 0.070 | 5.200 ± 0.049 | 5.191 ± 0.036 |
MultiModality ↑ | 1.793 ± 0.094 | 2.618 ± 0.115 | 2.463 ± 0.102 |
如果需要训练模型,请下载CMP数据集,然后按照MotionGPT或其它text-to-motion算法的教程配置环境,训练模型。
如果只需要使用CMP数据集上训练好的AGPT模型,请参考以下步骤:
配置环境
我们的实验环境是Ubuntu22.04,NVIDIA GeForce RTX 4090,CUDA 11.8
git clone https://github.com/OpenMotionLab/MotionGPT.git
cd MotionGPT
conda create python=3.10 --name mgpt
conda activate mgpt
pip install -r requirements.txt
python -m spacy download en_core_web_sm
mkdir deps
cd deps
bash prepare/prepare_t5.sh
bash prepare/download_t2m_evaluators.sh
下载CMP数据集
将数据集解压到datasets/humanml3d
路径下。
.
└── humanml3d
├── new_joint_vecs
├── new_joints
└── texts
使用模型生成动画
git clone https://github.com/fyyakaxyy/AnimationGPT.git
将tools
文件夹和config_AGPT.yaml
复制到MotionGPT
文件夹下
下载AGPT model,放置在MotionGPT
路径下
将prompt保存到input.txt
中
python demo.py --cfg ./config_AGPT.yaml --example ./input.txt
生成的结果是id_out.npy
,保存在results/mgpt/debug--AGPT/
路径下
文件格式转换
tools/animation.py
中的文件路径,然后运行:python animation.py
tools/npy2bvh/joints2bvh.py
中的文件路径,然后运行:python joints2bvh.py
备注:npy2bvh的代码来源于Momask在Windows10环境下使用训练好的模型:
为MotionGPT配置环境时(步骤1),使用其推荐的python=3.10.6并安装完requirements所需包可能仍然缺少一些包,按提示手动安装即可。
linux系统和windows文件路径分隔符不同,有路径类错误需要改为win系统的分隔符,如config.py中的分隔符'/'
修改为os.sep
。
npy文件转为mp4文件时,经测试不适合在python=3.10环境下执行,其matplotlib库需要使用3.3.3旧版本,但cp310最低支持的库版本为3.5.0,如果使用高于3.5.0版本的库会遇到三处报错:
ax.lines = [] AttributeError: can't set attribute
,
ax.collections = [] AttributeError: can't set attribute
,
ani.save "ValueError: unknown file extension: .mp4
如果您使用matplotlib>=3.5.0执行时只遇到了前两条报错,可以参考https://github.com/GuyTevet/motion-diffusion-model/issues/6。
如果您也遇到了无法识别mp4文件,需要额外下载ffmpeg,解压并修改tools/animation.py
中的:
import matplotlib.pyplot as plt
plt.rcParams['animation.ffmpeg_path'] = r'D:\\ffmpeg\\bin\\ffmpeg.exe' #ffmpeg floder
from mpl_toolkits.mplot3d import Axes3D
如果您解决报错后生成了视频文件,但视频为纯白画面,请尝试换用其他的python版本来做npy文件格式转换tools/requirements.txt
提供了python=3.9.19环境下可以正常执行所需的依赖。
npy文件转为bvh文件时,可能会遇到以下问题:
缺少某些包或是numpy报错。优先使用python=3.9.19并安装tools/requirements.txt
中的依赖。
tools/npy2bvh/joints2bvh.py
缺少import。为其中添加这些代码。
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import mpl_toolkits.mplot3d.axes3d as p3
No such file or directory: './visualization/data/template.bvh'
。修改下面的路径,换用注释掉的版本。
self.template = BVH.load('./visualization/data/template.bvh', need_quater=True)
#self.template = BVH.load(os.path.dirname(__file__) + '\\visualization\\data\\template.bvh', need_quater=True)
index 1 is out of bounds for axis 1 with size 1
。不要转换那些_in.npy
文件,换一个仅包含_out.npy
文件的路径转换数据。
在数据集制作和模型训练调优的过程中,您可能会在文本标注、模型训练、数据增强等方面遇到一些问题。基于我们的经验,给出以下建议:
如果采用HumanML3D的pipline处理数据,可能会遇到以下问题,它们将会导致模型训练崩溃:
将HumanML3D、KIT-ML和CMP数据集混合起来训练模型,在评估指标上会带来巨大提升,但评估指标和视觉效果并不等价,对于部分生成结果,混合训练的模型表现不如单独使用CMP数据集训练的模型,这是因为不同数据集动作风格的差异改变了数据分布,进而影响了模型的性能。
可以尝试将Motion-X转换成HumanML3D的格式,用于预训练模型,然后在CMP数据集上微调。
我们的代码部分借鉴了以上工作。
如果您觉得这个仓库对您有用,请考虑引用:
@misc{CombatMotion,
title={AnimationGPT:An AIGC tool for generating game combat motion assets},
author={Yihao Liao, Yiyu Fu, Ziming Cheng, Jiangfeiyang Wang},
year={2024},
howpublished={\url{https://github.com/fyyakaxyy/AnimationGPT}}
}
@InProceedings{Guo_2022_CVPR,
author = {Guo, Chuan and Zou, Shihao and Zuo, Xinxin and Wang, Sen and Ji, Wei and Li, Xingyu and Cheng, Li},
title = {Generating Diverse and Natural 3D Human Motions From Text},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {5152-5161}
}
@article{jiang2024motiongpt,
title={Motiongpt: Human motion as a foreign language},
author={Jiang, Biao and Chen, Xin and Liu, Wen and Yu, Jingyi and Yu, Gang and Chen, Tao},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2024}
}