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animationgpt's Introduction

animationGPT

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Character animation generation based on text-to-motion and large models

Dataset

数据集制作和其他问题:dataset.md

Version Size Notes
soul_v1(old) 30140
(-M=15070)
1. 删除了镜像npy(mirror)后文件数是15070;
2. 注意:Mean.npy和Std.npy的计算不包括镜像文件;
3. 部分动画new_joints缺失帧数信息,导致对应的new_joint_vecs的帧数为1。
soul_v2 14993 1. shinnobi和grappling缺失词性标注导致模型训练崩溃。
soul_v3 11662 1. 部分标注异常,例如“The character performs the '忍义手' pose ”;
2. 多个标注内容重复(GPT标注问题):
其中5个重复(12)、4个重复(19)、3个重复(153)、2个重复(863)。
soul_v4 8723 1. 两版标注(一简、二简;对应的soul_v3是详细版表述);
2. 侧重对root motion的方位词描述;
3. 增加了帧数的描述。

Experiment

evaluation on mGPT

Metric soul_v3 HSmerge Finetune-H+S3
Matching Score↓ 6.1470±0.0140 6.1315±0.0182 6.1942±0.0127
Matching Score (vald)↓ (gt for MLD/mGPT) 5.5185±0.0043 3.5719±0.0056 5.5185±0.0043
R_precision (top 1)↑ 0.0668±0.0018 0.1825±0.0028 0.0364±0.0018
R_precision (top 2)↑ 0.1250±0.0031 0.2781±0.0034 0.0697±0.0029
R_precision (top 3)↑ 0.1730±0.0031 0.3452±0.0033 0.1043±0.0037
R_precision (gt top 1)↑ 0.0929±0.0019 0.4466±0.0031 0.0929±0.0019
R_precision (gt top 2)↑ 0.1586±0.0023 0.5879±0.0024 0.1586±0.0023
R_precision (gt top 3)↑ 0.2130±0.0029 0.6612±0.0020 0.2130±0.0029
FID↓ 1.3792±0.0498 0.9084±0.0255 1.9095±0.0342
Diversity→ 5.7904±0.0510 8.3893±0.0752 4.6923±0.0325
Diversity (vald)→ (gt for MLD/mGPT) 5.6903±0.0740 8.5648±0.0603 5.6903±0.0740
MultiModality ↑ 3.6207±0.0872 5.8888±0.1620 2.9249±0.0914

从评估指标来看,混合训练在数据量上占优势,因此刷点效果不错,视觉效果待评价。微调的效果最差,从网页展示的对比效果来看,数据分布被严重影响。

Acknowledgments

Our code is partially borrowing from them.

感谢大佬们的项目

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