unexpected retargeting motion with custom bvh input motion

[summer-wind-hi]

hi PeiZhuo， 感谢分享这么好的项目，我在用我们自己的运动数据进行运动重定向的时候遇到一个问题，想请教一下，我们的具体处理流程是这样的：

1. 将检测到的运动员的3d pose转为bvh文件
2. 用生成的bvh文件作为输入，按照您项目中提供的预训练模型进行预处理 + retargeting，结果和预期大相径庭，见下图（gt和output）

图为输出和gt的bvh文件在blender中的可视化结果，其中红色是gt，黄色是output，当前姿态是一个运动员半弯腰准备跑动；

我将2d pose 转bvh以及对T-pose以及关节点的处理说明如下： 1）pose2bvh 使用的是这个项目：https://github.i.agoralab.co/dachr8/pose2bvh/tree/main/bvh_skeleton ， 输入是3d position，输出时对应的bvh文件；需要说明的是，我们在这个过程中做了2个改动： a.使用右手坐标系而不是左手坐标系 （Y-up, Z-forward, X-right） b. 由于我们只检测到14个skeleton 关键点，进行插值得到和您的方法相同的23个关键点

2）我们生成的bvh文件和您的输入保持相同的拓扑结构，T-pose 如下图所示(blender中按照Z-up显示)，包括bvh文件中保存关节点的顺序也一致 3）实验的时候，先让输入和输出拓扑相同，以便先走通流程

我们进行了多次实验，然而retargeting的结果都是和gt相差很多，而且毫无规律可言，对此我很疑惑，是不是输入bvh有其他方面的限定而我没有想到的？或者还有可能是哪方面存在问题导致这样的现象？非常期待您的回复和建议，谢谢！

regards, Summer Gao

[summer-wind-hi]

补充： Hi @PeizhuoLi 我想把我的bvh文件上传以便您参考，但是不知道如何上传，只好将T-pose和部分帧数据拷贝过来，麻烦您看下并不吝赐教，谢谢！

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[PeizhuoLi]

你好，首先joint position到bvh即joint rotation的转换是ill-posed，根据我的了解，至今没有效果特别好的解决方案。根据你提供的bvh文件（另，可以打包为zip直接拖到书写框上传），动作的质量非常不理想。一个可能的问题就是你的输入和训练数据的输入差异过大，可能无法迁移。另一方面，我们需要做per-character的normalization，因此可能需要一定量的数据量才能保证结果的正确。

[summer-wind-hi]

Hi @PeizhuoLi , 谢谢您的及时回复和建议！按照您说的方法，上传了我们的bvh文件data.zip，您有时间的话麻烦看一下； 根据您的两个建议，我表述下我这边的做法： 1）一个可能的问题就是你的输入和训练数据的输入差异过大，可能无法迁移 我们的数据和mixamo目前的差异（我发现的）是T-pose里两条腿的局部坐标系不一致（一个x轴向左一个向右），我们的T-pose在上图展示了，mixamo的T-pose如下图所示：

不知道每个关节点的局部坐标系是否会影响最后的结果？此外我说下我对bvh文件中旋转角度的理解，我理解的旋转角度是每个关节点相对于T-pose时这个关节点的角度，也就是当前帧这个关节点的局部坐标系相对于T-pose中这个关节点的局部坐标系的旋转角，不知道我理解的有没有问题；

 还有模型的大小是否对重建有影响？我们的motion大小上是比较小的，和mixamo整体差几倍到几十倍的样子，但是我一般会将我们的数据进行放大，看上去和mixamo大小上还有些差别，但不是很大；

此外人物距离原点的距离大是否会导致这样的结果？我们的场景是运动员在球场上跑，一段时间内跑动距离是比较长的；

2）另一方面，我们需要做per-character的normalization，因此可能需要一定量的数据量才能保证结果的正确 关于normalization我之前没有做太多测试工作，多谢您的提醒，我可能要检查一下这里；在这里说明一下我现在的测试方法：目前我测试的时候让输入和输出都是同一个模型，也就是说这个人的动作重建到相同的骨架上，我理解属于intra-structure类，具体细节和步骤如下：‘

 1）bvh文件在：../datasets/Mixamo/Custom/data.bvh
 2)  ../datasets/下：  python  preprocess.py  （这样是不是相当于提取自身的mean和var进行normalization）
 3)  ../retargeting下： python test.py
 4）结果保存在：..pretrained/results/bvh/Custom/ gt.bvh + 0_0.bvh

请问上述步骤是否有问题？关于您说的一定量的数据是指我在推理的时候需要准备足够多的测试样本以提取mean和var吗？

 我会做进一步的测试，也会再仔细看一下项目代码，针对上面的问题，希望得到您的回复和建议，谢谢！

data.zip

regards, Summer Gao

[PeizhuoLi]

你好，我前面提到的“差异过大”主要是指你们的动作数据包含较多的噪音，数据的分布和训练集不太一样。根据我们的经验这样的情况会导致效果不佳。

关于局部坐标问题：可以直接尝试从数据集中复制旋转到你们的骨架上，如果结果相近就没有问题。

大小：能缩放到相近的大小为佳

步骤看起来是没有问题的，如果效果不佳，可能需要考虑在你们的数据集上重新训练模型。

[summer-wind-hi]

Hi @PeizhuoLi : 谢谢您的建议！ 1）我们的数据是含噪声 的，因为pose的检测结果不够稳定；现在在尝试在我们的基础上训练模型； 2）大小：能缩放到相近的大小为佳 =》 已经缩放到和mixamo相近大小 3）关于局部坐标：可以直接尝试从数据集中复制旋转到你们的骨架上，如果结果相近就没有问题 关于这个，我这边做了这样的实验：把我们的数据集和mixamo数据集的T-pose和rotation互换进行retargeting测试，结果如下：

其中init-motion是指互换之后组成的motion，retargeting是指用init-motion做输入的输出，都是在blender中验证；从这个实验结果上来看，似乎是我们的rotation存在问题或者和mixamo不一致，但是init-motion本身的动作在blender里面看是正确的，it's weird, right?

regards, summer Gao

[PeizhuoLi]

根据这里的结果的，我猜测可能还是因为你们的数据的分布（rotation的噪音等原因）和训练集差距较大，导致使用你们的rotation输入网络后retargeting失败

[summer-wind-hi]

好的，我会继续用我们的数据进行训练，然后测试看效果有没有改善，谢谢您的建议！

[WindRunner724]

Hi @summer-wind-hi ， 请问你们最终的测试效果如何，我最近也在研究野外视频到重定向问题，目前对于2D RGB照片/视频目前已经可以得到较为准确关节旋转信息从而摆脱ill-pose的问题，但还是想请问一下3D姿态数据对重定向网络的训练效果如何，以及该如何去除噪声的影响。 期待您的回复，谢谢！