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PyTorch implementation of "Towards Accurate Facial Motion Retargeting with Identity-Consistent and Expression-Exclusive Constraints" (AAAI2022)

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hi, i see in your paper that you also tried MobileNetV2 as backbone, how about the realtime? In other words, how much time does it takes to infer blendshapes from...

Hi, thanks for your great work. I use face-parsing.Pytorch to generate mask. The results as follows. AFW_156474078_1_0 ![AFW_156474078_1_0](https://user-images.githubusercontent.com/20163583/211957713-9a1a828f-5ae6-4c06-a907-327934032ef7.jpg) AFW_156474078_1_10 ![AFW_156474078_1_10](https://user-images.githubusercontent.com/20163583/211957737-9fba64bb-45f8-4721-ba00-b20172b7ca74.jpg) *** My result is different from yours. data/demo_dataset/300w_lp/face_mask/AFW_156474078_1/001/AFW_156474078_1_0 ![AFW_156474078_1_0](https://user-images.githubusercontent.com/20163583/211955101-7c57344f-c5c1-4893-8a43-e3e3dff65de6.jpg) data/demo_dataset/300w_lp/face_mask/AFW_156474078_1/001/AFW_156474078_1_10...

作者您好,感谢您分享的珍贵的研究工作,我这里有个问题想请教一下,关于mean_delta_blendshape.npy的计算问题,我看了您在https://github.com/deepmo24/CPEM/issues/3 这里的回复,有个细节没有考虑清楚,就是您在计算mean_delta_blendshape.npy的时候用到了deform transfor的技术.以下几个问题请您解惑: 1.facewarehouse的数据共有150个identity,每个identity有46个blendshape的obj文件,顶点数有11k个,而BFM的模型顶点数是35709,这里用deform transfor的时候,是将facewarehouse数据中的某个identity的46个blendshape的obj转换到BFM09的mean blendshape,还是先计算了数据集里面所有的identity的blendshap的平均值,也就是说把facewarehouse150个identity的46个blendshape的obj相加,然后求平均计算出46个blendshape,然后再转换到BFM09的的mean blendshape. 2.这个BFM09的mean blendshape是S+T?S代表形状的平均,T代表纹理的平均

lt looks like using alpha and beta params to control retarget face, but what if metahuman's face? How to using these params to retarget it?

Hi, I just finished reading your paper and I'm curious about how you implement cross-domain retargeting for virtual avatars. Specifically, after obtaining the pose and expression parameters, how would you...