Leveraging MoCap Data for Human Mesh Recovery

Training state-of-the-art models for human body pose and shape recovery from images or videos requires datasets with corresponding annotations that are really hard and expensive to obtain. Our goal in this paper is to study whether poses from 3D Motion Capture (MoCap) data can be used to improve image-based and video-based human mesh recovery methods. We find that fine-tune image-based models with synthetic renderings from MoCap data can increase their performance, by providing them with a wider variety of poses, textures and backgrounds. In fact, we show that simply fine-tuning the batch normalization layers of the model is enough to achieve large gains. We further study the use of MoCap data for video, and introduce PoseBERT, a transformer module that directly regresses the pose parameters and is trained via masked modeling. It is simple, generic and can be plugged on top of any state-of-the-art image-based model in order to transform it in a video-based model leveraging temporal information. Our experimental results show that the proposed approaches reach state-of-the-art performance on various datasets including 3DPW, MPI-INF-3DHP, MuPoTS-3D, MCB and AIST. Test code and models will be available soon.

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Results from the Paper

Ranked #3 on 3D Human Pose Estimation on MPI-INF-3DHP (Acceleration Error metric)

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Human Pose Estimation 3DPW MoCap-SPIN + PoseBERT PA-MPJPE 52.9 # 30
MPJPE 89.4 # 40
MPVPE 103.8 # 22
Acceleration Error 8.3 # 4
3D Human Pose Estimation MPI-INF-3DHP MoCap-SPIN + PoseBERT MPJPE 97.4 # 39
PA-MPJPE 63.3 # 9
Acceleration Error 8.7 # 3