HMP: Hand Motion Priors for Pose and Shape Estimation from Video

• URL: http://arxiv.org/abs/2312.16737v1
• Date: Wed, 27 Dec 2023 22:35:33 GMT
• Title: HMP: Hand Motion Priors for Pose and Shape Estimation from Video
• Authors: Enes Duran, Muhammed Kocabas, Vasileios Choutas, Zicong Fan and Michael J. Black
• Abstract summary: We develop a generative motion prior specific for hands, trained on the AMASS dataset which features diverse and high-quality hand motions. Our integration of a robust motion prior significantly enhances performance, especially in occluded scenarios. We demonstrate our method's efficacy via qualitative and quantitative evaluations on the HO3D and DexYCB datasets.
• Score: 52.39020275278984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding how humans interact with the world necessitates accurate 3D hand pose estimation, a task complicated by the hand's high degree of articulation, frequent occlusions, self-occlusions, and rapid motions. While most existing methods rely on single-image inputs, videos have useful cues to address aforementioned issues. However, existing video-based 3D hand datasets are insufficient for training feedforward models to generalize to in-the-wild scenarios. On the other hand, we have access to large human motion capture datasets which also include hand motions, e.g. AMASS. Therefore, we develop a generative motion prior specific for hands, trained on the AMASS dataset which features diverse and high-quality hand motions. This motion prior is then employed for video-based 3D hand motion estimation following a latent optimization approach. Our integration of a robust motion prior significantly enhances performance, especially in occluded scenarios. It produces stable, temporally consistent results that surpass conventional single-frame methods. We demonstrate our method's efficacy via qualitative and quantitative evaluations on the HO3D and DexYCB datasets, with special emphasis on an occlusion-focused subset of HO3D. Code is available at https://hmp.is.tue.mpg.de

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