Self-Attentive 3D Human Pose and Shape Estimation from Videos
| Autor: | Yun-Chun Chen, Marco Piccirilli, Ming-Hsuan Yang, Robinson Piramuthu |
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| Rok vydání: | 2021 |
| Předmět: |
Estimation
FOS: Computer and information sciences Exploit Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Frame (networking) Computer Science - Computer Vision and Pattern Recognition 020207 software engineering 02 engineering and technology Human motion Task (project management) Image (mathematics) Signal Processing 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Computer vision Computer Vision and Pattern Recognition Artificial intelligence business Temporal information Software |
| DOI: | 10.48550/arxiv.2103.14182 |
| Popis: | We consider the task of estimating 3D human pose and shape from videos. While existing frame-based approaches have made significant progress, these methods are independently applied to each image, thereby often leading to inconsistent predictions. In this work, we present a video-based learning algorithm for 3D human pose and shape estimation. The key insights of our method are two-fold. First, to address the inconsistent temporal prediction issue, we exploit temporal information in videos and propose a self-attention module that jointly considers short-range and long-range dependencies across frames, resulting in temporally coherent estimations. Second, we model human motion with a forecasting module that allows the transition between adjacent frames to be smooth. We evaluate our method on the 3DPW, MPI-INF-3DHP, and Human3.6M datasets. Extensive experimental results show that our algorithm performs favorably against the state-of-the-art methods. Comment: This paper is under consideration at Computer Vision and Image Understanding |
| Databáze: | OpenAIRE |
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