3d human shape and pose from a single low-resolution image with self-supervised learning
Abstract 3D human shape and pose estimation from monocular images has been an active
area of research in computer vision, having a substantial impact on the development of new
applications, from activity recognition to creating virtual avatars. Existing deep learning
methods for 3D human shape and pose estimation rely on relatively high-resolution input
images; however, high-resolution visual content is not always available in several practical
scenarios such as video surveillance and sports broadcasting. Low-resolution images in …
area of research in computer vision, having a substantial impact on the development of new
applications, from activity recognition to creating virtual avatars. Existing deep learning
methods for 3D human shape and pose estimation rely on relatively high-resolution input
images; however, high-resolution visual content is not always available in several practical
scenarios such as video surveillance and sports broadcasting. Low-resolution images in …
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning Supplementary Material
X Xu, H Chen, F Moreno-Noguer, LA Jeni… - ecva.net
… 2 Generalization to Real-World Images We also evaluate the proposed algorithm on real-world
low-resolution images. As shown in Figure 2 and 3, our method generalizes well to real
scenarios. …
low-resolution images. As shown in Figure 2 and 3, our method generalizes well to real
scenarios. …
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