Dense correspondence between humans carries powerful semantic information that can be utilized to solve fundamental problems for full-body understanding such as in-the-wild surface matching, tracking and reconstruction.
To tackle this problem, we introduce a novel method to integrate observations across frames and encode the appearance at each individual frame by utilizing the human pose that models the body shape and point clouds which cover partial part of the human as the input.
We observe that deformable object motion is often semantically structured, and thus propose to learn Structured-implicit PArametric Models (SPAMs) as a deformable object representation that structurally decomposes non-rigid object motion into part-based disentangled representations of shape and pose, with each being represented by deep implicit functions.
We show our method generates high-quality novel views of synthetic and real human actors given a single sparse RGB-D input.
Given a segmentation mask defining the layout of the semantic regions in the texture map, our network generates high-resolution textures with a variety of styles, that are then used for rendering purposes.
In this paper, we address a key limitation of existing 2D face recognition methods: robustness to occlusions.
For many computer vision applications such as image captioning, visual question answering, and person search, learning discriminative feature representations at both image and text level is an essential yet challenging problem.
For many computer vision applications, such as image description and human identification, recognizing the visual attributes of humans is an essential yet challenging problem.
In this paper, we introduce a novel method to combine the advantages of both multi-task and curriculum learning in a visual attribute classification framework.
Incorporating additional knowledge in the learning process can be beneficial for several computer vision and machine learning tasks.
Visual attributes, from simple objects (e. g., backpacks, hats) to soft-biometrics (e. g., gender, height, clothing) have proven to be a powerful representational approach for many applications such as image description and human identification.
In this paper, we propose a novel regression-based method for employing privileged information to estimate the height using human metrology.