We present ARCH++, an image-based method to reconstruct 3D avatars with arbitrary clothing styles.
We propose a data-driven end-to-end approach that reconstructs an implicit 3D representation of dressed humans from sparse camera views.
Tracking body and hand motions in the 3D space is essential for social and self-presence in augmented and virtual environments.
In this paper, we propose ARCH (Animatable Reconstruction of Clothed Humans), a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image.
Ranked #2 on 3D Object Reconstruction From A Single Image on BUFF
We present DenseRaC, a novel end-to-end framework for jointly estimating 3D human pose and body shape from a monocular RGB image.
Ranked #36 on 3D Human Pose Estimation on Human3.6M (using extra training data)
In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation from a monocular RGB image.
Ranked #5 on 3D Human Pose Estimation on HumanEva-I
We propose a computational framework to jointly parse a single RGB image and reconstruct a holistic 3D configuration composed by a set of CAD models using a stochastic grammar model.
Ranked #4 on Monocular 3D Object Detection on SUN RGB-D
This paper proposes a knowledge-guided fashion network to solve the problem of visual fashion analysis, e. g., fashion landmark localization and clothing category classification.
In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation.
The proposed joint parsing framework represents such correlations and constraints explicitly and generates semantic scene-centric parse graphs.
We introduce a Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's visibility fluent and its activities, and develop a probabilistic graph model to jointly reason the visibility fluent change (e. g., from visible to invisible) and track humans in videos.
Although it has been widely discussed in video surveillance, background subtraction is still an open problem in the context of complex scenarios, e. g., dynamic backgrounds, illumination variations, and indistinct foreground objects.
This paper aims at a newly raising task in visual surveillance: re-identifying people at a distance by matching body information, given several reference examples.