Search Results for author:
Found 22 papers, 7 papers with code
ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image
In this paper, we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM, a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy.
RoHM: Robust Human Motion Reconstruction via Diffusion
We apply RoHM to a variety of tasks -- from motion reconstruction and denoising to spatial and temporal infilling.
HISR: Hybrid Implicit Surface Representation for Photorealistic 3D Human Reconstruction
This representation is composed of two surface layers that represent opaque and translucent regions on the clothed human body.
NSF: Neural Surface Fields for Human Modeling from Monocular Depth
Compared to existing approaches, our method eliminates the expensive per-frame surface extraction while maintaining mesh coherency, and is capable of reconstructing meshes with arbitrary resolution without retraining.
VIVE3D: Viewpoint-Independent Video Editing using 3D-Aware GANs
Our experiments demonstrate that VIVE3D generates high-fidelity face edits at consistent quality from a range of camera viewpoints which are composited with the original video in a temporally and spatially consistent manner.
Multi-View Reconstruction using Signed Ray Distance Functions (SRDF)
Our approach bridges the gap between the two strategies with a novel volumetric shape representation that is implicit but parameterized with pixel depths to better materialize the shape surface with consistent signed distances along viewing rays.
Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet
In this paper, we propose Snipper, a unified framework to perform multi-person 3D pose estimation, tracking, and motion forecasting simultaneously in a single stage.
BodyMap: Learning Full-Body Dense Correspondence Map
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.
ARCH++: Animation-Ready Clothed Human Reconstruction Revisited
We present ARCH++, an image-based method to reconstruct 3D avatars with arbitrary clothing styles.
Ranked #1 on
3D Object Reconstruction From A Single Image
on RenderPeople
(using extra training data)
3D Object Reconstruction From A Single Image
Image-to-Image Translation
Data-Driven 3D Reconstruction of Dressed Humans From Sparse Views
We propose a data-driven end-to-end approach that reconstructs an implicit 3D representation of dressed humans from sparse camera views.
UNOC: Understanding Occlusion for Embodied Presence in Virtual Reality
Tracking body and hand motions in the 3D space is essential for social and self-presence in augmented and virtual environments.
ARCH: Animatable Reconstruction of Clothed Humans
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 #3 on
3D Object Reconstruction From A Single Image
on BUFF
3D Object Reconstruction From A Single Image
3D Reconstruction
DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Compare
We present DenseRaC, a novel end-to-end framework for jointly estimating 3D human pose and body shape from a monocular RGB image.
Ranked #85 on
3D Human Pose Estimation
on MPI-INF-3DHP
(using extra training data)
Learning Pose Grammar for Monocular 3D Pose Estimation
In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation from a monocular RGB image.
Ranked #15 on
3D Human Pose Estimation
on HumanEva-I
Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image
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
(AP@0.15 (10 / PNet-30) metric)
Attentive Fashion Grammar Network for Fashion Landmark Detection and Clothing Category Classification
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.
Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation
In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation.
Ranked #1 on
3D Absolute Human Pose Estimation
on Human3.6M
(Average MPJPE (mm) metric)
Scene-centric Joint Parsing of Cross-view Videos
The proposed joint parsing framework represents such correlations and constraints explicitly and generates semantic scene-centric parse graphs.
A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects
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.
Multi-View People Tracking via Hierarchical Trajectory Composition
This paper presents a hierarchical composition approach for multi-view object tracking.
Complex Background Subtraction by Pursuing Dynamic Spatio-Temporal Models
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.
Human Re-identification by Matching Compositional Template with Cluster Sampling
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.
