Search Results for author:
Found 23 papers, 14 papers with code
Generating Human Interaction Motions in Scenes with Text Control
Our approach begins with pre-training a scene-agnostic text-to-motion diffusion model, emphasizing goal-reaching constraints on large-scale motion-capture datasets.
DECO: Dense Estimation of 3D Human-Scene Contact In The Wild
In contrast, we focus on inferring dense, 3D contact between the full body surface and objects in arbitrary images.
Ranked #2 on
Contact Detection
on BEHAVE
POCO: 3D Pose and Shape Estimation with Confidence
To address this, we develop POCO, a novel framework for training HPS regressors to estimate not only a 3D human body, but also their confidence, in a single feed-forward pass.
TADA! Text to Animatable Digital Avatars
We introduce TADA, a simple-yet-effective approach that takes textual descriptions and produces expressive 3D avatars with high-quality geometry and lifelike textures, that can be animated and rendered with traditional graphics pipelines.
TeCH: Text-guided Reconstruction of Lifelike Clothed Humans
But how to effectively capture all visual attributes of an individual from a single image, which are sufficient to reconstruct unseen areas (e. g., the back view)?
ProxyCap: Real-time Monocular Full-body Capture in World Space via Human-Centric Proxy-to-Motion Learning
For more accurate and physically plausible predictions in world space, our network is designed to learn human motions from a human-centric perspective, which enables the understanding of the same motion captured with different camera trajectories.
Ranked #208 on
3D Human Pose Estimation
on Human3.6M
GraMMaR: Ground-aware Motion Model for 3D Human Motion Reconstruction
Demystifying complex human-ground interactions is essential for accurate and realistic 3D human motion reconstruction from RGB videos, as it ensures consistency between the humans and the ground plane.
High-Fidelity Clothed Avatar Reconstruction from a Single Image
This paper presents a framework for efficient 3D clothed avatar reconstruction.
SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments
We present SLOPER4D, a novel scene-aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human-scene interaction in the wild.
Test-time Personalizable Forecasting of 3D Human Poses
Current motion forecasting approaches typically train a deep end-to-end model from the source domain data, and then apply it directly to target subjects.
MIME: Human-Aware 3D Scene Generation
We propose MIME (Mining Interaction and Movement to infer 3D Environments), which is a generative model of indoor scenes that produces furniture layouts that are consistent with the human movement.
Ranked #2 on
Indoor Scene Synthesis
on PRO-teXt
2D Semantic Segmentation task 1 (8 classes)
3D Semantic Scene Completion
+2
Generating Holistic 3D Human Motion from Speech
This work addresses the problem of generating 3D holistic body motions from human speech.
Ranked #2 on
Gesture Generation
on BEAT2
One-shot Implicit Animatable Avatars with Model-based Priors
Most of these methods fail to achieve realistic reconstruction when only a single image is available.
NeRF-Loc: Transformer-Based Object Localization Within Neural Radiance Fields
Using current NeRF training tools, a robot can train a NeRF environment model in real-time and, using our algorithm, identify 3D bounding boxes of objects of interest within the NeRF for downstream navigation or manipulation tasks.
Capturing and Inferring Dense Full-Body Human-Scene Contact
We capture a new dataset called RICH for "Real scenes, Interaction, Contact and Humans."
Ranked #3 on
Contact Detection
on BEHAVE
Human-Aware Object Placement for Visual Environment Reconstruction
In fact, we demonstrate that these human-scene interactions (HSIs) can be leveraged to improve the 3D reconstruction of a scene from a monocular RGB video.
Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking
In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely $D^{2}$HC-RMVSNet, for accurate dense point cloud reconstruction.
M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network
To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences.
M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network
To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function to learn the inherent constraints from different perspectives of matching correspondences.
SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud
First, the semantic context information in LiDAR is seldom explored in previous works, which may help identify ambiguous vehicles.
Learning Depth-Guided Convolutions for Monocular 3D Object Detection
3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information.
Ranked #17 on
Vehicle Pose Estimation
on KITTI Cars Hard
Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation
n this paper, we propose an effective and efficient pyramid multi-view stereo (MVS) net with self-adaptive view aggregation for accurate and complete dense point cloud reconstruction.
MMFace: A Multi-Metric Regression Network for Unconstrained Face Reconstruction
We propose to address the face reconstruction in the wild by using a multi-metric regression network, MMFace, to align a 3D face morphable model (3DMM) to an input image.
