88 citations · 146 across the 17 of their papers we have counts for
24 papers · 1 filter
EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild
Manuel Kaufmann, Jie Song, Chen Guo +5
We present EMDB, the Electromagnetic Database of Global 3D Human Pose and Shape in the Wild. EMDB is a novel dataset that contains high-quality 3D SMPL pose and shape parameters wi…
Learning Locally Editable Virtual Humans
Hsuan-I Ho, Lixin Xue, Jie Song +1
In this paper, we propose a novel hybrid representation and end-to-end trainable network architecture to model fully editable and customizable neural avatars. At the core of our wo…
Hi4D: 4D Instance Segmentation of Close Human Interaction
Yifei Yin, Chen Guo, Manuel Kaufmann +3
We propose Hi4D, a method and dataset for the automatic analysis of physically close human-human interaction under prolonged contact. Robustly disentangling several in-contact subj…
Human from Blur: Human Pose Tracking from Blurry Images
Yiming Zhao, Denys Rozumnyi, Jie Song +3
We propose a method to estimate 3D human poses from substantially blurred images. The key idea is to tackle the inverse problem of image deblurring by modeling the forward problem…
Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition
Chen Guo, Tianjian Jiang, Xu Chen +2
We present Vid2Avatar, a method to learn human avatars from monocular in-the-wild videos. Reconstructing humans that move naturally from monocular in-the-wild videos is difficult.…
InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds
Tianjian Jiang, Xu Chen, Jie Song +1
In this paper, we take a significant step towards real-world applicability of monocular neural avatar reconstruction by contributing InstantAvatar, a system that can reconstruct hu…
Fast-SNARF: A Fast Deformer for Articulated Neural Fields
Xu Chen, Tianjian Jiang, Jie Song +4
Neural fields have revolutionized the area of 3D reconstruction and novel view synthesis of rigid scenes. A key challenge in making such methods applicable to articulated objects,…
PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence
Zijian Dong, Chen Guo, Jie Song +3
We present a novel method to learn Personalized Implicit Neural Avatars (PINA) from a short RGB-D sequence. This allows non-expert users to create a detailed and personalized virtu…
gDNA: Towards Generative Detailed Neural Avatars
Xu Chen, Tianjian Jiang, Jie Song +4
To make 3D human avatars widely available, we must be able to generate a variety of 3D virtual humans with varied identities and shapes in arbitrary poses. This task is challenging…
Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training
Haofei Zhang, Jiarui Duan, Mengqi Xue +3
Recently, vision Transformers (ViTs) are developing rapidly and starting to challenge the domination of convolutional neural networks (CNNs) in the realm of computer vision (CV). W…
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
Sammy Christen, Muhammed Kocabas, Emre Aksan +3
We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose.…
A Survey of Deep Learning for Low-Shot Object Detection
Qihan Huang, Haofei Zhang, Mengqi Xue +2
Object detection has achieved a huge breakthrough with deep neural networks and massive annotated data. However, current detection methods cannot be directly transferred to the sce…
Human Performance Capture from Monocular Video in the Wild
Chen Guo, Xu Chen, Jie Song +1
Capturing the dynamically deforming 3D shape of clothed human is essential for numerous applications, including VR/AR, autonomous driving, and human-computer interaction. Existing…
Render In-between: Motion Guided Video Synthesis for Action Interpolation
Hsuan-I Ho, Xu Chen, Jie Song +1
Upsampling videos of human activity is an interesting yet challenging task with many potential applications ranging from gaming to entertainment and sports broadcasting. The main d…
Shape-aware Multi-Person Pose Estimation from Multi-View Images
Zijian Dong, Jie Song, Xu Chen +2
In this paper we contribute a simple yet effective approach for estimating 3D poses of multiple people from multi-view images. Our proposed coarse-to-fine pipeline first aggregates…
Convolutional Autoencoders for Human Motion Infilling
Manuel Kaufmann, Emre Aksan, Jie Song +3
In this paper we propose a convolutional autoencoder to address the problem of motion infilling for 3D human motion data. Given a start and end sequence, motion infilling aims to c…
Human Body Model Fitting by Learned Gradient Descent
Jie Song, Xu Chen, Otmar Hilliges
We propose a novel algorithm for the fitting of 3D human shape to images. Combining the accuracy and refinement capabilities of iterative gradient-based optimization techniques wit…
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis
Xu Chen, Zijian Dong, Jie Song +2
Many object pose estimation algorithms rely on the analysis-by-synthesis framework which requires explicit representations of individual object instances. In this paper we combine…
Unpaired Pose Guided Human Image Generation
Xu Chen, Jie Song, Otmar Hilliges
This paper studies the task of full generative modelling of realistic images of humans, guided only by coarse sketch of the pose, while providing control over the specific instance…
Monocular Neural Image Based Rendering with Continuous View Control
Xu Chen, Jie Song, Otmar Hilliges
We present an approach that learns to synthesize high-quality, novel views of 3D objects or scenes, while providing fine-grained and precise control over the 6-DOF viewpoint. The a…
End-to-end Learning for Graph Decomposition
Jie Song, Bjoern Andres, Michael Black +2
We propose a novel end-to-end trainable framework for the graph decomposition problem. The minimum cost multicut problem is first converted to an unconstrained binary cubic formula…
Cross-modal Deep Variational Hand Pose Estimation
Adrian Spurr, Jie Song, Seonwook Park +1
The human hand moves in complex and high-dimensional ways, making estimation of 3D hand pose configurations from images alone a challenging task. In this work we propose a method t…
Learning Human Motion Models for Long-term Predictions
Partha Ghosh, Jie Song, Emre Aksan +1
We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone. Our approach, dubbed the Dropout Autoencoder LSTM, is capable of synthes…
Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos
Jie Song, Limin Wang, Luc Van Gool +1
Deep ConvNets have been shown to be effective for the task of human pose estimation from single images. However, several challenging issues arise in the video-based case such as se…