Search Results for author:
Found 17 papers, 7 papers with code
Counting Stacked Objects from Multi-View Images
Visual object counting is a fundamental computer vision task underpinning numerous real-world applications, from cell counting in biomedicine to traffic and wildlife monitoring.
DiscoNeRF: Class-Agnostic Object Field for 3D Object Discovery
Neural Radiance Fields (NeRFs) have become a powerful tool for modeling 3D scenes from multiple images.
Reconstruction of Manipulated Garment with Guided Deformation Prior
Modeling the shape of garments has received much attention, but most existing approaches assume the garments to be worn by someone, which constrains the range of shapes they can assume.
Garment Recovery with Shape and Deformation Priors
While modeling people wearing tight-fitting clothing has made great strides in recent years, loose-fitting clothing remains a challenge.
ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns
Many approaches to draping individual garments on human body models are realistic, fast, and yield outputs that are differentiable with respect to the body shape on which they are draped.
DrapeNet: Garment Generation and Self-Supervised Draping
Recent approaches to drape garments quickly over arbitrary human bodies leverage self-supervision to eliminate the need for large training sets.
DIG: Draping Implicit Garment over the Human Body
Existing data-driven methods for draping garments over human bodies, despite being effective, cannot handle garments of arbitrary topology and are typically not end-to-end differentiable.
Is There More Pattern in Knowledge Graph? Exploring Proximity Pattern for Knowledge Graph Embedding
Modeling of relation pattern is the core focus of previous Knowledge Graph Embedding works, which represents how one entity is related to another semantically by some explicit relation.
How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View
However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate.
Ranked #13 on
Link Prediction
on FB15k-237
Learning Local Recurrent Models for Human Mesh Recovery
We conduct a variety of experiments on standard video mesh recovery benchmark datasets such as Human3. 6M, MPI-INF-3DHP, and 3DPW, demonstrating the efficacy of our design of modeling local dynamics as well as establishing state-of-the-art results based on standard evaluation metrics.
Ranked #54 on
3D Human Pose Estimation
on 3DPW
Everybody Is Unique: Towards Unbiased Human Mesh Recovery
Next, we present a simple baseline to address this problem that is scalable and can be easily used in conjunction with existing algorithms to improve their performance.
Ranked #1 on
3D Human Shape Estimation
on SSP-3D
(PVE-T metric)
A Hierarchical Deep Convolutional Neural Network and Gated Recurrent Unit Framework for Structural Damage Detection
Specifically, CNN is utilized to model the spatial relations and the short-term temporal dependencies among sensors, while the output features of CNN are fed into the GRU to learn the long-term temporal dependencies jointly.
An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection
We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes.
Hierarchical Kinematic Human Mesh Recovery
In this work, we address this gap by proposing a new technique for regression of human parametric model that is explicitly informed by the known hierarchical structure, including joint interdependencies of the model.
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
The quality of generated videos outperforms those in existing datasets, validated by user studies.
Training on the test set? An analysis of Spampinato et al. [arXiv:1609.00344]
A recent paper [arXiv:1609. 00344] claims to classify brain processing evoked in subjects watching ImageNet stimuli as measured with EEG and to use a representation derived from this processing to create a novel object classifier.
