Search Results for author:
Found 43 papers, 27 papers with code
AiOS: All-in-One-Stage Expressive Human Pose and Shape Estimation
Expressive human pose and shape estimation (a. k. a.
Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM).
GPAvatar: Generalizable and Precise Head Avatar from Image(s)
Head avatar reconstruction, crucial for applications in virtual reality, online meetings, gaming, and film industries, has garnered substantial attention within the computer vision community.
DiffSHEG: A Diffusion-Based Approach for Real-Time Speech-driven Holistic 3D Expression and Gesture Generation
We propose DiffSHEG, a Diffusion-based approach for Speech-driven Holistic 3D Expression and Gesture generation with arbitrary length.
DPoser: Diffusion Model as Robust 3D Human Pose Prior
Our approach demonstrates considerable enhancements over common uniform scheduling used in image domains, boasting improvements of 5. 4%, 17. 2%, and 3. 8% across human mesh recovery, pose completion, and motion denoising, respectively.
PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction
To make up for the lack of dynamic HOI scenarios in this area, we introduce the BallPlay dataset that contains eight whole-body basketball skills.
HumanTOMATO: Text-aligned Whole-body Motion Generation
This work targets a novel text-driven whole-body motion generation task, which takes a given textual description as input and aims at generating high-quality, diverse, and coherent facial expressions, hand gestures, and body motions simultaneously.
UniPose: Detecting Any Keypoints
This work proposes a unified framework called UniPose to detect keypoints of any articulated (e. g., human and animal), rigid, and soft objects via visual or textual prompts for fine-grained vision understanding and manipulation.
Ranked #1 on
2D Human Pose Estimation
on Human-Art
(using extra training data)
Bridging the Gap between Human Motion and Action Semantics via Kinematic Phrases
The goal of motion understanding is to establish a reliable mapping between motion and action semantics, while it is a challenging many-to-many problem.
Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code
Specifically, in the context of diffusion-based editing, where a source image is edited according to a target prompt, the process commences by acquiring a noisy latent vector corresponding to the source image via the diffusion model.
Ranked #3 on
Text-based Image Editing
on PIE-Bench
FreeMan: Towards Benchmarking 3D Human Pose Estimation under Real-World Conditions
To facilitate the development of 3D pose estimation, we present FreeMan, the first large-scale, multi-view dataset collected under the real-world conditions.
Neural Interactive Keypoint Detection
Click-Pose explores how user feedback can cooperate with a neural keypoint detector to correct the predicted keypoints in an interactive way for a faster and more effective annotation process.
Effective Whole-body Pose Estimation with Two-stages Distillation
Different from the previous self-knowledge distillation, this stage finetunes the student's head with only 20% training time as a plug-and-play training strategy.
Ranked #1 on
2D Human Pose Estimation
on COCO-WholeBody
(using extra training data)
FITS: Modeling Time Series with $10k$ Parameters
In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis.
Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset
In this paper, we present Motion-X, a large-scale 3D expressive whole-body motion dataset.
detrex: Benchmarking Detection Transformers
To address this issue, we develop a unified, highly modular, and lightweight codebase called detrex, which supports a majority of the mainstream DETR-based instance recognition algorithms, covering various fundamental tasks, including object detection, segmentation, and pose estimation.
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion Model
Extensive experiments demonstrate that DiffHOI significantly outperforms the state-of-the-art in regular detection (i. e., 41. 50 mAP) and zero-shot detection.
Ranked #2 on
Zero-Shot Human-Object Interaction Detection
on HICO-DET
(using extra training data)
Human-Object Interaction Detection
Zero-Shot Human-Object Interaction Detection
A Strong and Reproducible Object Detector with Only Public Datasets
This work presents Focal-Stable-DINO, a strong and reproducible object detection model which achieves 64. 6 AP on COCO val2017 and 64. 8 AP on COCO test-dev using only 700M parameters without any test time augmentation.
Ranked #5 on
Object Detection
on COCO minival
(using extra training data)
HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation
While such a plug-and-play approach is appealing, the inevitable and uncertain conflicts between the original images produced from the frozen SD branch and the given condition incur significant challenges for the learnable branch, which essentially conducts image feature editing for condition enforcement.
One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer
It is challenging to perform this task with a single network due to resolution issues, i. e., the face and hands are usually located in extremely small regions.
Ranked #3 on
3D Human Pose Estimation
on UBody
From Knowledge Distillation to Self-Knowledge Distillation: A Unified Approach with Normalized Loss and Customized Soft Labels
We decompose the KD loss and find the non-target loss from it forces the student's non-target logits to match the teacher's, but the sum of the two non-target logits is different, preventing them from being identical.
Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR
Recent DEtection TRansformer-based (DETR) models have obtained remarkable performance.
Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes
Humans have long been recorded in a variety of forms since antiquity.
Introducing Depth into Transformer-based 3D Object Detection
To address the second issue, we introduce an auxiliary learning task called Depth-aware Negative Suppression loss.
FrAug: Frequency Domain Augmentation for Time Series Forecasting
In time series forecasting (TSF), we need to model the fine-grained temporal relationship within time series segments to generate accurate forecasting results given data in a look-back window.
Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information.
Ranked #2 on
2D Human Pose Estimation
on Human-Art
Lite DETR: An Interleaved Multi-Scale Encoder for Efficient DETR
Recent DEtection TRansformer-based (DETR) models have obtained remarkable performance.
ViTKD: Practical Guidelines for ViT feature knowledge distillation
In this paper, we explore the way of feature-based distillation for ViT.
Are Transformers Effective for Time Series Forecasting?
Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task.
Ranked #1 on
Time Series Forecasting
on ETTh1 (96) Univariate
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling
4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications.
DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation
This paper proposes a simple baseline framework for video-based 2D/3D human pose estimation that can achieve 10 times efficiency improvement over existing works without any performance degradation, named DeciWatch.
Ranked #1 on
2D Human Pose Estimation
on JHMDB (2D poses only)
SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos
With a simple yet effective motion-aware fully-connected network, SmoothNet improves the temporal smoothness of existing pose estimators significantly and enhances the estimation accuracy of those challenging frames as a side-effect.
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
In this work, we propose a novel tree-structured wavelet neural network for time series signal analysis, namely T-WaveNet, by taking advantage of an inherent property of various types of signals, known as the dominant frequency range.
Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation
While the average prediction accuracy has been improved significantly over the years, the performance on hard poses with depth ambiguity, self-occlusion, and complex or rare poses is still far from satisfactory.
Ranked #23 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Information Bottleneck Approach to Spatial Attention Learning
Extensive experiments show that the proposed IB-inspired spatial attention mechanism can yield attention maps that neatly highlight the regions of interest while suppressing backgrounds, and bootstrap standard DNN structures for visual recognition tasks (e. g., image classification, fine-grained recognition, cross-domain classification).
Human Pose Regression with Residual Log-likelihood Estimation
In light of this, we propose a novel regression paradigm with Residual Log-likelihood Estimation (RLE) to capture the underlying output distribution.
Ranked #59 on
3D Human Pose Estimation
on Human3.6M
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences.
Ranked #1 on
Time Series Forecasting
on ETTh1 (24) Multivariate
(using extra training data)
Relational Graph Neural Network Design via Progressive Neural Architecture Search
We propose a novel solution to addressing a long-standing dilemma in the representation learning of graph neural networks (GNNs): how to effectively capture and represent useful information embedded in long-distance nodes to improve the performance of nodes with low homophily without leading to performance degradation in nodes with high homophily.
Skimming and Scanning for Untrimmed Video Action Recognition
Video action recognition (VAR) is a primary task of video understanding, and untrimmed videos are more common in real-life scenes.
T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis
Motivated by the fact that usually a small subset of the frequency components carries the primary information for sensor data, we propose a novel tree-structured wavelet neural network for sensor data analysis, namely \emph{T-WaveNet}.
SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach
With the reduced dimensionality of less relevant body areas, the training set distribution within network branches more closely reflects the statistics of local poses instead of global body poses, without sacrificing information important for joint inference.
Ranked #20 on
Monocular 3D Human Pose Estimation
on Human3.6M
DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image
In this paper, we propose a two-stage fully 3D network, namely \textbf{DeepFuse}, to estimate human pose in 3D space by fusing body-worn Inertial Measurement Unit (IMU) data and multi-view images deeply.
Ranked #5 on
3D Human Pose Estimation
on Total Capture
Structure-Aware 3D Hourglass Network for Hand Pose Estimation from Single Depth Image
Experimental results show that the proposed structure-aware 3D hourglass network is able to achieve a mean joint error of 7. 4 mm in MSRA and 8. 9 mm in NYU datasets, respectively.
