283 citations · 510 across the 13 of their papers we have counts for
13 papers
MM-NeRF: Multimodal-Guided 3D Multi-Style Transfer of Neural Radiance Field
Zijiang Yang, Zhongwei Qiu, Chang Xu +1
3D style transfer aims to generate stylized views of 3D scenes with specified styles, which requires high-quality generating and keeping multi-view consistency. Existing methods st…
Learning Structure-Guided Diffusion Model for 2D Human Pose Estimation
Zhongwei Qiu, Qiansheng Yang, Jian Wang +7
One of the mainstream schemes for 2D human pose estimation (HPE) is learning keypoints heatmaps by a neural network. Existing methods typically improve the quality of heatmaps by c…
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending
Xingjian He, Sihan Chen, Fan Ma +7
Large-scale image-text contrastive pre-training models, such as CLIP, have been demonstrated to effectively learn high-quality multimodal representations. However, there is limited…
PSVT: End-to-End Multi-person 3D Pose and Shape Estimation with Progressive Video Transformers
Zhongwei Qiu, Yang Qiansheng, Jian Wang +6
Existing methods of multi-person video 3D human Pose and Shape Estimation (PSE) typically adopt a two-stage strategy, which first detects human instances in each frame and then per…
Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution
Zhongwei Qiu, Huan Yang, Jianlong Fu +3
Video Super-Resolution (VSR) aims to restore high-resolution (HR) videos from low-resolution (LR) videos. Existing VSR techniques usually recover HR frames by extracting pertinent…
Weakly-supervised Pre-training for 3D Human Pose Estimation via Perspective Knowledge
Zhongwei Qiu, Kai Qiu, Jianlong Fu +1
Modern deep learning-based 3D pose estimation approaches require plenty of 3D pose annotations. However, existing 3D datasets lack diversity, which limits the performance of curren…
DMIS: Dynamic Mesh-based Importance Sampling for Training Physics-Informed Neural Networks
Zijiang Yang, Zhongwei Qiu, Dongmei Fu
Modeling dynamics in the form of partial differential equations (PDEs) is an effectual way to understand real-world physics processes. For complex physics systems, analytical solut…
IVT: An End-to-End Instance-guided Video Transformer for 3D Pose Estimation
Zhongwei Qiu, Qiansheng Yang, Jian Wang +1
Video 3D human pose estimation aims to localize the 3D coordinates of human joints from videos. Recent transformer-based approaches focus on capturing the spatiotemporal informatio…
Dynamic Graph Reasoning for Multi-person 3D Pose Estimation
Zhongwei Qiu, Qiansheng Yang, Jian Wang +1
Multi-person 3D pose estimation is a challenging task because of occlusion and depth ambiguity, especially in the cases of crowd scenes. To solve these problems, most existing meth…
Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution
Zhongwei Qiu, Huan Yang, Jianlong Fu +1
Compressed video super-resolution (VSR) aims to restore high-resolution frames from compressed low-resolution counterparts. Most recent VSR approaches often enhance an input frame…
Contrastive Masked Autoencoders are Stronger Vision Learners
Zhicheng Huang, Xiaojie Jin, Chengze Lu +5
Masked image modeling (MIM) has achieved promising results on various vision tasks. However, the limited discriminability of learned representation manifests there is still plenty…
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
Zhicheng Huang, Zhaoyang Zeng, Yupan Huang +3
We study joint learning of Convolutional Neural Network (CNN) and Transformer for vision-language pre-training (VLPT) which aims to learn cross-modal alignments from millions of im…
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
Zhicheng Huang, Zhaoyang Zeng, Bei Liu +2
We propose Pixel-BERT to align image pixels with text by deep multi-modal transformers that jointly learn visual and language embedding in a unified end-to-end framework. We aim to…