37 citations · 74 across the 12 of their papers we have counts for
13 papers
Moaw: Unleashing Motion Awareness for Video Diffusion Models
Tianqi Zhang, Ziyi Wang, Wenzhao Zheng +5
Video diffusion models, trained on large-scale datasets, naturally capture correspondences of shared features across frames. Recent works have exploited this property for tasks suc…
Geometry-Aware Single-Image 4D Synthesis via Dense Trajectory Generation
Yanran Zhang, Ziyi Wang, Wenzhao Zheng +3
Generating interactive and dynamic 4D scenes from a single static image remains a core challenge. Most existing generate-then-reconstruct and reconstruct-then-generate methods deco…
Vision Generalist Model: A Survey
Ziyi Wang, Yongming Rao, Shuofeng Sun +8
Recently, we have witnessed the great success of the generalist model in natural language processing. The generalist model is a general framework trained with massive data and is a…
UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting
Ziyi Wang, Yanran Zhang, Jie Zhou +1
The scale diversity of point cloud data presents significant challenges in developing unified representation learning techniques for 3D vision. Currently, there are few unified 3D…
OGGSplat: Open Gaussian Growing for Generalizable Reconstruction with Expanded Field-of-View
Yanbo Wang, Ziyi Wang, Wenzhao Zheng +2
Reconstructing semantic-aware 3D scenes from sparse views is a challenging yet essential research direction, driven by the demands of emerging applications such as virtual reality…
XMask3D: Cross-modal Mask Reasoning for Open Vocabulary 3D Semantic Segmentation
Ziyi Wang, Yanbo Wang, Xumin Yu +2
Existing methodologies in open vocabulary 3D semantic segmentation primarily concentrate on establishing a unified feature space encompassing 3D, 2D, and textual modalities. Nevert…
Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models
Ziyi Wang, Xumin Yu, Yongming Rao +2
With the overwhelming trend of mask image modeling led by MAE, generative pre-training has shown a remarkable potential to boost the performance of fundamental models in 2D vision.…
AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers
Xumin Yu, Yongming Rao, Ziyi Wang +2
In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr, which adopts a Transforme…
P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting
Ziyi Wang, Xumin Yu, Yongming Rao +2
Nowadays, pre-training big models on large-scale datasets has become a crucial topic in deep learning. The pre-trained models with high representation ability and transferability a…
SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation
Ziyi Wang, Yongming Rao, Xumin Yu +2
Conventional point cloud semantic segmentation methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric informat…
PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
Xumin Yu, Yongming Rao, Ziyi Wang +3
Point clouds captured in real-world applications are often incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete poin…
Towards Interpretable Deep Metric Learning with Structural Matching
Wenliang Zhao, Yongming Rao, Ziyi Wang +2
How do the neural networks distinguish two images? It is of critical importance to understand the matching mechanism of deep models for developing reliable intelligent systems for…
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds
Yi Wei, Ziyi Wang, Yongming Rao +2
In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. Since point clouds are irregular and unorder…