2 citations · 2 across the 12 of their papers we have counts for
14 papers
Q-BridgeNet: A Quantization Network for Cross-Lingual Sign Language Translation
Liqian Feng, Lintao Wang, Xiaochen Liu +5
Most sign language translation (SLT) methods focus on isolated native sign-spoken pairs (e.g., American Sign Language - English). Extending language-specific SLT models to multilin…
KAN Text to Vision? The Exploration of Kolmogorov-Arnold Networks for Multi-Scale Sequence-Based Pose Animation from Sign Language Notation
Guanyi Du, Lintao Wang, Kun Hu +1
Sign language production from symbolic notation offers a scalable route to accessible sign animation. We present KANMultiSign, a multi-scale sequence generator that translates HamN…
Stable Attention Response for Reliable Precipitation Nowcasting
Penghui Wen, Zexin Hu, Sen Zhang +6
Precipitation nowcasting remains challenging due to the highly localized, rapidly evolving, and heterogeneous nature of atmospheric dynamics. Although recent methods increasingly a…
OrthoEraser: Coupled-Neuron Orthogonal Projection for Concept Erasure
Chuancheng Shi, Wenhua Wu, Fei Shen +3
Text-to-image (T2I) models face significant safety risks from adversarial induction, yet current concept erasure methods often cause collateral damage to benign attributes when sup…
Pb4U-GNet: Resolution-Adaptive Garment Simulation via Propagation-before-Update Graph Network
Aoran Liu, Kun Hu, Clinton Ansun Mo +3
Garment simulation is fundamental to various applications in computer vision and graphics, from virtual try-on to digital human modelling. However, conventional physics-based metho…
Text2Sign Diffusion: A Generative Approach for Gloss-Free Sign Language Production
Liqian Feng, Lintao Wang, Kun Hu +2
Sign language production (SLP) aims to translate spoken language sentences into a sequence of pose frames in a sign language, bridging the communication gap and promoting digital i…
PUMPS: Skeleton-Agnostic Point-based Universal Motion Pre-Training for Synthesis in Human Motion Tasks
Clinton Ansun Mo, Kun Hu, Chengjiang Long +3
Motion skeletons drive 3D character animation by transforming bone hierarchies, but differences in proportions or structure make motion data hard to transfer across skeletons, posi…
Extended Short- and Long-Range Mesh Learning for Fast and Generalized Garment Simulation
Aoran Liu, Kun Hu, Clinton Mo +2
3D garment simulation is a critical component for producing cloth-based graphics. Recent advancements in graph neural networks (GNNs) offer a promising approach for efficient garme…
DC-PCN: Point Cloud Completion Network with Dual-Codebook Guided Quantization
Qiuxia Wu, Haiyang Huang, Kunming Su +2
Point cloud completion aims to reconstruct complete 3D shapes from partial 3D point clouds. With advancements in deep learning techniques, various methods for point cloud completio…
RI-MAE: Rotation-Invariant Masked AutoEncoders for Self-Supervised Point Cloud Representation Learning
Kunming Su, Qiuxia Wu, Panpan Cai +4
Masked point modeling methods have recently achieved great success in self-supervised learning for point cloud data. However, these methods are sensitive to rotations and often exh…
SITransformer: Shared Information-Guided Transformer for Extreme Multimodal Summarization
Sicheng Liu, Lintao Wang, Xiaogang Zhu +3
Extreme Multimodal Summarization with Multimodal Output (XMSMO) becomes an attractive summarization approach by integrating various types of information to create extremely concise…
Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Imaging
Wenhua Wu, Kun Hu, Wenxi Yue +5
Knee osteoarthritis (KOA), a common form of arthritis that causes physical disability, has become increasingly prevalent in society. Employing computer-aided techniques to automati…
Radio Frequency Signal based Human Silhouette Segmentation: A Sequential Diffusion Approach
Penghui Wen, Kun Hu, Dong Yuan +3
Radio frequency (RF) signals have been proved to be flexible for human silhouette segmentation (HSS) under complex environments. Existing studies are mainly based on a one-shot app…
SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation
Wenxi Yue, Jing Zhang, Kun Hu +5
The Segment Anything Model (SAM) exhibits promise in generic object segmentation and offers potential for various applications. Existing methods have applied SAM to surgical instru…