8 papers
Odin: Primitive-Level Synchronization for Distributed Point-Based Neural Rendering
Zhenxiang Ma, Zeyu He, Yuanzhen Zhou +6
Point-based neural rendering (PBNR) represents 3D scenes as explicit, trainable primitives and underpins high-quality reconstruction and emerging embodied AI and world-model pipeli…
FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
Huan Wang, Jun Shen, Haoran Li +6
Federated Learning (FL) enables collaborative training of distributed clients while protecting privacy. To enhance generalization capability in FL, prototype-based FL is in the spo…
MacroNav: Multi-Task Context Representation Learning Enables Efficient Navigation in Unknown Environments
Kuankuan Sima, Longbin Tang, Zhenyu Yang +2
Autonomous navigation in unknown environments requires multi-scale spatial understanding that captures geometric details, topological connectivity, and global structure to support…
DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications
Bohao Wang, Zehua Jiang, Zhenyu Yang +9
Domain-specific datasets are the foundation for unleashing artificial intelligence (AI)-driven wireless innovation. Yet existing wireless AI corpora are slow to produce, offer limi…
LandMarkSystem Technical Report
Zhenxiang Ma, Zhenyu Yang, Miao Tao +5
3D reconstruction is vital for applications in autonomous driving, virtual reality, augmented reality, and the metaverse. Recent advancements such as Neural Radiance Fields(NeRF) a…
FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality
Zhengyao Lv, Chenyang Si, Junhao Song +4
In this paper, we present \textbf{\textit{FasterCache}}, a novel training-free strategy designed to accelerate the inference of video diffusion models with high-quality generation.…