2 citations · 2 across the 8 of their papers we have counts for
9 papers
Omni-LiveAvatar: Minute-Level Real-Time Streaming Joint Audio-Video Avatar Generation
Lunjie Zhu, Xingtong Ge, Fangyu Lin +7
Joint audio-video generative models serve as foundation for immersive and interactive digital-human generation. Nevertheless, most existing models rely on bidirectional attention a…
Generation Models Know Space: Unleashing Implicit 3D Priors for Scene Understanding
Xianjin Wu, Dingkang Liang, Tianrui Feng +5
While Multimodal Large Language Models demonstrate impressive semantic capabilities, they often suffer from spatial blindness, struggling with fine-grained geometric reasoning and…
Flash-VAED: Plug-and-Play VAE Decoders for Efficient Video Generation
Lunjie Zhu, Yushi Huang, Xingtong Ge +5
Latent diffusion models have enabled high-quality video synthesis, yet their inference remains costly and time-consuming. As diffusion transformers become increasingly efficient, t…
Video4Edit: Viewing Image Editing as a Degenerate Temporal Process
Xiaofan Li, Yanpeng Sun, Chenming Wu +5
We observe that recent advances in multimodal foundation models have propelled instruction-driven image generation and editing into a genuinely cross-modal, cooperative regime. Nev…
U-ViLAR: Uncertainty-Aware Visual Localization for Autonomous Driving via Differentiable Association and Registration
Xiaofan Li, Zhihao Xu, Chenming Wu +11
Accurate localization using visual information is a critical yet challenging task, especially in urban environments where nearby buildings and construction sites significantly degr…
DriVerse: Navigation World Model for Driving Simulation via Multimodal Trajectory Prompting and Motion Alignment
Xiaofan Li, Chenming Wu, Zhao Yang +5
This paper presents DriVerse, a generative model for simulating navigation-driven driving scenes from a single image and a future trajectory. Previous autonomous driving world mode…
UniFuture: A 4D Driving World Model for Future Generation and Perception
Dingkang Liang, Dingyuan Zhang, Xin Zhou +7
We present UniFuture, a unified 4D Driving World Model designed to simulate the dynamic evolution of the 3D physical world. Unlike existing driving world models that focus solely o…
The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey
Sifan Tu, Xin Zhou, Dingkang Liang +4
The Driving World Model (DWM), which focuses on predicting scene evolution during the driving process, has emerged as a promising paradigm in the pursuit of autonomous driving (AD)…
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
Dingkang Liang, Tianrui Feng, Xin Zhou +3
Recently, leveraging pre-training techniques to enhance point cloud models has become a prominent research topic. However, existing approaches typically require full fine-tuning of…