1 citations · 1 across the 6 of their papers we have counts for
8 papers
WALT: Learning World-Model-Aligned Latent Trajectories for Autonomous Driving
Mingkai Jia, Jiaxin Guo, Zhijian Shu +5
Driving world models learn rich predictive representations of the surrounding environment from visual observations, yet accurate visual prediction does not necessarily translate in…
DINO-Tok: Adapting DINO for Visual Tokenizers
Mingkai Jia, Mingxiao Li, Zhijian Shu +12
Recent advances in visual generation have emphasized the importance of Latent Generative Models (LGMs), which critically depend on effective visual tokenizers to bridge pixels and…
3D and 4D World Modeling: A Survey
Lingdong Kong, Yu Yang, Jianbiao Mei +20
World modeling has become a cornerstone in AI research, enabling agents to understand, represent, and predict the dynamic environments they inhabit. While prior work largely emphas…
DCReg: Decoupled Characterization for Efficient Degenerate LiDAR Registration
Xiangcheng Hu, Xieyuanli Chen, Mingkai Jia +3
LiDAR point cloud registration is fundamental to robotic perception and navigation. In geometrically degenerate environments (e.g., corridors), registration becomes ill-conditioned…
2D Gaussians Meet Visual Tokenizer
Yiang Shi, Xiaoyang Guo, Wei Yin +5
The image tokenizer is a critical component in AR image generation, as it determines how rich and structured visual content is encoded into compact representations. Existing quanti…
MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Mingkai Jia, Wei Yin, Xiaotao Hu +5
Vector Quantized Variational Autoencoders (VQ-VAEs) are fundamental models that compress continuous visual data into discrete tokens. Existing methods have tried to improve the qua…
DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT
Xiaotao Hu, Mingkai Jia, Xiaoyang Guo +3
Recent successes in autoregressive (AR) generation models, such as the GPT series in natural language processing, have motivated efforts to replicate this success in visual tasks.…
MapEval: Towards Unified, Robust and Efficient SLAM Map Evaluation Framework
Xiangcheng Hu, Jin Wu, Mingkai Jia +6
Evaluating massive-scale point cloud maps in Simultaneous Localization and Mapping (SLAM) remains challenging, primarily due to the absence of unified, robust and efficient evaluat…