214 citations · 218 across the 12 of their papers we have counts for
15 papers
Entropy-Guided k-Guard Sampling for Long-Horizon Autoregressive Video Generation
Yizhao Han, Tianxing Shi, Zhao Wang +6
Autoregressive (AR) architectures have achieved significant successes in LLMs, inspiring explorations for video generation. In LLMs, top-p/top-k sampling strategies work exceptiona…
LiteVGGT: Boosting Vanilla VGGT via Geometry-aware Cached Token Merging
Zhijian Shu, Cheng Lin, Tao Xie +8
3D vision foundation models like Visual Geometry Grounded Transformer (VGGT) have advanced greatly in geometric perception. However, it is time-consuming and memory-intensive for l…
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…
OccTENS: 3D Occupancy World Model via Temporal Next-Scale Prediction
Bu Jin, Songen Gu, Xiaotao Hu +5
In this paper, we propose OccTENS, a generative occupancy world model that enables controllable, high-fidelity long-term occupancy generation while maintaining computational effici…
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…
Epona: Autoregressive Diffusion World Model for Autonomous Driving
Kaiwen Zhang, Zhenyu Tang, Xiaotao Hu +9
Diffusion models have demonstrated exceptional visual quality in video generation, making them promising for autonomous driving world modeling. However, existing video diffusion-ba…
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving
Zebin Xing, Xingyu Zhang, Yang Hu +5
We propose GoalFlow, an end-to-end autonomous driving method for generating high-quality multimodal trajectories. In autonomous driving scenarios, there is rarely a single suitable…
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.…
Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration
Junyuan Deng, Wei Yin, Xiaoyang Guo +5
In this paper, we present DM-Calib, a diffusion-based approach for estimating pinhole camera intrinsic parameters from a single input image. Monocular camera calibration is essenti…
DOME: Taming Diffusion Model into High-Fidelity Controllable Occupancy World Model
Songen Gu, Wei Yin, Bu Jin +5
We propose DOME, a diffusion-based world model that predicts future occupancy frames based on past occupancy observations. The ability of this world model to capture the evolution…
ComDrive: Comfort-Oriented End-to-End Autonomous Driving
Junming Wang, Xingyu Zhang, Zebin Xing +7
We propose ComDrive: the first comfort-oriented end-to-end autonomous driving system to generate temporally consistent and comfortable trajectories. Recent studies have demonstrate…
OccRWKV: Rethinking Efficient 3D Semantic Occupancy Prediction with Linear Complexity
Junming Wang, Wei Yin, Xiaoxiao Long +4
3D semantic occupancy prediction networks have demonstrated remarkable capabilities in reconstructing the geometric and semantic structure of 3D scenes, providing crucial informati…
DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos
Linhan Wang, Kai Cheng, Shuo Lei +5
We present DC-Gaussian, a new method for generating novel views from in-vehicle dash cam videos. While neural rendering techniques have made significant strides in driving scenario…
Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation
Mu Hu, Wei Yin, Chi Zhang +7
We introduce Metric3D v2, a geometric foundation model for zero-shot metric depth and surface normal estimation from a single image, which is crucial for metric 3D recovery. While…
GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image
Xiao Fu, Wei Yin, Mu Hu +6
We introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes, e.g., depth and normals, from single images. While significant research has…