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Drive3R: Block-wise Streaming Geometric Foundation Models for Autonomous Driving
Tianyi Yang, Haojun Yang, Jipeng Xiao +4
High-fidelity closed-loop simulation for autonomous driving increasingly relies on dense multi-view 3D reconstruction. While recent feed-forward geometric foundation models have re…
Spatial Memory Intelligence: Endowing World Models with Understanding-Driven Long-Term Memory
Ying Yang, Guiyu Zhang, Lianghua Huang +5
Long-video generation and world models have shown strong potential for interactive entertainment and embodied simulation by predicting future observations conditioned on user actio…
SolarWM: Open Data and Scalable Training for Long-Horizon Video World Models
Junchao Huang, Guian Fang, Shengju Qian +15
We introduce SolarWM, a fully open foundation for building interactive video world models from data preparation through long-horizon inference. Training across heterogeneous data s…
OptiGeo: Efficient Monocular Geometry for Embodied Perception in Optically Challenging Scenes
Muxin Liu, Tianbo Liu, Jing Xia +7
Monocular depth estimation has achieved strong open-domain generalization, yet reliable robotic deployment remains difficult in transparent, reflective, and specular environments,…
FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry
Muxin Liu, Xiaoyang Lyu, Tianhe Ren +7
We present FoundationGeo, a two-stage framework that explicitly bridges relative and metric prediction via spatial calibration and principled data design. Stage 1 learns a high-fid…
DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Driving
Chen Shi, Jinrui Xu, Shaoshuai Shi +3
Pretrained foundation models have become an important basis for end-to-end autonomous driving. In contrast to vision-language models pretrained primarily on static image-text pairs…
Stabilizing Streaming Video Geometry via Dynamic Feature Normalization
Xiaoyang Lyu, Muxin Liu, Xiaoshan Wu +5
Consistent 3D geometry estimation from streaming RGB input is crucial for real-world applications such as autonomous driving, embodied AI, and large-scale reconstruction. While mod…
LIVE: Long-horizon Interactive Video World Modeling
Junchao Huang, Ziyang Ye, Xinting Hu +5
Autoregressive video world models predict future visual observations conditioned on actions. While effective over short horizons, these models often struggle with long-horizon gene…
GeoPredict: Leveraging Predictive Kinematics and 3D Gaussian Geometry for Precise VLA Manipulation
Jingjing Qian, Boyao Han, Chen Shi +4
Vision-Language-Action (VLA) models achieve strong generalization in robotic manipulation but remain largely reactive and 2D-centric, making them unreliable in tasks that require p…
UniSplat: Unified Spatio-Temporal Fusion via 3D Latent Scaffolds for Dynamic Driving Scene Reconstruction
Chen Shi, Shaoshuai Shi, Xiaoyang Lyu +4
Feed-forward 3D reconstruction for autonomous driving has advanced rapidly, yet existing methods struggle with the joint challenges of sparse, non-overlapping camera views and comp…
Memory Forcing: Spatio-Temporal Memory for Consistent Scene Generation on Minecraft
Junchao Huang, Xinting Hu, Boyao Han +4
Autoregressive video diffusion models have proved effective for world modeling and interactive scene generation, with Minecraft gameplay as a representative application. To faithfu…
Enhancing Spatial Reasoning in Multimodal Large Language Models through Reasoning-based Segmentation
Zhenhua Ning, Zhuotao Tian, Shaoshuai Shi +4
Recent advances in point cloud perception have demonstrated remarkable progress in scene understanding through vision-language alignment leveraging large language models (LLMs). Ho…
Proteus-ID: ID-Consistent and Motion-Coherent Video Customization
Guiyu Zhang, Chen Shi, Zijian Jiang +4
Video identity customization seeks to synthesize realistic, temporally coherent videos of a specific subject, given a single reference image and a text prompt. This task presents t…
DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving
Chen Shi, Shaoshuai Shi, Kehua Sheng +2
Data-driven learning has advanced autonomous driving, yet task-specific models struggle with out-of-distribution scenarios due to their narrow optimization objectives and reliance…
JiSAM: Alleviate Labeling Burden and Corner Case Problems in Autonomous Driving via Minimal Real-World Data
Runjian Chen, Wenqi Shao, Bo Zhang +3
Deep-learning-based autonomous driving (AD) perception introduces a promising picture for safe and environment-friendly transportation. However, the over-reliance on real labeled d…
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
Xiaosong Jia, Shaoshuai Shi, Zijun Chen +4
As an essential task in autonomous driving (AD), motion prediction aims to predict the future states of surround objects for navigation. One natural solution is to estimate the pos…