Hugging Face Trending Papers
Fast-Slow Evolutionary Occupancy Prediction via Controlled Dynamics
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arXiv Computer Vision
Sep 4
Drive-HWM: Hierarchical World Models for Dynamic-Latent Guided Autonomous Driving
Drive‑HWM introduces a hierarchical slow‑fast world modeling framework for autonomous driving. The slow model predicts multi‑step future representations, while the fast model jointly predicts the next frame and immediate action using a lightweight multimodal backbone and an autoregressive expert. Dynamic‑Aware Latents, learned through optical‑flow prediction, explicitly capture motion dynamics, and experiments on NAVSIM v1 and v2 show strong driving performance with validated ablation studies.
arXiv Computer Vision
Oct 8
LighTROcc: Lightweight 4D Occupancy Forecasting via Instance-Centric 3D Gaussians
arXiv:2610.09444v1 Announce Type: new Abstract: Forecasting future 3D occupancy from surround-view cameras is essential for autonomous driving, yet existing approaches rely on dense voxel or bird's-e...
arXiv Computer Vision
Oct 9
VGGTWorld-VLA: Intent-Conditioned 3D World Evolution for Autonomous Driving
VGGTWorld-VLA extends the VGGT-World model by adding intention-conditioned control to 3D world evolution for autonomous driving. It introduces an action–semantic conditioning mechanism that injects driving semantics and ego‑motion into the future‑token stream, allowing different future geometry predictions for the same observed scene under alternative actions. A geometry–language–action bridge further integrates historical geometry, VLA semantic features, and maneuver/trajectory representations to jointly condition future geometry prediction, achieving competitive performance on NAVSIM and demonstrating the benefits of semantic and action conditioning.
arXiv Computer Vision
Sep 23
VEOcc: Voxel-Centric Online Semantic Occupancy Prediction For Embodied Scene Understanding
arXiv:2605.25059v4 Announce Type: replace Abstract: Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Em...
arXiv Computer Vision
Oct 21
HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation
HERMES++ is a unified driving world model that simultaneously performs 3D scene understanding and future geometry prediction. It uses a BEV representation to merge multi‑view spatial data for LLM compatibility, introduces LLM‑enhanced world queries for knowledge transfer, and employs a Current‑to‑Future Link to condition future geometry on semantic context. A Joint Geometric Optimization strategy enforces structural integrity by combining explicit geometric constraints with implicit latent regularization, and extensive benchmark tests show that HERMES++ outperforms specialist methods in both point cloud prediction and scene understanding.
Hugging Face Trending Papers
Jul 27
InterOCF: Spatio-Temporal 2D-3D Interaction for Camera-Only 4D Occupancy Forecasting
Camera-only 4D occupancy forecasting enables autonomous vehicles to predict future 3D semantic scenes solely from historical multi-view images, which is critical for driving safety. Even though current methods have achieved good performance, the strong spatial-temporal modeling between the input multi-view frames is still underexplored, which limits the performance of those methods in future 4D forecasting.