arXiv Computer Vision
Future Dynamic 3D Reconstruction: Toward 3D World Modeling with Disentangled Ego-Motion
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arXiv AI
Jun 4
Beyond Pixel Histories: World Models with Persistent 3D State
arXiv:2603. 03482v2 Announce Type: replace-cross Abstract: Interactive world models continually generate video by responding to a user's actions, enabling open-ended generation capabilities.
arXiv Computer Vision
5d ago
HarmoHOI: Harmonizing Appearance and 3D Motion for Multi-view Hand-Object Interaction Synthesis
arXiv:2607. 17097v2 Announce Type: replace Abstract: Hand-Object Interaction (HOI) synthesis is a cornerstone for animation production and embodied AI.
Hugging Face Trending Papers
Jun 11
MoVerse: Real-Time Video World Modeling with Panoramic Gaussian Scaffold
We present MoVerse, a real-time video world model that creates an interactively navigable scene from a single narrow-field-of-view image. This setting is challenging because the input observes only a small fraction of the environment, while interactive roaming requires a complete surrounding world, persistent geometry, controllable camera motion, and temporally coherent high-fidelity observations.
arXiv AI
Jun 2
Real2SAM2Real: Generative 3D Caches as Complementary Context for Video Diffusion
arXiv:2606. 00299v1 Announce Type: cross Abstract: While Video Diffusion Models (VDMs) excel at synthesizing high-fidelity videos, enabling precise camera and scene control remains challenging.
arXiv AI
Jul 3
Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
arXiv:2605. 00412v3 Announce Type: replace Abstract: World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and model-based reinforcement learning.
arXiv Computer Vision
2d ago
Driving with DINO: Vision Foundation Features as a Unified Bridge for Sim-to-Real Generation in Autonomous Driving
The paper introduces Driving with DINO (DwD), a framework that uses Vision Foundation Module (VFM) features to bridge simulation and real-world domains for autonomous driving video generation. It addresses the consistency‑realism dilemma by projecting VFM features onto a principal subspace, dropping high‑frequency texture elements, and applying a Random Channel Tail Drop to preserve structural detail. Additional components— a learnable Spatial Alignment Module and a Causal Temporal Aggregator— enhance control precision, spatial alignment, and temporal stability, reducing motion blur and ensuring realistic, consistent outputs.