3D & World Models

DreamX repurposes a pretrained video diffusion model to extract compact latent actions that reconstruct future video and transfer across human and robot embodiments.

The paper finds that frozen video-world-model representations expose modest physical-label information and action sensitivity, but native prediction errors are strongly affected by appearance and static content and can rank impossible events below possible ones.

Wizard is an agentic game world model that decouples explicit game-state control from video generation to enable real-time, zero-shot control of scenes, characters, and motions.

HiFOR couples multi-horizon future prediction (short-term state, semantic milestone, goal) with action-guided retrieval from a structured latent world to condition a flow-matching VLA policy during denoising.

ReLift combines frozen 2D tracks and pretrained geometry into lifted 3D trajectories, then uses a feed-forward trajectory corrector trained on simulated lifts to repair errors and complete occluded motion.

The paper functionally probes ten frozen visual foundation models across four spatial capabilities and finds that spatial readout, use of history and motion, predictive transfer, and goal-conditioned action utility dissociate across tasks, layers, and prefix lengths.

L3G-World combines an online-updated latent 3D Gaussian memory with a memory-conditioned video DiT to generate spatially consistent worlds in real time.

HairDeck generates stylized hair cards and spring-bone chains from a single image or point cloud by encoding each card as one token and denoising all tokens together with a set diffusion transformer.

OmniShowcase generates omnidirectional subject-consistent videos from multi-view reference images by combining a 3D-asset-based data synthesis pipeline with surface-normal turntable conditioning reconstructed from the references.