Proxy2World: Learning to Generate Worlds From Lightweight Proxies without Seeing Them

arXiv:2609.35023 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Project page: https://dumdumgura.github.io/proxy2world

Terminology

• Alignment — 10× in this paper · explained in 1 episode
• Diffusion Model — 10× in this paper · explained in 8 episodes
• Grounding — 10× in this paper · explained in 3 episodes
• Diffusion Models — 8× in this paper · explained in 6 episodes
• World Model — 8× in this paper · explained in 7 episodes
• Abstraction — 5× in this paper · explained in 1 episode
• Consistency — 4× in this paper · explained in 3 episodes
• Flow Matching — 4× in this paper · explained in 8 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Structural Control — 2× in this paper · explained in 2 episodes
• ACM Transactions on Graphics — 1× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition Conference — 1× in this paper
• Embodied AI — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• In IEEE International Conference on Computer Vision — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes

Sources

• Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors
• Video Models as Native 4D Renderers: World-Grounded Conditioning from Animated Mesh
• Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning
• Code World Model: Coding Agent as World Brain
• Image Generators are Generalist Vision Learners
• Coarse-to-Real: Generative Rendering for Populated Dynamic Scenes
• CameraCtrl: Enabling Camera Control for Text-to-Video Generation
• Training-free Camera Control for Video Generation
• ViPE: Video Pose Engine for 3D Geometric Perception
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• Depth Anything 3: Recovering the Visual Space from Any Views
• OmniDirector: General Multi-Shot Camera Cloning without Cross-Paired Data
• Lyra 2.0: Explorable Generative 3D Worlds
• Gemini: A Family of Highly Capable Multimodal Models
• Wan: Open and Advanced Large-Scale Video Generative Models
• Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling

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