Fysiverse-3D-Vision Technical Report: Generating Executable 3D Worlds from Images through Unified Spatial Reasoning

arXiv:2609.25741 · cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.CV

Submitted: 2026-09-22

Updated: 2026-09-22

Comments: Fysics AI Technical Report

Code: https://github.com/Fysics-AI/Fysiverse-3D-Vision

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Consistency — 10× in this paper · explained in 3 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Transformer — 7× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Foundation Model — 6× in this paper · explained in 3 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• Affordance — 5× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Decoupling — 2× in this paper · explained in 2 episodes
• Embodied AI — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• 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
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Ming-Omni: A Unified Multimodal Model for Perception and Generation
• PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image
• BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
• SAM 3D: 3Dfy Anything in Images
• OmniFysics: Towards Physical Intelligence Evolution via Omni-Modal Signal Processing and Network Optimization
• Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images
• G$^2$VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning
• I-Scene: 3D Instance Models are Implicit Generalizable Spatial Learners
• SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects
• DreamArt: Generating Interactable Articulated Objects from a Single Image
• SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass
• SceneMaker: Open-set 3D Scene Generation with Decoupled De-occlusion and Pose Estimation Model
• LongCat-Flash-Omni Technical Report
• Qwen3.5-Omni Technical Report
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• $\pi^3$: Permutation-Equivariant Visual Geometry Learning
• SAGE: Scalable Agentic 3D Scene Generation for Embodied AI
• Native and Compact Structured Latents for 3D Generation
• UniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding
• Omni123: Exploring 3D Native Foundation Models with Limited 3D Data by Unifying Text to 2D and 3D Generation

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