Bootstrapping Video Interaction Generation with Synthetic State Transitions

arXiv:2610.01039 · cs.CV · Submitted 2026-10-01 · Read on arXiv

cs.CV

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/black-forest-labs/flux

Terminology

• State-Guided Sampling — 10× in this paper
• Consistency — 9× in this paper · explained in 3 episodes
• Generative Model — 9× in this paper · explained in 2 episodes
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Diffusion Models — 8× in this paper · explained in 6 episodes
• Fidelity — 7× in this paper · explained in 2 episodes
• Generative Models — 7× in this paper · explained in 2 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Actor — 4× in this paper · explained in 3 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Plausibility Probe — 3× in this paper
• Quality Classifier — 3× in this paper
• Temporal consistency — 3× in this paper · explained in 2 episodes
• World Model — 3× in this paper · explained in 7 episodes
• AUC — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Action Flow — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Dynamic Weighting — 1× in this paper · explained in 3 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes
• Vision-Language Model — 1× in this paper
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Cosmos World Foundation Model Platform for Physical AI
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generation
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• Intuitive physics understanding emerges from self-supervised pretraining on natural videos
• AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
• Classifier-Free Diffusion Guidance
• CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers
• Diffusion Models for Video Prediction and Infilling
• GPT-4o System Card
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
• Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
• Flow Matching for Generative Modeling
• Large Language Models have Intrinsic Self-Correction Ability
• Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
• Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency
• Latte: Latent Diffusion Transformer for Video Generation
• Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation

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