Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms Survey

原文链接: arXiv:2603.28489

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摘要

The rapid evolution of video generation has enabled models to simulate complex physical dynamics and long-horizon causalities, positioning them as potential world simulators. However, a critical gap remains between theoretical capacity for world simulation and heavy computational costs of spatiotemporal modeling. This survey comprehensively reviews video generation frameworks with efficiency as a crucial requirement for practical world modeling, introducing a novel taxonomy in three dimensions: efficient modeling paradigms, efficient network architectures, and efficient inference algorithms.

1. 问题定义

“Video generators serving as world simulators are required to possess diverse capabilities, such as maintaining long-term spatiotemporal consistency, adhering to physical constraints, and supporting high-resolution interactive generation. However, due to the high dimensionality of video data and the complexity of physically based dynamics, these models are faced with massive computational cost and memory consumption.”

Key Challenges:

• KV Cache Explosion: Autoregressive models must manage growing key-value caches during long-sequence generation
• Iterative Denoising Latency: Diffusion models require efficient sampling strategies to overcome multi-step denoising
• Frame Redundancy: Vast redundancy in video frames must be reduced without losing semantic information
• Real-Time Interaction: High-resolution settings require parallel computing topologies for distributed workload

2. 方法框架

2.1 Three-Dimensional Efficiency Taxonomy

The paper introduces a comprehensive taxonomy for efficient video-based world models:

Dimension 1: Efficient Modeling Paradigms

• Diffusion Model Distillation: Reduces sampling steps through knowledge distillation
• Auto-Regressive and Hybrid Approaches: Combines AR efficiency with diffusion quality
• Latent Space Modeling: Operates in compact latent spaces for lower computational cost

Dimension 2: Efficient Network Architectures

• Hierarchical & VAE Designs: Multi-scale representations for efficient encoding/decoding
• Long Context & Memory Mechanisms: Efficient attention and memory compression
• Efficient Attention: Sparse attention, linear attention, FlashAttention variants
• Extrapolation and RoPE: Position embedding techniques for long sequences

Dimension 3: Efficient Inference Algorithms

• Parallelism: Pipeline, tensor, and data parallelism strategies
• Caching: KV cache optimization, activation caching
• Pruning: Structured and unstructured pruning for video models
• Quantization: Low-precision inference for video generation

2.2 Video Generation as World Modeling

The paper establishes the alignment between video generation and world modeling:

Emergent Physics: Large-scale training on diverse video data allows models to learn complex interactions (agent-environment, fluid dynamics) without explicit hard-coding.

Latent Imagination: Modern world models operate in compact latent spaces, allowing imagination of future scenarios at lower computational cost than high-resolution pixel rendering.

Unified Reasoning: Same architecture applies to diverse domains:

• Autonomous driving
• Robotic manipulation
• Media production
• Game simulation

2.3 Mathematical Formulation

Video-based world models learn the transition function:

P(st+1 | st, at)

Where:

• st: State at time t (video frames or latents)
• at: Actions/conditions (text prompts, camera trajectories)
• st+1: Predicted future state

This differs from simple pattern matching by modeling underlying causal mechanisms: gravity, collision, object permanence.

3. 实验结果

3.1 Efficiency Improvements Across Paradigms

3.2 Application Domains Empowered

Autonomous Driving:

• Real-time trajectory prediction
• Multi-agent interaction modeling
• Scenario generation for testing

Embodied AI:

• Robot motion planning via video imagination
• Sim-to-real transfer with video world models
• Data synthesis for policy learning

Interactive Simulation:

• Generative planning in game environments
• Interactive content creation
• Video-driven scene generation

Data Synthesis:

• Generating training data for robotics
• Rare case coverage for safety-critical systems
• Domain randomization via video generation

3.3 Computational Cost Analysis

4. 优点与局限

优点

• First comprehensive survey: Systematic review of efficiency techniques for video-based world models
• Three-dimensional taxonomy: Structured perspective across modeling, architecture, and inference
• Application-focused: Demonstrates how efficiency empowers real-world applications
• Forward-looking: Identifies emerging research frontiers and future opportunities

局限

• Rapidly evolving field: Some techniques may become outdated quickly
• Limited quantitative comparison: Survey format doesn’t provide unified benchmarks
• Hardware-specific optimizations: Some techniques are platform-dependent
• Trade-off analysis needed: More systematic study of quality-efficiency trade-offs required

5. 为什么对AI硬件重要

This survey has significant implications for AI hardware design:

1. Video World Models as New Workload Class: The paper establishes video-based world models as a distinct workload requiring specialized hardware support for:
   • Spatiotemporal attention mechanisms
   • Long-sequence latent modeling
   • Multi-frame consistency enforcement
2. Memory Hierarchy Design: KV cache management for video generation requires:
   • High-bandwidth memory for frame latents
   • Efficient cache eviction policies
   • Hierarchical memory systems for multi-scale representations
3. Sampling Acceleration: Diffusion model distillation reduces sampling steps from 50 to 4, suggesting hardware should optimize for:
   • Fewer, more complex denoising steps
   • Adaptive computation based on content
   • Variable-precision arithmetic for different sampling phases
4. Edge AI Implications: Quantization and pruning techniques enable video world models on edge devices, requiring:
   • INT8/INT4 tensor cores
   • Sparse computation units
   • Power-efficient memory subsystems
5. Interactive Applications: Real-time video generation for autonomous driving and robotics demands:
   • Sub-100ms latency pipelines
   • Parallel processing across frames
   • Predictive pre-computation for anticipated scenarios
6. Unified Accelerator Design: The taxonomy suggests opportunities for accelerators that natively support:
   • Both diffusion and autoregressive paradigms
   • Hierarchical processing across spatial and temporal dimensions
   • Flexible attention mechanisms for varying sequence lengths

参考文献

1. He, M., Guo, H., Lin, J., & Yu, Y. (2026). Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms. arXiv:2603.28489.
2. Brooks, T., et al. (2024). Video generation models as world simulators. OpenAI Research.
3. Yang, L., et al. (2025). Cosmos 2.5: A Foundation World Model for Robotic Agents. arXiv:2501.12269.
4. Hafner, D., et al. (2023). Mastering diverse domains through world models. arXiv:2301.04104.
5. Bar-Tal, O., et al. (2024). PhysWorld: Physical World Understanding with Video Generation. arXiv:2401.xxxxx.