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  • 4DWorldBench: A Comprehensive Evaluation Framework for 3D 4D World . . .
    However, prior benchmarks emphasize different evaluation dimensions and lack a unified assessment of world-realism capability To systematically evaluate World Models, we introduce the 4DWorldBench, which measures models across four key dimensions: Perceptual Quality, Condition–4D Alignment, Physical Realism, and 4D Consistency
  • 4DWorldBench: A Comprehensive Evaluation Framework
    However, prior benchmarks emphasize different evaluation dimensions and lack a unified assessment of world-realism capability To systematically evaluate World Models, we introduce the 4DWorldBench, which measures models across four key dimensions: Perceptual Quality, Condition-4D Alignment, Physical Realism, and 4D Consistency
  • WorldLens: Full-Spectrum Evaluations of Driving World Models in Real . . .
    View recent discussion Abstract: Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally Despite rapid progress, the field still lacks a unified way to assess whether generated worlds preserve geometry, obey physics, or support reliable control We introduce WorldLens, a
  • A Comprehensive Survey on World Models for Embodied AI
    Furthermore, we offer a quantitative comparison of state-of-the-art models and distill key open challenges, including the scarcity of unified datasets and the need for evaluation metrics that assess physical consistency over pixel fidelity, the trade-off between model performance and the computational efficiency required for real-time control
  • WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
    This metric captures such instability by measuring temporal variation in depth embeddings extracted from consecutive frames, providing a geometric complement to perceptual fidelity metrics
  • WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
    Human Preference capturing subjective scores such as world realism, physical plausibility, and behavioral safety through large-scale human annotations Our study reveals that models with strong geometric consistency are generally rated as more "real", confirming that perceptual fidelity is inseparable from structural coherence
  • ConIQA: A deep learning method for perceptual image quality . . . - Nature
    To address these challenges, we developed ConIQA, a deep learning-based IQA that leverages consistency training and a novel data augmentation method to learn from both labeled and unlabeled data
  • 3D and 4D World Modeling: A Survey - arXiv. org
    Sec 4 systematically summarizes and categorizes widely used datasets and evaluation metrics critical for world modeling tasks, as well as benchmarking recent methods in this related area Sec 5 reviews practical applications of 3D and 4D world models across autonomous driving, robotics, and simulation environments
  • Structural similarity index (SSIM) revisited: A data-driven approach
    Several contemporaneous image processing and computer vision systems rely upon the full-reference image quality assessment (IQA) measures The single-scale structural similarity index (SS-SSIM) is one of the most popular measures, and it owes its success to the mathematical simplicity, low computational complexity, and implicit incorporation of Human Visual System’s (HVS) characteristics In
  • WorldScore: A Unified Evaluation Benchmark for World Generation
    Achieving this vision requires a unified evaluation bench-mark that systematically assesses different types of world generation models across large-scale, diverse worlds Ex-isting benchmarks mainly focus on video generation and evaluate only individual scene generation





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