EWMBench: Evaluating Scene, Motion, and Semantic Quality in Embodied World Models (2505.09694v2)

Abstract: Recent advances in creative AI have enabled the synthesis of high-fidelity images and videos conditioned on language instructions. Building on these developments, text-to-video diffusion models have evolved into embodied world models (EWMs) capable of generating physically plausible scenes from language commands, effectively bridging vision and action in embodied AI applications. This work addresses the critical challenge of evaluating EWMs beyond general perceptual metrics to ensure the generation of physically grounded and action-consistent behaviors. We propose the Embodied World Model Benchmark (EWMBench), a dedicated framework designed to evaluate EWMs based on three key aspects: visual scene consistency, motion correctness, and semantic alignment. Our approach leverages a meticulously curated dataset encompassing diverse scenes and motion patterns, alongside a comprehensive multi-dimensional evaluation toolkit, to assess and compare candidate models. The proposed benchmark not only identifies the limitations of existing video generation models in meeting the unique requirements of embodied tasks but also provides valuable insights to guide future advancements in the field. The dataset and evaluation tools are publicly available at https://github.com/AgibotTech/EWMBench.

Scene, Motion, and Semantic Evaluation in Embodied World Models

The paper, "EWMBench: Evaluating Scene, Motion, and Semantic Quality in Embodied World Models," introduces a novel benchmark designed to address the unique challenges posed by evaluating Embodied World Models (EWMs). EWMs represent a sophisticated advancement in AI, extending beyond traditional video generation models by producing scenes that are inherently action-consistent and physically plausible, thus functioning as simulators within embodied AI applications.

Overview and Contributions

In recent years, generative models have evolved significantly, particularly text-to-video diffusion models which have transitioned into embodied world models. However, the task of evaluating these models comprehensively against the specific requirements of embodied AI—such as maintaining physical realism and ensuring action consistency—remains underexplored. The paper proposes a structured evaluation framework known as EWMBench, aimed at addressing these evaluation challenges by focusing on three principal dimensions: visual scene consistency, motion correctness, and semantic alignment.

Key Contributions:

1. Benchmark Proposal: The paper introduces EWMBench, the first benchmarking framework tailored specifically for evaluating EWMs.
2. Dataset Construction: The authors compiled a diverse dataset, Agibot-World, rich in real-world robotic manipulation tasks, which serves as the foundation for benchmark assessments.
3. Multi-dimensional Metrics: The paper details a suite of metrics designed to evaluate scene stability, motion fidelity, and semantic coherence in generated videos.
4. Evaluation Insights: Through EWMBench, the paper provides critical insights into the performance limitations of current video generation models concerning embodied tasks.

Evaluation Approach

EWMBench leverages a sophisticated dataset and evaluation toolkit to assess EWMs' ability to generate realistic, action-aligned video content. The evaluation considers:

• Scene Consistency: This involves ensuring static elements within the generated scenes remain appropriately fixed while only the intended motions unfold. The paper uses fine-tuned visual models, like DINOv2, to assess spatial and visual consistency.
• Motion Correctness: Using metrics like the Symmetric Hausdorff Distance and Dynamic Time Warping, the paper evaluates the spatial and temporal dynamics of the predicted trajectories against ground truth data.
• Semantic Alignment: Evaluating how well the model-generated actions and scenes align with linguistic prompts or instructions, with metrics assessing both linguistic similarity and task diversity.

Analysis and Implications

The benchmark revealed several critical insights. Models that have undergone domain-specific adaptations perform significantly better across all evaluation dimensions. Notably, these models excel at understanding task logic and motion dynamics but may occasionally falter in fine-grained action execution, such as grasping tasks without interaction precision. The paper highlights the need for more robust semantic grounding to alleviate biases toward human-centric representations seen in several commercial models.

The implications of this research are twofold:

1. Practical Implications: The evaluation framework proposed by EWMBench could drive improvements in embodied AI applications, particularly in robotics, where physical interactions based on video-generated instructions are crucial.
2. Theoretical Implications: The structured evaluation criteria could guide future model design, promoting more profound interconnections between vision, semantics, and action in embodied world models.

Future Directions

The paper acknowledges several limitations and outlines potential future work areas. It emphasizes expanding the benchmark to broader task domains beyond static-viewpoint robotic manipulations, such as incorporating navigation and mobile manipulation scenarios. Additionally, enhancing dataset diversity and evaluating models on flexible camera perspectives could provide deeper insights into embodied video generation models' capabilities.

In conclusion, EWMBench represents a valuable tool to systematically guide advancements in the evaluation of EWMs for embodied AI, fostering improvements in both model design and real-world application efficacy.