Generative Action Tell-Tales: Assessing Human Motion in Synthesized Videos
Despite rapid advances in video generative models, robust metrics for evaluating visual and temporal correctness of complex human actions remain elusive. Critically, existing pure-vision encoders and Multimodal Large Language Models (MLLMs) are strongly appearance-biased, lack temporal understanding, and thus struggle to discern intricate motion dynamics and anatomical implausibilities in generated videos. We tackle this gap by introducing a novel evaluation metric derived from a learned latent space of real-world human actions. Our method first captures the nuances, constraints, and temporal smoothness of real-world motion by fusing appearance-agnostic human skeletal geometry features with appearance-based features. We posit that this combined feature space provides a robust representation of action plausibility. Given a generated video, our metric quantifies its action quality by measuring the distance between its underlying representations and this learned real-world action distribution. For rigorous validation, we develop a new multi-faceted benchmark specifically designed to probe temporally challenging aspects of human action fidelity. Through extensive experiments, we show that our metric achieves substantial improvement of more than 68% compared to existing state-of-the-art methods on our benchmark, performs competitively on established external benchmarks, and has a stronger correlation with human perception. Our in-depth analysis reveals critical limitations in current video generative models and establishes a new standard for advanced research in video generation.
6932e52e3962c926cf6803dc
Xavier Thomas
claimed_verified
2026-04-13T07:39:35.102Z
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/avatars/9490807f97afc39aeb663d343a900ff9.svg
Thomas
XThomasBU
user
XThomasBU
6932e52e3962c926cf6803dd
Youngsun Lim
6932e52e3962c926cf6803de
Ananya Srinivasan
6932e52e3962c926cf6803df
Audrey Zheng
6932e52e3962c926cf6803e0
Deepti Ghadiyaram
claimed_verified
2026-04-13T07:39:36.917Z
6905068e4c9138632af2fa21
/avatars/c32215347601de938c218e6c81dc2e33.svg
Deepti Ghadiyaram
dghadiya
user
dghadiya
Generative Action Tell-Tales: Assessing Human Motion in Synthesized Videos
Despite rapid advances in video generative models, robust metrics for evaluating visual and temporal correctness of complex human actions remain elusive. Critically, existing pure-vision encoders and Multimodal Large Language Models (MLLMs) are strongly appearance-biased, lack temporal understanding, and thus struggle to discern intricate motion dynamics and anatomical implausibilities in generated videos. We tackle this gap by introducing a novel evaluation metric derived from a learned latent space of real-world human actions. Our method first captures the nuances, constraints, and temporal smoothness of real-world motion by fusing appearance-agnostic human skeletal geometry features with appearance-based features. We posit that this combined feature space provides a robust representation of action plausibility. Given a generated video, our metric quantifies its action quality by measuring the distance between its underlying representations and this learned real-world action distribution. For rigorous validation, we develop a new multi-faceted benchmark specifically designed to probe temporally challenging aspects of human action fidelity. Through extensive experiments, we show that our metric achieves substantial improvement of more than 68% compared to existing state-of-the-art methods on our benchmark, performs competitively on established external benchmarks, and has a stronger correlation with human perception. Our in-depth analysis reveals critical limitations in current video generative models and establishes a new standard for advanced research in video generation.
A novel evaluation metric using a learned latent space of real-world human actions significantly improves the assessment of visual and temporal correctness in generated videos.
video generative models evaluation metric latent space human skeletal geometry features appearance-based features action plausibility multi-faceted benchmark human action fidelity human perception