Papers with video models

2 papers
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation (2024.emnlp-main)

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Challenge: Existing video metrics are lagging behind in providing reliable scores over generated videos due to lack of large-scale human-annotated dataset.
Approach: They propose to use VideoFeedback to train a human-annotated multi-aspect score over 37.6K synthesized videos from 11 existing video generative models.
Outcome: The proposed model outperforms the prior best metrics by 50 points in the test.
Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning (2026.acl-long)

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Challenge: Existing evaluation frameworks often rely on single-frame assessments, which can lead to outcome-hacking.
Approach: They propose a process-aware evaluation paradigm that uses a hierarchical rubric to evaluate the validity of the intermediate steps and the final result.
Outcome: The proposed model achieves POC@1.0 only about 20% and exhibits significant outcome-hacking.

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