Towards Subject Consistency over Dynamic Subject Sets in Video Generation
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arXiv:2609.22283v1 Announce Type: new Abstract: Understanding the design space of streaming video diffusion is essential to exploring its potential for generation quality and computational efficiency...
AVC-Reward introduces an event‑centric framework for evaluating audio‑video consistency, breaking down scenes into individual audio and visual events and assessing them at three levels: global narrative harmony, event‑level temporal synchronization, and fine‑grained attribute alignment. The metric, instantiated as a trainable reward model, is validated against human judgments using the AVC‑Benchmark, which contains diverse positive and negative samples from various generation models. AVC‑Reward outperforms existing automatic methods, offers interpretable failure localization, and enhances consistency when used as a reinforcement‑learning signal.
arXiv:2610.02914v1 Announce Type: new Abstract: Autoregressive video models can generate minute-long videos in real time, but they produce generic subjects from text rather than specific subjects fro...
AVC-Reward introduces an event‑centric framework for evaluating audio‑video consistency in joint generation models. It separates audio and visual events, assesses them independently, cross‑validates cross‑modal relations, and aggregates results into a three‑level metric covering global narrative harmony, event‑level synchronization, and fine‑grained attribute alignment. The resulting AVC‑Reward model aligns closely with human judgments, offers interpretable failure localization, and enhances consistency when used as a reinforcement‑learning signal.
arXiv:2609.38377v1 Announce Type: new Abstract: Evaluating the physical consistency of generated videos remains a fundamental challenge. Existing approaches rely on off-the-shelf vision-language mode...
arXiv:2510.07441v2 Announce Type: replace Abstract: Existing text-to-video (T2V) evaluation benchmarks, such as VBench and EvalCrafter, predominantly contain either subject-centric prompts or static...