PRISM‑Bench is an audio‑centric diagnostic benchmark for text‑to‑audio‑video generation, built from 900 human‑verified samples. It evaluates audio along two axes—audio type (speech, music, sound) and sound‑source visibility (on‑screen vs. off‑screen)—across four perceptual dimensions (audio‑visual coherence, audio quality, audio expressiveness, and prompt following) using 35 fine‑grained criteria. The benchmark employs an enhanced MLLM‑as‑a‑Judge protocol that aligns strongly with human raters, revealing a performance gap between frontier and open‑source T2AV models and highlighting overfitting to perceptual fidelity while struggling with complex grounding and control tasks, especially for music and synchronized on‑screen audio.
By Yuchen Sun, Qian Yang, Jun Wang, Detai Xin, Guoqiao Yu, Guanglu Wan, Qi Jia
arXiv:2605. 00873v2 Announce Type: replace-cross Abstract: The rapid advancement of photorealistic Text-to-Video (T2V) generation brings in an urgent need for up-to-date evaluation methods.
By Advait Tilak, Jiwon Choi, Nazifa Mouli, Wei Le
arXiv:2609.15562v1 Announce Type: cross
Abstract: As generative models continue to advance, AI-generated content (AIGC) is becoming increasingly realistic, weakening the artifact cues commonly exploi...
By Bo Zheng, Kangran Zhao, Xiaoyu Zhang, Weinan Guan, Zhiheng Li, Yize Chen, Haizhou Li, Qingshan Liu, Siwei Lyu, Baoyuan Wu
StreamAV-Bench is the first comprehensive benchmark designed for streaming audio‑video generation, addressing the limitations of existing benchmarks that focus on completed sequences. It introduces a unified evaluation framework with a progressive track for instruction adherence and long‑horizon stability, and an interactive track for responsive interaction and state retention. The benchmark includes 32 fine‑grained, expert‑verified evaluation cases and evaluates 13 representative systems, revealing temporal drift in progressive generation and responsiveness bottlenecks in interactive control.
By Kaiqi Liu, Haoxuan Zeng, Jingqi Liu, Jiacong Fang, Ziqi Cai, Yunyao Mao, Henglin Liu, Yu Sheng, Shuchen Weng, Boxin Shi
arXiv:2609.38444v1 Announce Type: new
Abstract: Text- and image-conditioned world generators can create visually rich 3D environments, yet these worlds often remain silent or rely on soundtracks synt...
By Duowen Chen, Jinjin He, Gouthaman KV, Sandeep Bangalore Venkatesh, Bo Zhu
arXiv:2610.00691v1 Announce Type: new
Abstract: Recent joint audio-video generative models can synthesize realistic videos with synchronized sound, but typically generate audio as a single mixed trac...
By Zhuo Ning, AmirHossein Naghi Razlighi, Sagi Polaczek, Daniel Cohen-Or, Ali Mahdavi-Amiri