CAST-Bench: Tracing Every Sound to Its Source in Multi-Shot Audio-Video Generation

Yucong Liu, Qi Tian, Shuyuan Tu, Kaihang Pan, Weijie Kong +1

Existing benchmarks for joint text-to-audio-video (T2AV) generation evaluate perceptual quality and audiovisual alignment, but balanced sound-source sam?pling and separate reporting by shot structure remain limited. This makes it dif?ficult to determine whether a specific sound is missing, attributed to the wrong source, or lost across shot transitions. To address this, we introduce CAST?BENCH, a benchmark designed to trace every generated sound back to its in?tended source in multi-shot audio-video generation. The core idea is to formu?late T2AV correctness as an audiovisual contract in which each requested sound must be emitted by the right source, synchronized with the corresponding visual action, and carry the right semantic content. Concretely, the benchmark orga?nizes 900 prompts into 9 sound-source categories, with 100 prompts per cate?gory, spanning speech, animals, instruments, ambient sounds, and other emitter types. The prompts include 439 single-shot and 461 multi-shot narratives, each derived from a structured specification that links every audio event to its emit?ter, subject, and temporal slot. The benchmark provides an 18-metric evaluation suite covering three…