ImagerySearch: Adaptive Test-Time Search for Video Generation Beyond
  Semantic Dependency Constraints
Authors: name: Meiqi Wu, name: Jiashu Zhu, name: Xiaokun Feng, name: Chubin Chen, name: Chen Zhu, name: Bingze Song, name: Fangyuan Mao, name: Jiahong Wu, name: Xiangxiang Chu, name: Kaiqi Huang
Organization: AMAP-ML

Video generation models have achieved remarkable progress, particularly
excelling in realistic scenarios; however, their performance degrades notably
in imaginative scenarios. These prompts often involve rarely co-occurring
concepts with long-distance semantic relationships, falling outside training
distributions. Existing methods typically apply test-time scaling for improving
video quality, but their fixed search spaces and static reward designs limit
adaptability to imaginative scenarios. To fill this gap, we propose
ImagerySearch, a prompt-guided adaptive test-time search strategy that
dynamically adjusts both the inference search space and reward function
according to semantic relationships in the prompt. This enables more coherent
and visually plausible videos in challenging imaginative settings. To evaluate
progress in this direction, we introduce LDT-Bench, the first dedicated
benchmark for long-distance semantic prompts, consisting of 2,839 diverse
concept pairs and an automated protocol for assessing creative generation
capabilities. Extensive experiments show that ImagerySearch consistently
outperforms strong video generation baselines and existing test-time scaling
approaches on LDT-Bench, and achieves competitive improvements on VBench,
demonstrating its effectiveness across diverse prompt types. We will release
LDT-Bench and code to facilitate future research on imaginative video
generation.