Title: TTOM: Test-Time Optimization and Memorization for Compositional Video Generation
Primary area: generative models
Keywords: Text-to-Video Generation, Test-Time Optimization, Memory

Abstract: Video Foundation Models (VFMs) exhibit remarkable visual generation performance, but struggle in compositional scenarios (\eg, motion, numeracy, and spatial relation). 
In this work, we introduce **Test-Time Optimization and Memorization (TTOM)**, a training-free framework that aligns VFM outputs with spatiotemporal layouts during inference for better text-image alignment.
Rather than direct intervention to latents or attention per-sample in existing work, we integrate and optimize new parameters guided by a general layout-attention objective. 
Furthermore, we formulate
video generation within a streaming setting, and maintain historical optimization contexts with a parametric memory mechanism that supports flexible operations, such as insert, read, update, and delete. 
Notably, we found that TTOM disentangles compositional world knowledge, showing powerful transferability and generalization. 
Experimental results on the T2V-CompBench and Vbench benchmarks establish TTOM as an effective, practical, scalable, and efficient framework to achieve cross-modal alignment for compositional video generation on the fly.

TL;DR: We propose a latent memory–based test-time optimization framework that improves compositional text-to-video generation by dynamically adapting to unseen prompts during inference.