DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation

• URL: http://arxiv.org/abs/2504.15032v2
• Date: Wed, 30 Apr 2025 01:00:18 GMT
• Title: DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation
• Authors: Weijie He, Mushui Liu, Yunlong Yu, Zhao Wang, Chao Wu,
• Abstract summary: DyST-XL is a framework that enhances off-the-shelf text-to-video models through frame-aware control.<n>The code is released in https://github.com/XiaoBuL/DyST-XL.
• Score: 14.34140569565309
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compositional text-to-video generation, which requires synthesizing dynamic scenes with multiple interacting entities and precise spatial-temporal relationships, remains a critical challenge for diffusion-based models. Existing methods struggle with layout discontinuity, entity identity drift, and implausible interaction dynamics due to unconstrained cross-attention mechanisms and inadequate physics-aware reasoning. To address these limitations, we propose DyST-XL, a \textbf{training-free} framework that enhances off-the-shelf text-to-video models (e.g., CogVideoX-5B) through frame-aware control. DyST-XL integrates three key innovations: (1) A Dynamic Layout Planner that leverages large language models (LLMs) to parse input prompts into entity-attribute graphs and generates physics-aware keyframe layouts, with intermediate frames interpolated via trajectory optimization; (2) A Dual-Prompt Controlled Attention Mechanism that enforces localized text-video alignment through frame-aware attention masking, achieving precise control over individual entities; and (3) An Entity-Consistency Constraint strategy that propagates first-frame feature embeddings to subsequent frames during denoising, preserving object identity without manual annotation. Experiments demonstrate that DyST-XL excels in compositional text-to-video generation, significantly improving performance on complex prompts and bridging a crucial gap in training-free video synthesis. The code is released in https://github.com/XiaoBuL/DyST-XL.

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