LayerT2V: Interactive Multi-Object Trajectory Layering for Video Generation

• URL: http://arxiv.org/abs/2508.04228v1
• Date: Wed, 06 Aug 2025 09:03:16 GMT
• Title: LayerT2V: Interactive Multi-Object Trajectory Layering for Video Generation
• Authors: Kangrui Cen, Baixuan Zhao, Yi Xin, Siqi Luo, Guangtao Zhai, Xiaohong Liu,
• Abstract summary: Controlling object motion trajectories in Text-to-Video (T2V) generation is a challenging and relatively under-explored area.<n>We introduce LayerT2V, the first approach for generating video by compositing background and foreground objects layer by layer.<n>Experiments demonstrate the superiority of LayerT2V in generating complex multi-object scenarios, showcasing 1.4x and 4.5x improvements in mIoU and AP50 metrics over state-of-the-art (SOTA) methods.
• Score: 33.26383352897258
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Controlling object motion trajectories in Text-to-Video (T2V) generation is a challenging and relatively under-explored area, particularly in scenarios involving multiple moving objects. Most community models and datasets in the T2V domain are designed for single-object motion, limiting the performance of current generative models in multi-object tasks. Additionally, existing motion control methods in T2V either lack support for multi-object motion scenes or experience severe performance degradation when object trajectories intersect, primarily due to the semantic conflicts in colliding regions. To address these limitations, we introduce LayerT2V, the first approach for generating video by compositing background and foreground objects layer by layer. This layered generation enables flexible integration of multiple independent elements within a video, positioning each element on a distinct "layer" and thus facilitating coherent multi-object synthesis while enhancing control over the generation process. Extensive experiments demonstrate the superiority of LayerT2V in generating complex multi-object scenarios, showcasing 1.4x and 4.5x improvements in mIoU and AP50 metrics over state-of-the-art (SOTA) methods. Project page and code are available at https://kr-panghu.github.io/LayerT2V/ .

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