LayerFlow: A Unified Model for Layer-aware Video Generation

Sihui Ji, Hao Luo, xichen, Yuanpeng Tu, Yiyang Wang, Hengshuang Zhao

LayerFlow: A Unified Model for Layer-aware Video Generation: 13 upvotes on Hugging Face Daily Papers, #16 of 50 papers on 2025-06-05. Day-by-day upvote history.

We present LayerFlow, a unified solution for layer-aware video generation. Given per-layer prompts, LayerFlow generates videos for the transparent foreground, clean background, and blended scene. It also supports versatile variants like decomposing a blended video or generating the background for the given foreground and vice versa. Starting from a text-to-video diffusion transformer, we organize the videos for different layers as sub-clips, and leverage layer embeddings to distinguish each clip and the corresponding layer-wise prompts. In this way, we seamlessly support the aforementioned variants in one unified framework. For the lack of high-quality layer-wise training videos, we design a multi-stage training strategy to accommodate static images with high-quality layer annotations. Specifically, we first train the model with low-quality video data. Then, we tune a motion LoRA to make the model compatible with static frames. Afterward, we train the content LoRA on the mixture of image data with high-quality layered images along with copy-pasted video data. During inference, we remove the motion LoRA thus generating smooth videos with desired layers.

Paper page on Hugging Face · arXiv

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