LayerFlow: A Unified Model for Layer-aware Video Generation
Authors: name: Sihui Ji, name: Hao Luo, name: Xi Chen, name: Yuanpeng Tu, name: Yiyang Wang, name: Hengshuang Zhao

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.