LayerFlow: A Unified Model for Layer-aware Video Generation (2506.04228v1)

Abstract: 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.

• The paper presents a unified model that generates multi-layer videos using text-to-video diffusion with distinct layer segmentation.
• It utilizes a multi-stage training strategy with optimized low-rank matrix adaptations to integrate high-quality images with dynamic video data.
• User studies and qualitative analyses demonstrate superior inter-layer coherence, aesthetic quality, and semantic fidelity over existing methods.

LayerFlow: A Unified Model for Layer-aware Video Generation

The paper "LayerFlow: A Unified Model for Layer-aware Video Generation" introduces an innovative approach to generating multi-layer videos that incorporate a transparent foreground, clean background, and composite scene. The research aims to address the challenges of layer-aware video generation, which offers substantial benefits in visual content production workflows through flexible decomposition, recomposition, and modular editing. Indeed, this paper holds implications for both practical video content creation and theoretical advancements in multimodal deep learning models.

Framework and Methodology

The LayerFlow model utilizes a text-to-video diffusion transformer, yet distinguishes itself by organizing different video layers as discrete sub-clips, each with specific textual prompts. Through layer embeddings, the model identifies and associates video segments with their respective layer-wise textual descriptions. This method allows the deployment of a holistic framework supporting various generation scenarios, such as extracting layers from a composite video or generating a scene based on one given layer.

To counteract the deficit of high-quality layered video data, a pivotal component of their approach is the multi-stage training strategy, employing optimized low-rank matrix adaptations (LoRA) to facilitate simultaneous training on static high-quality images and dynamic low-quality videos. Initially, a pre-trained text-to-video model undergoes fine-tuning with low-quality data to enhance baseline layer generation capabilities. Subsequently, a motion LoRA adapts the model for static frames. Finally, the content LoRA is applied using mixed data containing high-quality layered images and synthetic videos, reinforcing layer-aware synthesis through joint image-video training.

Evaluation and Comparative Analysis

The researchers conduct extensive qualitative analyses, illustrating LayerFlow's superior performance over pre-existing solutions such as the sequential application of LayerDiffuse with a motion synthesis module. LayerFlow's architecture offers enhanced inter-layer coherence and separation alongside improved dynamic consistency, aesthetic quality, and semantic fidelity relative to alternatives.

Furthermore, the user studies present compelling evidence of LayerFlow's advantages across generation tasks. Participants assessed the model's output, focusing on criteria such as foreground and background quality, blended scene harmony, and alignment with textual descriptions. The results align with the quantitative analysis provided, showing LayerFlow's proficiency in generating coherent and high-fidelity video layers.

Implications and Future Directions

The broad implications of this research extend beyond immediate video generation tasks. LayerFlow potentially facilitates various multimedia applications, from media production to interactive digital content creation. It empowers creators to dynamically compose, edit, and adapt video assets at the layer level, boosting creativity and efficiency in multimedia workflows.

The theoretical contributions evolve the understanding of multimodal learning, demonstrating how layer-aware embeddings and tailored training strategies can enhance deep model capabilities in handling diverse video generation scenarios. Future developments may explore the integration of variable layer generation, broadening the model's applicability in generating complex scenes. Additionally, expanding the dataset and refining the motion adaptation process may further optimize its performance.

In summation, LayerFlow exemplifies meaningful steps towards mastering layer-aware video generation. Its unified framework equips researchers and practitioners with the tools to synthesize, decompose, and adapt visual layers, paving the way for advanced and flexible multimedia applications.