FlowVid: Taming Imperfect Optical Flows: Inflating Image U-Net to Accommodate Video

Table of Links

• Abstract and Introduction
• 2. Related Work
• 3. Preliminary
• 4. FlowVid
• 4.1. Inflating image U-Net to accommodate video
• 4.2. Training with joint spatial-temporal conditions
• 4.3. Generation: edit the first frame then propagate
• 5. Experiments
• 5.1. Settings
• 5.2. Qualitative results
• 5.3. Quantitative results
• 5.4. Ablation study and 5.5. Limitations
• Conclusion, Acknowledgments and References
• A. Webpage Demo and B. Quantitative comparisons

4.1. Inflating image U-Net to accommodate video

The latent diffusion models (LDMs) are built upon the architecture of U-Net, which comprises multiple encoder and decoder blocks. Each block has two components: a residual convolutional module and a transformer module. The transformer module, in particular, comprises a spatial selfattention layer, a cross-attention layer, and a feed-forward network. To extend the U-Net architecture to accommodate an additional temporal dimension, we first modify all the 2D layers within the convolutional module to pseudo-3D layers and add an extra temporal self-attention layer [18]. Following common practice [6, 18, 25, 35, 46], we further adapt the spatial self-attention layer to a spatial-temporal self-attention layer. For video frame Ii , the attention matrix would take the information from the first frame I1 and the previous frame Ii−1. Specifically, we obtain the query feature from frame Ii , while getting the key and value features from I1 and Ii−1. The Attention(Q, K, V ) of spatial-temporal self-attention could be written as