3d U Net Architecture With Resnet Blocks For Video Denoising

via “3d unet temporal-spatial denoising with frame coherence”

VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Unique: 3D convolutions operate jointly on temporal and spatial dimensions, enabling the model to learn motion patterns directly rather than treating frames independently. Attention layers capture long-range temporal dependencies, maintaining consistency across multiple frames.

vs others: 3D convolutions provide better temporal coherence than frame-by-frame generation or 2D convolutions with temporal attention; joint spatial-temporal processing more efficient than separate temporal and spatial pathways; architecture enables learning of motion patterns from data.