# Comparison

## Dense Task

Qualitative comparison on dense control. 2D-based methods, MagicMotion and Go-with-the-Flow struggle to capture fine-grained details, while DiffusionAsShader also fails as its trajectory representation cannot handle newly emerging points. In contrast, our method outperforms them by closely following the motion in the source frames.

## Spatial Sparse Task

Qualitative comparison on spatial-sparse control. The subject in the left of input image is occluded by the subject in the right. 2D-based methods (MagicMotion, ToRA) fail in handling occlusion, U-Net-based method LeviTor introduces artifacts, while ours accurately captures occlusion with high visual fidelity.

## Temporal Sparse Task

Qualitative comparison on temporal-sparse control. SparseCtrl yields unsatisfactory results, while MagicMotion shows weak alignment and blurriness. Our method aligns with the anchor-frame motion and generates coherent in-between frames.

## Unaligned Task

Qualitative comparison on unaligned control. DAS introduces artifacts (red blurriness around subject) from strict alignment, while Go-with-the-Flow produces implausible results. Our method flexibly follows input motion.