Motion Attribution for Video Generation

Investigates which training clips influence generated motion. Gradient-based attribution uses motion-weighted masks to separate temporal dynamics from static appearance and guide fine-tuning data selection.

Investigates which training clips influence generated motion. Gradient-based attribution uses motion-weighted masks to separate temporal dynamics from static appearance and guide fine-tuning data selection.

When reading the original paper, identify its problem, inputs, outputs, and methodological assumptions.

Investigates which training clips influence generated motion. Gradient-based attribution uses motion-weighted masks to separate temporal dynamics from static appearance and guide fine-tuning data selection.

Check the baselines, datasets, evaluation protocol, and ablations in the original paper. This directory overview does not provide an independent evaluation or experimental results table.