Direct Preference Optimization (DPO) has recently been applied as a
post-training technique for text-to-video diffusion models. To obtain training
data, annotators are asked to provide preferences between two videos generated
from independent noise. However, this approach prohibits fine-grained
comparisons, and we point out that it biases the annotators towards low-motion
clips as they often contain fewer visual artifacts. In this work, we introduce
DenseDPO, a method that addresses these shortcomings by making three
contributions. First, we create each video pair for DPO by denoising corrupted
copies of a ground truth video. This results in aligned pairs with similar
motion structures while differing in local details, effectively neutralizing
the motion bias. Second, we leverage the resulting temporal alignment to label
preferences on short segments rather than entire clips, yielding a denser and
more precise learning signal. With only one-third of the labeled data, DenseDPO
greatly improves motion generation over vanilla DPO, while matching it in text
alignment, visual quality, and temporal consistency. Finally, we show that
DenseDPO unlocks automatic preference annotation using off-the-shelf Vision
Language Models (VLMs): GPT accurately predicts segment-level preferences
similar to task-specifically fine-tuned video reward models, and DenseDPO
trained on these labels achieves performance close to using human labels.