REVIEW 2 cited by
Enhance-A-Video: Better Generated Video for Free
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
DiT-based video generation has achieved remarkable results, but research into enhancing existing models remains relatively unexplored. In this work, we introduce a training-free approach to enhance the coherence and quality of DiT-based generated videos, named Enhance-A-Video. The core idea is enhancing the cross-frame correlations based on non-diagonal temporal attention distributions. Thanks to its simple design, our approach can be easily applied to most DiT-based video generation frameworks without any retraining or fine-tuning. Across various DiT-based video generation models, our approach demonstrates promising improvements in both temporal consistency and visual quality. We hope this research can inspire future explorations in video generation enhancement.
Forward citations
Cited by 2 Pith papers
-
LatSearch: Latent Reward-Guided Search for Faster Inference-Time Scaling in Video Diffusion
LatSearch improves video diffusion quality and efficiency by scoring intermediate latents with a trained reward model and performing reward-guided resampling plus final pruning.
-
Rethinking Reward Signals in Video GRPO: When Scores Become Targets
A self-paced GRPO variant that adaptively reweights visual, temporal, and text-alignment reward components as the generator improves reports small VBench gains over static-reward baselines.
Discussion (0). Continue with ORCID to comment.