RepVideo: Rethinking Cross-Layer Representation for Video Generation

TL;DR

RepVideo introduces a novel cross-layer feature accumulation method for text-to-video diffusion models, significantly improving spatial accuracy and temporal coherence in generated videos by stabilizing semantic representations.

Contribution

It proposes RepVideo, a new framework that enhances semantic stability and temporal consistency by aggregating features across layers in diffusion-based video generation.

Findings

Improves spatial appearance accuracy in generated videos.

Enhances temporal consistency across frames.

Captures complex spatial relationships effectively.

Abstract

Video generation has achieved remarkable progress with the introduction of diffusion models, which have significantly improved the quality of generated videos. However, recent research has primarily focused on scaling up model training, while offering limited insights into the direct impact of representations on the video generation process. In this paper, we initially investigate the characteristics of features in intermediate layers, finding substantial variations in attention maps across different layers. These variations lead to unstable semantic representations and contribute to cumulative differences between features, which ultimately reduce the similarity between adjacent frames and negatively affect temporal coherence. To address this, we propose RepVideo, an enhanced representation framework for text-to-video diffusion models. By accumulating features from neighboring layers to form enriched representations, this approach captures more stable semantic information. These enhanced representations are then used as inputs to the attention mechanism, thereby improving semantic expressiveness while ensuring feature consistency across adjacent frames. Extensive experiments demonstrate that our RepVideo not only significantly enhances the ability to generate accurate spatial appearances, such as capturing complex spatial relationships between multiple objects, but also improves temporal consistency in video generation.