EmoVid: A Multimodal Emotion Video Dataset for Emotion-Centric Video Understanding and Generation

TL;DR

EmoVid introduces a novel multimodal, emotion-annotated video dataset for creative media, enabling improved emotion-aware video generation and analysis across stylized and non-realistic contexts.

Contribution

This work presents EmoVid, the first dataset of its kind, and develops an emotion-conditioned video generation method that enhances visual quality and emotional expression in generated videos.

Findings

Significant improvement in quantitative metrics for generated videos.

Enhanced visual quality in text-to-video and image-to-video tasks.

Uncovered spatial and temporal patterns linking visual features to emotions.

Abstract

Emotion plays a pivotal role in video-based expression, but existing video generation systems predominantly focus on low-level visual metrics while neglecting affective dimensions. Although emotion analysis has made progress in the visual domain, the video community lacks dedicated resources to bridge emotion understanding with generative tasks, particularly for stylized and non-realistic contexts. To address this gap, we introduce EmoVid, the first multimodal, emotion-annotated video dataset specifically designed for creative media, which includes cartoon animations, movie clips, and animated stickers. Each video is annotated with emotion labels, visual attributes (brightness, colorfulness, hue), and text captions. Through systematic analysis, we uncover spatial and temporal patterns linking visual features to emotional perceptions across diverse video forms. Building on these insights, we develop an emotion-conditioned video generation technique by fine-tuning the Wan2.1 model. The results show a significant improvement in both quantitative metrics and the visual quality of generated videos for text-to-video and image-to-video tasks. EmoVid establishes a new benchmark for affective video computing. Our work not only offers valuable insights into visual emotion analysis in artistically styled videos, but also provides practical methods for enhancing emotional expression in video generation.