Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation

TL;DR

This paper introduces a training-free, alignment-based method for pose-guided video generation that maintains appearance consistency and improves animation quality without requiring extensive data or computation.

Contribution

It proposes a novel training-free framework with a dual alignment strategy to enhance appearance preservation and control in character animation.

Findings

Improves temporal consistency and visual cohesion in generated videos.

Achieves high-quality animations without large datasets or heavy computation.

Decouples skeletal and motion priors for better control.

Abstract

Character animation is a transformative field in computer graphics and vision, enabling dynamic and realistic video animations from static images. Despite advancements, maintaining appearance consistency in animations remains a challenge. Our approach addresses this by introducing a training-free framework that ensures the generated video sequence preserves the reference image's subtleties, such as physique and proportions, through a dual alignment strategy. We decouple skeletal and motion priors from pose information, enabling precise control over animation generation. Our method also improves pixel-level alignment for conditional control from the reference character, enhancing the temporal consistency and visual cohesion of animations. Our method significantly enhances the quality of video generation without the need for large datasets or expensive computational resources.