MVAnimate: Enhancing Character Animation with Multi-View Optimization

2026-02-09

AI Summary

Existing character animation generation methods are often constrained by 2D or 3D modeling paradigms and suffer from low output quality and limited training data. This work proposes MVAnimate, a novel framework that introduces multi-view priors into character animation synthesis for the first time. By jointly optimizing multi-view video sequences, MVAnimate integrates 2D/3D human pose representations with multi-view geometric constraints to produce temporally consistent and spatially coherent high-quality animations. Extensive experiments demonstrate that the method exhibits strong robustness across diverse motions and appearances on multiple datasets, significantly outperforming current state-of-the-art approaches in terms of visual fidelity and motion realism.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Video Understanding & Activity Analysis
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The demand for realistic and versatile character animation has surged, driven by its wide-ranging applications in various domains. However, the animation generation algorithms modeling human pose with 2D or 3D structures all face various problems, including low-quality output content and training data deficiency, preventing the related algorithms from generating high-quality animation videos. Therefore, we introduce MVAnimate, a novel framework that synthesizes both 2D and 3D information of dynamic figures based on multi-view prior information, to enhance the generated video quality. Our approach leverages multi-view prior information to produce temporally consistent and spatially coherent animation outputs, demonstrating improvements over existing animation methods. Our MVAnimate also optimizes the multi-view videos of the target character, enhancing the video quality from different views. Experimental results on diverse datasets highlight the robustness of our method in handling various motion patterns and appearances.

Problem

• character animation
• animation generation
• video quality
• multi-view optimization
• training data deficiency

Innovation

• multi-view optimization
• character animation
• 2D-3D fusion
• temporal consistency
• spatial coherence