EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation

TL;DR

EchoMimicV2 introduces a simplified half-body human animation approach that enhances detail and expressiveness while reducing control complexity, utilizing novel strategies like Audio-Pose Harmonization and Head Partial Attention.

Contribution

It presents a new half-body animation method with innovative strategies to improve detail and reduce conditions, along with a benchmark for evaluation.

Findings

Outperforms existing methods quantitatively and qualitatively.

Enhances facial and gestural expressiveness in animations.

Reduces control conditions without sacrificing quality.

Abstract

Recent work on human animation usually involves audio, pose, or movement maps conditions, thereby achieves vivid animation quality. However, these methods often face practical challenges due to extra control conditions, cumbersome condition injection modules, or limitation to head region driving. Hence, we ask if it is possible to achieve striking half-body human animation while simplifying unnecessary conditions. To this end, we propose a half-body human animation method, dubbed EchoMimicV2, that leverages a novel Audio-Pose Dynamic Harmonization strategy, including Pose Sampling and Audio Diffusion, to enhance half-body details, facial and gestural expressiveness, and meanwhile reduce conditions redundancy. To compensate for the scarcity of half-body data, we utilize Head Partial Attention to seamlessly accommodate headshot data into our training framework, which can be omitted during inference, providing a free lunch for animation. Furthermore, we design the Phase-specific Denoising Loss to guide motion, detail, and low-level quality for animation in specific phases, respectively. Besides, we also present a novel benchmark for evaluating the effectiveness of half-body human animation. Extensive experiments and analyses demonstrate that EchoMimicV2 surpasses existing methods in both quantitative and qualitative evaluations.