MirrorMe: Towards Realtime and High Fidelity Audio-Driven Halfbody Animation

• URL: http://arxiv.org/abs/2506.22065v1
• Date: Fri, 27 Jun 2025 09:57:23 GMT
• Title: MirrorMe: Towards Realtime and High Fidelity Audio-Driven Halfbody Animation
• Authors: Dechao Meng, Steven Xiao, Xindi Zhang, Guangyuan Wang, Peng Zhang, Qi Wang, Bang Zhang, Liefeng Bo,
• Abstract summary: MirrorMe is a real-time, controllable framework built on the LTX video model.<n>MirrorMe compresses video spatially and temporally for efficient latent space denoising.<n> experiments on the EMTD Benchmark demonstrate MirrorMe's state-of-the-art performance in fidelity, lip-sync accuracy, and temporal stability.
• Score: 21.216297567167036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Audio-driven portrait animation, which synthesizes realistic videos from reference images using audio signals, faces significant challenges in real-time generation of high-fidelity, temporally coherent animations. While recent diffusion-based methods improve generation quality by integrating audio into denoising processes, their reliance on frame-by-frame UNet architectures introduces prohibitive latency and struggles with temporal consistency. This paper introduces MirrorMe, a real-time, controllable framework built on the LTX video model, a diffusion transformer that compresses video spatially and temporally for efficient latent space denoising. To address LTX's trade-offs between compression and semantic fidelity, we propose three innovations: 1. A reference identity injection mechanism via VAE-encoded image concatenation and self-attention, ensuring identity consistency; 2. A causal audio encoder and adapter tailored to LTX's temporal structure, enabling precise audio-expression synchronization; and 3. A progressive training strategy combining close-up facial training, half-body synthesis with facial masking, and hand pose integration for enhanced gesture control. Extensive experiments on the EMTD Benchmark demonstrate MirrorMe's state-of-the-art performance in fidelity, lip-sync accuracy, and temporal stability.

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