StreamAvatar：用于实时交互人类分身的流式扩散模型
StreamAvatar: Streaming Diffusion Models for Real-Time Interactive Human Avatars
AI总结：
本文提出一种两阶段自回归适应与加速框架，通过自回归蒸馏和对抗性细化，使高保真人类视频扩散模型适用于实时交互流式生成，实现自然对话与倾听行为。
摘要：
实时、流式交互分身在数字人研究中是一个关键但具有挑战性的目标。尽管基于扩散的人类分身生成方法取得了显著成功，但其非因果架构和高计算成本使其不适合流式处理。此外，现有交互方法通常局限于头部和上半身区域，限制了生成手势和身体动作的能力。为了解决这些挑战，我们提出了一种两阶段自回归适应与加速框架，通过自回归蒸馏和对抗性细化来适应高保真人类视频扩散模型，以实现实时交互流式生成。为确保长期稳定性和一致性，我们引入了三个关键组件：参考汇、参考锚定位置重编码（RAPR）策略和一致性感知判别器。基于此框架，我们开发了一种单次交互的人类分身模型，能够生成自然的说话和倾听行为，并具有连贯的手势。大量实验表明，我们的方法在生成质量、实时效率和交互自然度方面均达到最先进的水平。项目页面：https://streamavatar.github.io
英文摘要：
Real-time, streaming interactive avatars represent a critical yet challenging goal in digital human research. Although diffusion-based human avatar generation methods achieve remarkable success, their non-causal architecture and high computational costs make them unsuitable for streaming. Moreover, existing interactive approaches are typically restricted to the head-and-shoulder region, limiting their ability to produce gestures and body motions. To address these challenges, we propose a two-stage autoregressive adaptation and acceleration framework that applies autoregressive distillation and adversarial refinement to adapt a high-fidelity human video diffusion model for real-time, interactive streaming. To ensure long-term stability and consistency, we introduce three key components: a Reference Sink, a Reference-Anchored Positional Re-encoding (RAPR) strategy, and a Consistency-Aware Discriminator. Building on this framework, we develop a one-shot, interactive, human avatar model capable of generating both natural talking and listening behaviors with coherent gestures. Extensive experiments demonstrate that our method achieves state-of-the-art performance, surpassing existing approaches in generation quality, real-time efficiency, and interaction naturalness. Project page: https://streamavatar.github.io .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
