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LiveAnimate achieves real-time, stable long-form human animation

Researchers have developed LiveAnimate, a novel system capable of generating stable, long-form human animation in real-time. Built on a 14-billion parameter video Diffusion Transformer, the system employs a two-stage training pipeline and a unique Pose-Retrieval Sink Attention mechanism to maintain consistent appearance and identity over extended durations. This allows for interactive applications like live streaming and virtual avatars, achieving 19.63 FPS inference on high-end GPUs and demonstrating minimal degradation in quality over a three-minute benchmark. AI

IMPACT Enables real-time interactive applications like live streaming and virtual avatars by overcoming latency and stability issues in diffusion-based animation.

RANK_REASON The item describes a new research paper detailing a novel system for real-time human animation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LiveAnimate achieves real-time, stable long-form human animation

COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yuxuan Zhang, Haozhong Xiong, Yubo Huang, Jiayi Song, Jinpeng Yu, Haofan Wang, Jiaming Liu, Ruihua Huang, Liwei Wang ·

    LiveAnimate: Stable Long-Form Streaming Human Animation in Real-Time

    arXiv:2608.11745v1 Announce Type: new Abstract: Pose-driven human animation synthesizes a video of a target person from a single reference image and a driving pose stream. Real-time generation is essential for interactive applications such as live streaming, telepresence, and vir…