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Avatar-Forever framework enables real-time infinite avatars with parallel training

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Avatar-Forever, a novel framework designed for high-quality, real-time infinite interactive avatars. This system decouples the training of generation efficiency and long-horizon robustness into parallel branches, addressing limitations in existing sequential training pipelines. Avatar-Forever utilizes a full-parameter distillation for visual quality and a lightweight adapter trained with Recovery-oriented Rollout Training (RRT) for improved generation robustness. Additionally, it incorporates ForeverCache, a chunk-wise feature caching mechanism to optimize streaming inference by reducing redundant computations. Built on a 22B video foundation model, Avatar-Forever achieves unbounded audio-driven avatar generation with consistent identity and motion, reaching a throughput of 27.2 FPS at 768x512 resolution on a single H100 GPU. AI

IMPACT This framework could enable more stable and realistic digital humans for interactive applications.

RANK_REASON The cluster contains a research paper detailing a new technical framework for avatar generation. [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 →

Avatar-Forever framework enables real-time infinite avatars with parallel training

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The cluster contains a research paper detailing a new technical framework for avatar generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ruibin Li, Tao Yang, Zhiyuan Ma, Fangzhou Ai, Shilei Wen, Lei Zhang ·

    Avatar-Forever: Decoupled Parallel Training for High-Quality Real-Time Infinite Avatars

    arXiv:2608.12107v1 Announce Type: new Abstract: Existing streaming video systems often rely on sequential, distillation-centered training pipelines to enable few-step long-video generation. However, this paradigm suffers from two limitations. First, failures or distribution shift…

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