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

• URL: http://arxiv.org/abs/2608.12107v1
• Date: Wed, 12 Aug 2026 14:29:08 GMT
• Title: Avatar-Forever: Decoupled Parallel Training for High-Quality Real-Time Infinite Avatars
• Abstract summary: Avatar-Forever is a decoupled parallel training framework for high-quality real-time infinite interactive avatars.<n>It supports audio-driven avatar generation while maintaining identity consistency, motion coherence, and visual fidelity.<n>It enables an end-to-end throughput of high-resolution 768x512 videos at 27.2 FPS on a single H100 GPU.
• Score: 16.361849320150863
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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 shifts introduced in earlier stages affect later optimization, complicating the training process to converge. Second, the distillation-centric objective favours short-term generation but is prone to quality degradation when autoregressive errors accumulate over long rollouts. We propose Avatar-Forever, a decoupled parallel training framework for high-quality real-time infinite interactive avatars. Instead of coupling generation efficiency and long-horizon robustness under a sequential distillation pipeline, we treat them as two independent capabilities that can be trained in parallel. One branch performs full-parameter distillation to train an efficient generator with high visual quality, while another trains a lightweight long-horizon adapter via Recovery-oriented Rollout Training (RRT), which improves generation robustness under long-horizon inference conditions. Our decoupled parallel training design simplifies the overall training process and avoids unnecessary objective conflicts between few-step generation and long-horizon adaptation. We further introduce ForeverCache, a chunk-wise feature caching mechanism to substantially reduce redundant history computation during streaming inference. Built upon a 22B video foundation model, Avatar-Forever supports unbounded audio-driven avatar generation while maintaining identity consistency, motion coherence, and visual fidelity, enabling an end-to-end throughput of high-resolution 768x512 videos at 27.2 FPS on a single H100 GPU and providing a practical path toward stable digital humans.

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