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arXiv · 2506.00830

SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers

Zhengcong Fei·Hao Jiang·Di Qiu·Baoxuan Gu·Youqiang Zhang·Jiahua Wang·Jialin Bai·Debang Li·Mingyuan Fan·Guibin Chen·Yahui Zhou
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Abstract

The generation and editing of audio-conditioned talking portraits guided by multimodal inputs, including text, images, and videos, remains under explored. In this paper, we present SkyReels-Audio, a unified framework for synthesizing high-fidelity and temporally coherent talking portrait videos. Built upon pretrained video diffusion transformers, our framework supports infinite-length generation and editing, while enabling diverse and controllable conditioning through multimodal inputs. We employ a hybrid curriculum learning strategy to progressively align audio with facial motion, enabling fine-grained multimodal control over long video sequences. To enhance local facial coherence, we introduce a facial mask loss and an audio-guided classifier-free guidance mechanism. A sliding-window denoising approach further fuses latent representations across temporal segments, ensuring visual fidelity and temporal consistency across extended durations and diverse identities. More importantly, we construct a dedicated data pipeline for curating high-quality triplets consisting of synchronized audio, video, and textual descriptions. Comprehensive benchmark evaluations show that SkyReels-Audio achieves superior performance in lip-sync accuracy, identity consistency, and realistic facial dynamics, particularly under complex and challenging conditions.

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Zhengcong Fei, Hao Jiang, Di Qiu, Baoxuan Gu, Youqiang Zhang, Jiahua Wang, Jialin Bai, Debang Li, Mingyuan Fan, Guibin Chen, Yahui Zhou. 2025-06-01. SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers. https://arxiv.org/abs/2506.00830

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