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Puyuan Guo

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2 papers

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AAAI Conference 2026 Conference Paper

HQ-SVC: Towards High-Quality Zero-Shot Singing Voice Conversion in Low-Resource Scenarios

Bingsong Bai
Yizhong Geng
Fengping Wang
Cong Wang
Puyuan Guo
Yingming Gao
Ya Li

Zero-shot singing voice conversion (SVC) transforms a source singer's timbre to an unseen target speaker's voice while preserving melodic content without fine-tuning. Existing methods model speaker timbre and vocal content separately, losing essential acoustic information that degrades output quality while requiring significant computational resources. To overcome these limitations, we propose HQ-SVC, an efficient framework for high-quality zero-shot SVC. HQ-SVC first extracts jointly content and speaker features using a decoupled codec. It then enhances fidelity through pitch and volume modeling, preserving critical acoustic information typically lost in separate modeling approaches, and progressively refines outputs via differentiable signal processing and diffusion techniques. Evaluations confirm HQ-SVC significantly outperforms state-of-the-art zero-shot SVC methods in conversion quality and efficiency. Beyond voice conversion, HQ-SVC achieves superior voice naturalness compared to specialized audio super-resolution methods while natively supporting voice super-resolution tasks.

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AAAI Conference 2025 Conference Paper

Controllable 3D Dance Generation Using Diffusion-Based Transformer U-Net

Puyuan Guo
Tuo Hao
Wenxin Fu
Yingming Gao
Ya Li

Recently, dance generation has attracted increasing interest. In particular, the success of diffusion models in image generation has led to the emergence of dance generation systems based on the diffusion framework. However, these systems lack controllability, which limits their practical applications. In this paper, we propose a controllable dance generation method based on the diffusion model, which can generate 3D dance motions controlled by 2D keypoint sequences. Specifically, we design a transformer-based U-Net model to predict actual motions. Then, we fix the parameters of the U-Net model and train an additional control network, enabling the generated motions to be controlled by 2D keypoints. We conduct extensive experiments and compared our method with existing works on the widely used AIST++ dataset, demonstrating that our approach has certain advantages and controllability. Moreover, we also test our model on in-the-wild videos and find that it is capable of generating dance movements similar to the motions in the videos as well.

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