Joonwoo Kwon

Joonwoo Kwon, Sooyoung Kim, Yuewei Lin et al.

Neural style transfer (NST) has evolved significantly in recent years. Yet, despite its rapid progress and advancement, existing NST methods either struggle to transfer aesthetic information from a style effectively or suffer from high computational costs and inefficiencies in feature disentanglement due to using pre-trained models. This work proposes a lightweight but effective model, AesFA -- Aesthetic Feature-Aware NST. The primary idea is to decompose the image via its frequencies to better disentangle aesthetic styles from the reference image while training the entire model in an end-to-end manner to exclude pre-trained models at inference completely. To improve the network's ability to extract more distinct representations and further enhance the stylization quality, this work introduces a new aesthetic feature: contrastive loss. Extensive experiments and ablations show the approach not only outperforms recent NST methods in terms of stylization quality, but it also achieves faster inference. Codes are available at https://github.com/Sooyyoungg/AesFA.

Heehwan Wang, Joonwoo Kwon, Sooyoung Kim et al.

Music style transfer enables personalized music creation by blending the structure of a source with the stylistic attributes of a reference. Existing text-conditioned and diffusion-based approaches show promise but often require paired datasets, extensive training, or detailed annotations. We present Stylus, a training-free framework that repurposes a pre-trained Stable Diffusion model for music style transfer in the mel-spectrogram domain. Stylus manipulates self-attention by injecting style key-value features while preserving source queries to maintain musical structure. To improve fidelity, we introduce a phase-preserving reconstruction strategy that avoids artifacts from Griffin-Lim reconstruction, and we adopt classifier-free-guidance-inspired control for adjustable stylization and multi-style blending. In extensive evaluations, Stylus outperforms state-of-the-art baselines, achieving 34.1% higher content preservation and 25.7% better perceptual quality without any additional training.