3 citations · 3 across the 5 of their papers we have counts for
7 papers
JewelTry: Mask-Free Scale Aware Jewelry Virtual Try-On
Xinlei Niu, Peixia Li, Jun Wang +5
Virtual try-on (VTON) enables customers to visualize how fashion products appear when worn and has become an important technology for online shopping. While recent advances have su…
Why Your Tokenizer Fails in Information Fusion: A Timing-Aware Pre-Quantization Fusion for Video-Enhanced Audio Tokenization
Xiangyu Zhang, Benjamin John Southwell, Siqi Pan +3
Audio tokenization has emerged as a critical component in end-to-end audio language models, enabling efficient discrete representation learning for both audio understanding and gen…
Beyond Video-to-SFX: Video to Audio Synthesis with Environmentally Aware Speech
Xinlei Niu, Jianbo Ma, Dylan Harper-Harris +3
The generation of realistic, context-aware audio is important in real-world applications such as video game development. While existing video-to-audio (V2A) methods mainly focus on…
SteerMusic: Enhanced Musical Consistency for Zero-shot Text-guided and Personalized Music Editing
Xinlei Niu, Kin Wai Cheuk, Jing Zhang +8
Music editing is an important step in music production, which has broad applications, including game development and film production. Most existing zero-shot text-guided editing me…
SoundMorpher: Perceptually-Uniform Sound Morphing with Diffusion Model
Xinlei Niu, Jing Zhang, Charles Patrick Martin
We present SoundMorpher, an open-world sound morphing method designed to generate perceptually uniform morphing trajectories. Traditional sound morphing techniques typically assume…
SoundLoCD: An Efficient Conditional Discrete Contrastive Latent Diffusion Model for Text-to-Sound Generation
Xinlei Niu, Jing Zhang, Christian Walder +1
We present SoundLoCD, a novel text-to-sound generation framework, which incorporates a LoRA-based conditional discrete contrastive latent diffusion model. Unlike recent large-scale…
HybridVC: Efficient Voice Style Conversion with Text and Audio Prompts
Xinlei Niu, Jing Zhang, Charles Patrick Martin
We introduce HybridVC, a voice conversion (VC) framework built upon a pre-trained conditional variational autoencoder (CVAE) that combines the strengths of a latent model with cont…