Search Results for author:
Found 8 papers, 2 papers with code
Learning to Generate Conditional Tri-plane for 3D-aware Expression Controllable Portrait Animation
In this paper, we present Export3D, a one-shot 3D-aware portrait animation method that is able to control the facial expression and camera view of a given portrait image.
Context-Preserving Two-Stage Video Domain Translation for Portrait Stylization
Portrait stylization, which translates a real human face image into an artistically stylized image, has attracted considerable interest and many prior works have shown impressive quality in recent years.
StyleLipSync: Style-based Personalized Lip-sync Video Generation
In this paper, we present StyleLipSync, a style-based personalized lip-sync video generative model that can generate identity-agnostic lip-synchronizing video from arbitrary audio.
Grad-StyleSpeech: Any-speaker Adaptive Text-to-Speech Synthesis with Diffusion Models
There has been a significant progress in Text-To-Speech (TTS) synthesis technology in recent years, thanks to the advancement in neural generative modeling.
StyleTalker: One-shot Style-based Audio-driven Talking Head Video Generation
We propose StyleTalker, a novel audio-driven talking head generation model that can synthesize a video of a talking person from a single reference image with accurately audio-synced lip shapes, realistic head poses, and eye blinks.
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation
Real-time video segmentation is a crucial task for many real-world applications such as autonomous driving and robot control.
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
In this work, we propose StyleSpeech, a new TTS model which not only synthesizes high-quality speech but also effectively adapts to new speakers.
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
Then, the learned model can be used for downstream few-shot classification tasks, where we obtain task-specific parameters by performing semi-supervised EM on the latent representations of the support and query set, and predict labels of the query set by computing aggregated posteriors.
