3 citations · 3 across the 3 of their papers we have counts for
5 papers
Visual Persona: Foundation Model for Full-Body Human Customization
Jisu Nam, Soowon Son, Zhan Xu +6
We introduce Visual Persona, a foundation model for text-to-image full-body human customization that, given a single in-the-wild human image, generates diverse images of the indivi…
Move-in-2D: 2D-Conditioned Human Motion Generation
Hsin-Ping Huang, Yang Zhou, Jui-Hsien Wang +4
Generating realistic human videos remains a challenging task, with the most effective methods currently relying on a human motion sequence as a control signal. Existing approaches…
HARIVO: Harnessing Text-to-Image Models for Video Generation
Mingi Kwon, Seoung Wug Oh, Yang Zhou +6
We present a method to create diffusion-based video models from pretrained Text-to-Image (T2I) models. Recently, AnimateDiff proposed freezing the T2I model while only training tem…
Progressive Autoregressive Video Diffusion Models
Desai Xie, Zhan Xu, Yicong Hong +5
Current frontier video diffusion models have demonstrated remarkable results at generating high-quality videos. However, they can only generate short video clips, normally around 1…
VideoGigaGAN: Towards Detail-rich Video Super-Resolution
Yiran Xu, Taesung Park, Richard Zhang +5
Video super-resolution (VSR) approaches have shown impressive temporal consistency in upsampled videos. However, these approaches tend to generate blurrier results than their image…