6 papers · 1 filter
AnyAct: Towards Human Reenactment of Character Motion From Video
Liuhan Chen, Lei Zhong, Jiawei Wang +6
We study the problem of directly deriving an initial human reenactment from a monocular video of a non-human character. Our goal is not to reconstruct the source character itself b…
EF-VI: Enhancing End-Frame Injection for Video Inbetweening
Liuhan Chen, Xiaodong Cun, Xiaoyu Li +5
Video inbetweening aims to synthesize intermediate video sequences conditioned on the given start and end frames. Current state-of-the-art methods primarily extend large-scale pre-…
WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model
Zongjian Li, Bin Lin, Yang Ye +4
Video Variational Autoencoder (VAE) encodes videos into a low-dimensional latent space, becoming a key component of most Latent Video Diffusion Models (LVDMs) to reduce model train…
Identity-Preserving Text-to-Video Generation by Frequency Decomposition
Shenghai Yuan, Jinfa Huang, Xianyi He +5
Identity-preserving text-to-video (IPT2V) generation aims to create high-fidelity videos with consistent human identity. It is an important task in video generation but remains an…
Open-Sora Plan: Open-Source Large Video Generation Model
Bin Lin, Yunyang Ge, Xinhua Cheng +21
We introduce Open-Sora Plan, an open-source project that aims to contribute a large generation model for generating desired high-resolution videos with long durations based on vari…
OD-VAE: An Omni-dimensional Video Compressor for Improving Latent Video Diffusion Model
Liuhan Chen, Zongjian Li, Bin Lin +6
Variational Autoencoder (VAE), compressing videos into latent representations, is a crucial preceding component of Latent Video Diffusion Models (LVDMs). With the same reconstructi…