2 citations · 6 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation
Shenghai Yuan, Jinfa Huang, Yongqi Xu +7
We propose a novel text-to-video (T2V) generation benchmark, ChronoMagic-Bench, to evaluate the temporal and metamorphic capabilities of the T2V models (e.g. Sora and Lumiere) in t…
UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark
Zhaokun Zhou, Qiulin Wang, Bin Lin +7
As an alternative to expensive expert evaluation, Image Aesthetic Assessment (IAA) stands out as a crucial task in computer vision. However, traditional IAA methods are typically c…
MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators
Shenghai Yuan, Jinfa Huang, Yujun Shi +6
Recent advances in Text-to-Video generation (T2V) have achieved remarkable success in synthesizing high-quality general videos from textual descriptions. A largely overlooked probl…