2.1 Base LVDM for Short Video Generation LVDM:= Latent video diffusion model Latent:= 对隐向量latent vector添加噪音,而不是直接对image或者video添加噪音 Video Generation Backbone.3D U-Net需要考虑一维时间和二维空间信息(spatial-temporal factorized)。在kernel为1×3×3的3D conv layers加上一些temporal a...
LPIPS (Learned Perceptual Image Patch Similarity) 是一种用于评估图像或视频质量的度量方法,它通过比较深度学习模型提取的特征来测量不同图像间的视觉相似性,LPIPS更能反映人类对视觉质量的感知差异 Base LVDM for Short Video Generation Video Generation Backbone 我们遵循[12](VDM)中的方法,利用时空分解的3D U-N...
2.1 视频自动编码器：轻量级的只包含几层3D conv的自编码器，三个方向都采用repeat padding。训练损失函数包括MSE、LPIPS loss和对抗损失。2.1 基于LVDM的短视频生成：LVDM是对隐向量添加噪音，而不是直接对image或video添加噪音。视频生成主干是3D U-Net，需要考虑时间维度和空间维度信息。在3D conv ...
To address this, we introduce lightweight video diffusion models by leveraging a low-dimensional 3D latent space, significantly outperforming previous pixel-space video diffusion models under a limited computational budget. In addition, we propose hierarchical diffusion in the latent space such that ...
CVPR2022论文精读:Latent Diffusion Model for Image Synthesis, 视频播放量 1.6万播放、弹幕量 2、点赞数 116、投硬币枚数 61、收藏人数 307、转发人数 38, 视频作者 可爱的肚, 作者简介 荷兰留学博主,埃因霍温理工大学,人工智能–增材制造和计算光学,岗位制博士在读,
5. Conclusion We present Stable Video Diffusion (SVD), a latent video diffusion model for high-resolution, state-of-the-art text-to- video and image-to-video synthesis. To construct its pre- training dataset, we conduct a systematic data selection and scaling study, and ...
LVDM:Latent Video Diffusion Models for High-Fidelity Long Video Generation Yingqing He1 Tianyu Yang2 Yong Zhang2 Ying Shan2 Qifeng Chen1 1The Hong Kong University of Science and Technology 2Tencent AI Lab TL;DR: An efficient video diffusion model that can: ...
Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We ...
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 training costs. However, as the resolution and duration of generated videos increase, the encoding cost of Video VAEs bec...
Variational Autoencoder (VAE), compressing videos into latent representations, is a crucial preceding component of Latent Video Diffusion Models (LVDMs). With the same reconstruction quality, the more sufficient the VAE's compression for videos is, the more efficient the LVDMs are. However, most ...