xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed
  Representations

Can Qin; Congying Xia; Krithika Ramakrishnan; Michael Ryoo; Lifu Tu; Yihao Feng; Manli Shu; Honglu Zhou; Anas Awadalla; Jun Wang; Senthil Purushwalkam; Le Xue; Yingbo Zhou; Huan Wang; Silvio Savarese; Juan Carlos Niebles; Zeyuan Chen; Ran Xu; Caiming Xiong

Paper · arXiv 2408.12590

xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations

Can Qin, Congying Xia, Krithika Ramakrishnan, Michael Ryoo, Lifu Tu, Yihao Feng, Manli Shu, Honglu Zhou, Anas Awadalla, Jun Wang, Senthil Purushwalkam, Le Xue, Yingbo Zhou, Huan Wang, Silvio Savarese, Juan Carlos Niebles, Zeyuan Chen, Ran Xu, Caiming Xiong

35 upvotesAugust 22, 2024arXiv 预印本

AI 摘要

The xGen-VideoSyn-1 model generates realistic videos from text using a Latent Diffusion Model and Video Variational Autoencoder, with a spatial and temporal divide-and-merge strategy and Diffusion Transformer.

latent diffusion modelVVVAEspatial compressiontemporal compressionvisual tokenscomputational demandsdivide-and-merge strategytemporal consistencyDiffusion Transformerspatial self-attentiontemporal self-attentionrobust generalizationtext-to-videovideo-text pairsclippngtext detectionmotion estimationaesthetics scoringdense captioningvideo-LLM model

Abstract

We present xGen-VideoSyn-1, a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. Building on recent advancements, such as OpenAI's Sora, we explore the latent diffusion model (LDM) architecture and introduce a video variational autoencoder (VidVAE). VidVAE compresses video data both spatially and temporally, significantly reducing the length of visual tokens and the computational demands associated with generating long-sequence videos. To further address the computational costs, we propose a divide-and-merge strategy that maintains temporal consistency across video segments. Our Diffusion Transformer (DiT) model incorporates spatial and temporal self-attention layers, enabling robust generalization across different timeframes and aspect ratios. We have devised a data processing pipeline from the very beginning and collected over 13M high-quality video-text pairs. The pipeline includes multiple steps such as clipping, text detection, motion estimation, aesthetics scoring, and dense captioning based on our in-house video-LLM model. Training the VidVAE and DiT models required approximately 40 and 642 H100 days, respectively. Our model supports over 14-second 720p video generation in an end-to-end way and demonstrates competitive performance against state-of-the-art T2V models.