Make-A-Video: Text-to-Video Generation without Text-Video Data

• URL: http://arxiv.org/abs/2209.14792v1
• Date: Thu, 29 Sep 2022 13:59:46 GMT
• Title: Make-A-Video: Text-to-Video Generation without Text-Video Data
• Authors: Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, Yaniv Taigman
• Abstract summary: Make-A-Video is an approach for translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V) We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules. In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation.
• Score: 69.20996352229422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from unsupervised video footage. Make-A-Video has three advantages: (1) it accelerates training of the T2V model (it does not need to learn visual and multimodal representations from scratch), (2) it does not require paired text-video data, and (3) the generated videos inherit the vastness (diversity in aesthetic, fantastical depictions, etc.) of today's image generation models. We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules. First, we decompose the full temporal U-Net and attention tensors and approximate them in space and time. Second, we design a spatial temporal pipeline to generate high resolution and frame rate videos with a video decoder, interpolation model and two super resolution models that can enable various applications besides T2V. In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation, as determined by both qualitative and quantitative measures.

Related papers

• Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model [133.01510927611452]
  We present Step-Video-T2V, a text-to-video pre-trained model with 30Bational parameters and the ability to generate videos up to 204 frames in length. A deep compression Vari Autoencoder, Video-VAE, is designed for video generation tasks, achieving 16x16 spatial and 8x temporal compression ratios. Step-Video-T2V's performance is evaluated on a novel video generation benchmark, Step-Video-T2V-Eval, demonstrating its state-of-the-art text-to-video quality.
  arXiv  Detail & Related papers  (2025-02-14T15:58:10Z)
• STIV: Scalable Text and Image Conditioned Video Generation [84.2574247093223]
  We present a simple and scalable text-image-conditioned video generation method, named STIV. Our framework integrates image condition into a Diffusion Transformer (DiT) through frame replacement, while incorporating text conditioning. STIV can be easily extended to various applications, such as video prediction, frame, multi-view generation, and long video generation.
  arXiv  Detail & Related papers  (2024-12-10T18:27:06Z)
• xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations [120.52120919834988]
  xGen-SynVideo-1 is a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. VidVAE compresses video data both spatially and temporally, significantly reducing the length of visual tokens. DiT model incorporates spatial and temporal self-attention layers, enabling robust generalization across different timeframes and aspect ratios.
  arXiv  Detail & Related papers  (2024-08-22T17:55:22Z)
• Factorized-Dreamer: Training A High-Quality Video Generator with Limited and Low-Quality Data [14.489919164476982]
  High-quality (HQ) video synthesis is challenging because of the diverse and complex motions existed in real world. Most existing works struggle to address this problem by collecting large-scale captions, which are inaccessible to the community. We show that publicly available limited and low-quality (LQ) data are sufficient to train a HQ video generator without recaptioning or finetuning.
  arXiv  Detail & Related papers  (2024-08-19T16:08:00Z)
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer [55.515836117658985]
  We present CogVideoX, a large-scale text-to-video generation model based on diffusion transformer. It can generate 10-second continuous videos aligned with text prompt, with a frame rate of 16 fps and resolution of 768 * 1360 pixels.
  arXiv  Detail & Related papers  (2024-08-12T11:47:11Z)
• LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation [44.220329202024494]
  We present a few-shot-based tuning framework, LAMP, which enables text-to-image diffusion model Learn A specific Motion Pattern with 816 videos on a single GPU. Specifically, we design a first-frame-conditioned pipeline that uses an off-the-shelf text-to-image model for content generation. To capture the features of temporal dimension, we expand the pretrained 2D convolution layers of the T2I model to our novel temporal-spatial motion learning layers.
  arXiv  Detail & Related papers  (2023-10-16T19:03:19Z)
• Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators [70.17041424896507]
  Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. We propose a new task of zero-shot text-to-video generation using existing text-to-image synthesis methods. Our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data.
  arXiv  Detail & Related papers  (2023-03-23T17:01:59Z)
• Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation [31.882356164068753]
  To reproduce the success of text-to-image (T2I) generation, recent works in text-to-video (T2V) generation employ massive dataset for dataset for T2V generation. We propose Tune-A-Video is capable of producing temporally-coherent videos over various applications.
  arXiv  Detail & Related papers  (2022-12-22T09:43:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.