Grid Diffusion Models for Text-to-Video Generation

• URL: http://arxiv.org/abs/2404.00234v1
• Date: Sat, 30 Mar 2024 03:50:43 GMT
• Title: Grid Diffusion Models for Text-to-Video Generation
• Authors: Taegyeong Lee, Soyeong Kwon, Taehwan Kim,
• Abstract summary: Most existing video generation methods use either a 3D U-Net architecture that considers the temporal dimension or autoregressive generation. We propose a simple but effective novel grid diffusion for text-to-video generation without temporal dimension in architecture and a large text-video paired dataset. Our proposed method outperforms the existing methods in both quantitative and qualitative evaluations.
• Score: 2.531998650341267
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in the diffusion models have significantly improved text-to-image generation. However, generating videos from text is a more challenging task than generating images from text, due to the much larger dataset and higher computational cost required. Most existing video generation methods use either a 3D U-Net architecture that considers the temporal dimension or autoregressive generation. These methods require large datasets and are limited in terms of computational costs compared to text-to-image generation. To tackle these challenges, we propose a simple but effective novel grid diffusion for text-to-video generation without temporal dimension in architecture and a large text-video paired dataset. We can generate a high-quality video using a fixed amount of GPU memory regardless of the number of frames by representing the video as a grid image. Additionally, since our method reduces the dimensions of the video to the dimensions of the image, various image-based methods can be applied to videos, such as text-guided video manipulation from image manipulation. Our proposed method outperforms the existing methods in both quantitative and qualitative evaluations, demonstrating the suitability of our model for real-world video generation.

Related papers

• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization [52.63845811751936]
  Video pre-training is challenging due to the modeling of its dynamics video. In this paper, we address such limitations in video pre-training with an efficient video decomposition. Our framework is both capable of comprehending and generating image and video content, as demonstrated by its performance across 13 multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-02-05T16:30:49Z)
• VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation [73.54366331493007]
  VideoGen is a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency. We leverage an off-the-shelf text-to-image generation model, e.g., Stable Diffusion, to generate an image with high content quality from the text prompt.
  arXiv  Detail & Related papers  (2023-09-01T11:14:43Z)
• Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising [43.35391175319815]
  This study explores the potential of extending the text-driven ability to the generation and editing of multi-text conditioned long videos. We introduce a novel paradigm dubbed Gen-L-Video, capable of extending off-the-shelf short video diffusion models. Our experimental outcomes reveal that our approach significantly broadens the generative and editing capabilities of video diffusion models.
  arXiv  Detail & Related papers  (2023-05-29T17:38:18Z)
• Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation [55.36617538438858]
  We propose a novel approach that strengthens the interaction between spatial and temporal perceptions. We curate a large-scale and open-source video dataset called HD-VG-130M.
  arXiv  Detail & Related papers  (2023-05-18T11:06:15Z)
• Imagen Video: High Definition Video Generation with Diffusion Models [64.06483414521222]
  Imagen Video is a text-conditional video generation system based on a cascade of video diffusion models. We find Imagen Video capable of generating videos of high fidelity, but also having a high degree of controllability and world knowledge.
  arXiv  Detail & Related papers  (2022-10-05T14:41:38Z)
• Make-A-Video: Text-to-Video Generation without Text-Video Data [69.20996352229422]
  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.
  arXiv  Detail & Related papers  (2022-09-29T13:59:46Z)
• Video Generation from Text Employing Latent Path Construction for Temporal Modeling [70.06508219998778]
  Video generation is one of the most challenging tasks in Machine Learning and Computer Vision fields of study. In this paper, we tackle the text to video generation problem, which is a conditional form of video generation. We believe that video generation from natural language sentences will have an important impact on Artificial Intelligence.
  arXiv  Detail & Related papers  (2021-07-29T06:28:20Z)
• TiVGAN: Text to Image to Video Generation with Step-by-Step Evolutionary Generator [34.7504057664375]
  We propose a novel training framework, Text-to-Image-to-Video Generative Adversarial Network (TiVGAN), which evolves frame-by-frame and finally produces a full-length video. Step-by-step learning process helps stabilize the training and enables the creation of high-resolution video based on conditional text descriptions.
  arXiv  Detail & Related papers  (2020-09-04T06:33:08Z)

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.