title: I2V-Adapter: A General Image-to-Video Adapter for Video Diffusion Models
summary: In the rapidly evolving domain of digital content generation, the focus has
  shifted from text-to-image (T2I) models to more advanced video diffusion
  models, notably text-to-video (T2V) and image-to-video (I2V). This paper
  addresses the intricate challenge posed by I2V: converting static images into
  dynamic, lifelike video sequences while preserving the original image fidelity.
  Traditional methods typically involve integrating entire images into diffusion
  processes or using pretrained encoders for cross attention. However, these
  approaches often necessitate altering the fundamental weights of T2I models,
  thereby restricting their reusability. We introduce a novel solution, namely
  I2V-Adapter, designed to overcome such limitations. Our approach preserves the
  structural integrity of T2I models and their inherent motion modules. The
  I2V-Adapter operates by processing noised video frames in parallel with the
  input image, utilizing a lightweight adapter module. This module acts as a
  bridge, efficiently linking the input to the model's self-attention mechanism,
  thus maintaining spatial details without requiring structural changes to the
  T2I model. Moreover, I2V-Adapter requires only a fraction of the parameters of
  conventional models and ensures compatibility with existing community-driven
  T2I models and controlling tools. Our experimental results demonstrate
  I2V-Adapter's capability to produce high-quality video outputs. This
  performance, coupled with its versatility and reduced need for trainable
  parameters, represents a substantial advancement in the field of AI-driven
  video generation, particularly for creative applications.
paper_authors._id: 658e3b153f3faee8ecaaac26
paper_authors.name: Xun Guo
paper_authors._id: 658e3b153f3faee8ecaaac27
paper_authors.name: Mingwu Zheng
paper_authors._id: 658e3b153f3faee8ecaaac28
paper_authors.name: Liang Hou
paper_authors._id: 658e3b153f3faee8ecaaac29
paper_authors.name: Yuan Gao
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-06-06T23:41:51.506Z
paper_authors._id: 66624861b01c3b590a8c289b
paper_authors.avatarUrl: /avatars/afd02857eb1632680d0d53da4488c8bc.svg
paper_authors.fullname: Haibin Huang
paper_authors.type: user
paper_authors.user: brotherhuang
paper_authors._id: 658e3b153f3faee8ecaaac2a
paper_authors.name: Yufan Deng
paper_authors._id: 658e3b153f3faee8ecaaac2b
paper_authors.name: Chongyang Ma
paper_authors._id: 658e3b153f3faee8ecaaac2c
paper_authors.name: Weiming Hu
paper_authors._id: 658e3b153f3faee8ecaaac2d
paper_authors.name: Zhengjun Zha
paper_authors._id: 658e3b153f3faee8ecaaac2e
paper_authors.name: Haibin Huang
paper_authors._id: 658e3b153f3faee8ecaaac2f
paper_authors.name: Pengfei Wan
paper_authors._id: 658e3b153f3faee8ecaaac30
paper_authors.name: Di Zhang
paper_title: I2V-Adapter: A General Image-to-Video Adapter for Video Diffusion Models
paper_summary: In the rapidly evolving domain of digital content generation, the focus has
  shifted from text-to-image (T2I) models to more advanced video diffusion
  models, notably text-to-video (T2V) and image-to-video (I2V). This paper
  addresses the intricate challenge posed by I2V: converting static images into
  dynamic, lifelike video sequences while preserving the original image fidelity.
  Traditional methods typically involve integrating entire images into diffusion
  processes or using pretrained encoders for cross attention. However, these
  approaches often necessitate altering the fundamental weights of T2I models,
  thereby restricting their reusability. We introduce a novel solution, namely
  I2V-Adapter, designed to overcome such limitations. Our approach preserves the
  structural integrity of T2I models and their inherent motion modules. The
  I2V-Adapter operates by processing noised video frames in parallel with the
  input image, utilizing a lightweight adapter module. This module acts as a
  bridge, efficiently linking the input to the model's self-attention mechanism,
  thus maintaining spatial details without requiring structural changes to the
  T2I model. Moreover, I2V-Adapter requires only a fraction of the parameters of
  conventional models and ensures compatibility with existing community-driven
  T2I models and controlling tools. Our experimental results demonstrate
  I2V-Adapter's capability to produce high-quality video outputs. This
  performance, coupled with its versatility and reduced need for trainable
  parameters, represents a substantial advancement in the field of AI-driven
  video generation, particularly for creative applications.
paper_ai_summary: I2V-Adapter enables the conversion of images to videos by integrating a lightweight adapter into existing T2I models, preserving motion modules and reducing parameter requirements.
paper_ai_keywords: video diffusion models text-to-video image-to-video diffusion processes pretrained encoders cross attention T2I models I2V-Adapter noised video frames self-attention mechanism parameter requirements community-driven T2I models controlling tools high-quality video outputs