DreamVideo: Composing Your Dream Videos with Customized Subject and Motion

• URL: http://arxiv.org/abs/2312.04433v1
• Date: Thu, 7 Dec 2023 16:57:26 GMT
• Title: DreamVideo: Composing Your Dream Videos with Customized Subject and Motion
• Authors: Yujie Wei, Shiwei Zhang, Zhiwu Qing, Hangjie Yuan, Zhiheng Liu, Yu Liu, Yingya Zhang, Jingren Zhou, Hongming Shan
• Abstract summary: We present DreamVideo, a novel approach to generating personalized videos from a few static images of the desired subject. DreamVideo decouples this task into two stages, subject learning and motion learning, by leveraging a pre-trained video diffusion model. In motion learning, we architect a motion adapter and fine-tune it on the given videos to effectively model the target motion pattern.
• Score: 52.7394517692186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Customized generation using diffusion models has made impressive progress in image generation, but remains unsatisfactory in the challenging video generation task, as it requires the controllability of both subjects and motions. To that end, we present DreamVideo, a novel approach to generating personalized videos from a few static images of the desired subject and a few videos of target motion. DreamVideo decouples this task into two stages, subject learning and motion learning, by leveraging a pre-trained video diffusion model. The subject learning aims to accurately capture the fine appearance of the subject from provided images, which is achieved by combining textual inversion and fine-tuning of our carefully designed identity adapter. In motion learning, we architect a motion adapter and fine-tune it on the given videos to effectively model the target motion pattern. Combining these two lightweight and efficient adapters allows for flexible customization of any subject with any motion. Extensive experimental results demonstrate the superior performance of our DreamVideo over the state-of-the-art methods for customized video generation. Our project page is at https://dreamvideo-t2v.github.io.

Related papers

• MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video Generation [65.74312406211213]
  This paper presents a method that allows users to design cinematic video shots in the context of image-to-video generation. By connecting insights from classical computer graphics and contemporary video generation techniques, we demonstrate the ability to achieve 3D-aware motion control in I2V synthesis.
  arXiv  Detail & Related papers  (2025-02-06T18:41:04Z)
• VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models [71.9811050853964]
  VideoJAM is a novel framework that instills an effective motion prior to video generators. VideoJAM achieves state-of-the-art performance in motion coherence. These findings emphasize that appearance and motion can be complementary and, when effectively integrated, enhance both the visual quality and the coherence of video generation.
  arXiv  Detail & Related papers  (2025-02-04T17:07:10Z)
• MoTrans: Customized Motion Transfer with Text-driven Video Diffusion Models [59.10171699717122]
  MoTrans is a customized motion transfer method enabling video generation of similar motion in new context. multimodal representations from recaptioned prompt and video frames promote the modeling of appearance. Our method effectively learns specific motion pattern from singular or multiple reference videos.
  arXiv  Detail & Related papers  (2024-12-02T10:07:59Z)
• DreamVideo-2: Zero-Shot Subject-Driven Video Customization with Precise Motion Control [42.506988751934685]
  We present DreamVideo-2, a zero-shot video customization framework capable of generating videos with a specific subject and motion trajectory. Specifically, we introduce reference attention, which leverages the model's inherent capabilities for subject learning. We devise a mask-guided motion module to achieve precise motion control by fully utilizing the robust motion signal of box masks.
  arXiv  Detail & Related papers  (2024-10-17T17:52:57Z)
• Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models [48.56724784226513]
  We propose Customize-A-Video that models the motion from a single reference video and adapts it to new subjects and scenes with both spatial and temporal varieties. The proposed modules are trained in a staged pipeline and inferred in a plug-and-play fashion, enabling easy extensions to various downstream tasks.
  arXiv  Detail & Related papers  (2024-02-22T18:38:48Z)
• VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models [58.93124686141781]
  Video Motion Customization (VMC) is a novel one-shot tuning approach crafted to adapt temporal attention layers within video diffusion models. Our approach introduces a novel motion distillation objective using residual vectors between consecutive frames as a motion reference. We validate our method against state-of-the-art video generative models across diverse real-world motions and contexts.
  arXiv  Detail & Related papers  (2023-12-01T06:50:11Z)
• MotionDirector: Motion Customization of Text-to-Video Diffusion Models [24.282240656366714]
  Motion Customization aims to adapt existing text-to-video diffusion models to generate videos with customized motion. We propose MotionDirector, with a dual-path LoRAs architecture to decouple the learning of appearance and motion. Our method also supports various downstream applications, such as the mixing of different videos with their appearance and motion respectively, and animating a single image with customized motions.
  arXiv  Detail & Related papers  (2023-10-12T16:26:18Z)

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.