VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models

• URL: http://arxiv.org/abs/2312.00845v1
• Date: Fri, 1 Dec 2023 06:50:11 GMT
• Title: VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models
• Authors: Hyeonho Jeong, Geon Yeong Park, Jong Chul Ye
• Abstract summary: 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.
• Score: 58.93124686141781
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-video diffusion models have advanced video generation significantly. However, customizing these models to generate videos with tailored motions presents a substantial challenge. In specific, they encounter hurdles in (a) accurately reproducing motion from a target video, and (b) creating diverse visual variations. For example, straightforward extensions of static image customization methods to video often lead to intricate entanglements of appearance and motion data. To tackle this, here we present the Video Motion Customization (VMC) framework, 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. The diffusion process then preserves low-frequency motion trajectories while mitigating high-frequency motion-unrelated noise in image space. We validate our method against state-of-the-art video generative models across diverse real-world motions and contexts. Our codes, data and the project demo can be found at https://video-motion-customization.github.io

Related papers

• Reenact Anything: Semantic Video Motion Transfer Using Motion-Textual Inversion [9.134743677331517]
  We propose a pre-trained image-to-video model to disentangle appearance from motion. Our method, called motion-textual inversion, leverages our observation that image-to-video models extract appearance mainly from the (latent) image input. By operating on an inflated motion-text embedding containing multiple text/image embedding tokens per frame, we achieve a high temporal motion granularity. Our approach does not require spatial alignment between the motion reference video and target image, generalizes across various domains, and can be applied to various tasks.
  arXiv  Detail & Related papers  (2024-08-01T10:55:20Z)
• Controllable Longer Image Animation with Diffusion Models [12.565739255499594]
  We introduce an open-domain controllable image animation method using motion priors with video diffusion models. Our method achieves precise control over the direction and speed of motion in the movable region by extracting the motion field information from videos. We propose an efficient long-duration video generation method based on noise reschedule specifically tailored for image animation tasks.
  arXiv  Detail & Related papers  (2024-05-27T16:08:00Z)
• Animate Your Motion: Turning Still Images into Dynamic Videos [58.63109848837741]
  We introduce Scene and Motion Conditional Diffusion (SMCD), a novel methodology for managing multimodal inputs. SMCD incorporates a recognized motion conditioning module and investigates various approaches to integrate scene conditions. Our design significantly enhances video quality, motion precision, and semantic coherence.
  arXiv  Detail & Related papers  (2024-03-15T10:36:24Z)
• 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)
• MotionCrafter: One-Shot Motion Customization of Diffusion Models [66.44642854791807]
  We introduce MotionCrafter, a one-shot instance-guided motion customization method. MotionCrafter employs a parallel spatial-temporal architecture that injects the reference motion into the temporal component of the base model. During training, a frozen base model provides appearance normalization, effectively separating appearance from motion.
  arXiv  Detail & Related papers  (2023-12-08T16:31:04Z)
• MoVideo: Motion-Aware Video Generation with Diffusion Models [97.03352319694795]
  We propose a novel motion-aware generation (MoVideo) framework that takes motion into consideration from two aspects: video depth and optical flow. MoVideo achieves state-of-the-art results in both text-to-video and image-to-video generation, showing promising prompt consistency, frame consistency and visual quality.
  arXiv  Detail & Related papers  (2023-11-19T13:36:03Z)
• 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)
• LaMD: Latent Motion Diffusion for Video Generation [69.4111397077229]
  latent motion diffusion (LaMD) framework consists of a motion-decomposed video autoencoder and a diffusion-based motion generator. Results show that LaMD generates high-quality videos with a wide range of motions, from dynamics to highly controllable movements.
  arXiv  Detail & Related papers  (2023-04-23T10:32:32Z)

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.