I2VControl: Disentangled and Unified Video Motion Synthesis Control
- URL: http://arxiv.org/abs/2411.17765v2
- Date: Sat, 30 Nov 2024 04:50:36 GMT
- Title: I2VControl: Disentangled and Unified Video Motion Synthesis Control
- Authors: Wanquan Feng, Tianhao Qi, Jiawei Liu, Mingzhen Sun, Pengqi Tu, Tianxiang Ma, Fei Dai, Songtao Zhao, Siyu Zhou, Qian He,
- Abstract summary: We present a disentangled and unified framework, namely I2VControl, that unifies multiple motion control tasks in image-to-video synthesis.
Our approach partitions the video into individual motion units and represents each unit with disentangled control signals.
Our methodology seamlessly integrates as a plug-in for pre-trained models and remains agnostic to specific model architectures.
- Score: 11.83645633418189
- License:
- Abstract: Video synthesis techniques are undergoing rapid progress, with controllability being a significant aspect of practical usability for end-users. Although text condition is an effective way to guide video synthesis, capturing the correct joint distribution between text descriptions and video motion remains a substantial challenge. In this paper, we present a disentangled and unified framework, namely I2VControl, that unifies multiple motion control tasks in image-to-video synthesis. Our approach partitions the video into individual motion units and represents each unit with disentangled control signals, which allows for various control types to be flexibly combined within our single system. Furthermore, our methodology seamlessly integrates as a plug-in for pre-trained models and remains agnostic to specific model architectures. We conduct extensive experiments, achieving excellent performance on various control tasks, and our method further facilitates user-driven creative combinations, enhancing innovation and creativity. The project page is: https://wanquanf.github.io/I2VControl .
Related papers
- Free-Form Motion Control: A Synthetic Video Generation Dataset with Controllable Camera and Object Motions [78.65431951506152]
We introduce a Synthetic dataset for Free-Form Motion Control (SynFMC)
The proposed SynFMC dataset includes diverse objects and environments and covers various motion patterns according to specific rules.
We further propose a method, Free-Form Motion Control (FMC), which enables independent or simultaneous control of object and camera movements.
arXiv Detail & Related papers (2025-01-02T18:59:45Z) - MotionBridge: Dynamic Video Inbetweening with Flexible Controls [29.029643539300434]
We introduce MotionBridge, a unified video inbetweening framework.
It allows flexible controls, including trajectory strokes, video editing masks, guide pixels, and text video.
We show that such multi-modal controls enable a more dynamic, customizable, and contextually accurate visual narrative.
arXiv Detail & Related papers (2024-12-17T18:59:33Z) - EasyControl: Transfer ControlNet to Video Diffusion for Controllable Generation and Interpolation [73.80275802696815]
We propose a universal framework called EasyControl for video generation.
Our method enables users to control video generation with a single condition map.
Our model demonstrates powerful image retention ability, resulting in high FVD and IS in UCF101 and MSR-VTT.
arXiv Detail & Related papers (2024-08-23T11:48:29Z) - AnyControl: Create Your Artwork with Versatile Control on Text-to-Image Generation [24.07613591217345]
Linguistic control enables effective content creation, but struggles with fine-grained control over image generation.
AnyControl develops a novel Multi-Control framework that extracts a unified multi-modal embedding to guide the generation process.
This approach enables a holistic understanding of user inputs, and produces high-quality, faithful results under versatile control signals.
arXiv Detail & Related papers (2024-06-27T07:40:59Z) - Image Conductor: Precision Control for Interactive Video Synthesis [90.2353794019393]
Filmmaking and animation production often require sophisticated techniques for coordinating camera transitions and object movements.
Image Conductor is a method for precise control of camera transitions and object movements to generate video assets from a single image.
arXiv Detail & Related papers (2024-06-21T17:55:05Z) - Fine-grained Controllable Video Generation via Object Appearance and
Context [74.23066823064575]
We propose fine-grained controllable video generation (FACTOR) to achieve detailed control.
FACTOR aims to control objects' appearances and context, including their location and category.
Our method achieves controllability of object appearances without finetuning, which reduces the per-subject optimization efforts for the users.
arXiv Detail & Related papers (2023-12-05T17:47:33Z) - Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning [50.60891619269651]
Control-A-Video is a controllable T2V diffusion model that can generate videos conditioned on text prompts and reference control maps like edge and depth maps.
We propose novel strategies to incorporate content prior and motion prior into the diffusion-based generation process.
Our framework generates higher-quality, more consistent videos compared to existing state-of-the-art methods in controllable text-to-video generation.
arXiv Detail & Related papers (2023-05-23T09:03:19Z) - ControlVideo: Training-free Controllable Text-to-Video Generation [117.06302461557044]
ControlVideo is a framework to enable natural and efficient text-to-video generation.
It generates both short and long videos within several minutes using one NVIDIA 2080Ti.
arXiv Detail & Related papers (2023-05-22T14:48:53Z)
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.