DreamVideo-2: Zero-Shot Subject-Driven Video Customization with Precise Motion Control
- URL: http://arxiv.org/abs/2410.13830v1
- Date: Thu, 17 Oct 2024 17:52:57 GMT
- Title: DreamVideo-2: Zero-Shot Subject-Driven Video Customization with Precise Motion Control
- Authors: Yujie Wei, Shiwei Zhang, Hangjie Yuan, Xiang Wang, Haonan Qiu, Rui Zhao, Yutong Feng, Feng Liu, Zhizhong Huang, Jiaxin Ye, Yingya Zhang, Hongming Shan,
- Abstract summary: 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.
- Score: 42.506988751934685
- License:
- Abstract: Recent advances in customized video generation have enabled users to create videos tailored to both specific subjects and motion trajectories. However, existing methods often require complicated test-time fine-tuning and struggle with balancing subject learning and motion control, limiting their real-world applications. In this paper, we present DreamVideo-2, a zero-shot video customization framework capable of generating videos with a specific subject and motion trajectory, guided by a single image and a bounding box sequence, respectively, and without the need for test-time fine-tuning. Specifically, we introduce reference attention, which leverages the model's inherent capabilities for subject learning, and devise a mask-guided motion module to achieve precise motion control by fully utilizing the robust motion signal of box masks derived from bounding boxes. While these two components achieve their intended functions, we empirically observe that motion control tends to dominate over subject learning. To address this, we propose two key designs: 1) the masked reference attention, which integrates a blended latent mask modeling scheme into reference attention to enhance subject representations at the desired positions, and 2) a reweighted diffusion loss, which differentiates the contributions of regions inside and outside the bounding boxes to ensure a balance between subject and motion control. Extensive experimental results on a newly curated dataset demonstrate that DreamVideo-2 outperforms state-of-the-art methods in both subject customization and motion control. The dataset, code, and models will be made publicly available.
Related papers
- MotionMatcher: Motion Customization of Text-to-Video Diffusion Models via Motion Feature Matching [27.28898943916193]
Text-to-video (T2V) diffusion models have promising capabilities in synthesizing realistic videos from input text prompts.
In this work, we tackle the motion customization problem, where a reference video is provided as motion guidance.
We propose MotionMatcher, a motion customization framework that fine-tunes the pre-trained T2V diffusion model at the feature level.
arXiv Detail & Related papers (2025-02-18T19:12:51Z) - 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) - 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) - MotionBooth: Motion-Aware Customized Text-to-Video Generation [44.41894050494623]
MotionBooth is a framework designed for animating customized subjects with precise control over both object and camera movements.
We efficiently fine-tune a text-to-video model to capture the object's shape and attributes accurately.
Our approach presents subject region loss and video preservation loss to enhance the subject's learning performance.
arXiv Detail & Related papers (2024-06-25T17:42:25Z) - Ctrl-V: Higher Fidelity Video Generation with Bounding-Box Controlled Object Motion [8.068194154084967]
This paper tackles a challenge of how to exert precise control over object motion for realistic video synthesis.
To accomplish this, we control object movements using bounding boxes and extend this control to the renderings of 2D or 3D boxes in pixel space.
Our method, Ctrl-V, leverages modified and fine-tuned Stable Video Diffusion (SVD) models to solve both trajectory and video generation.
arXiv Detail & Related papers (2024-06-09T03:44:35Z) - 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) - TrailBlazer: Trajectory Control for Diffusion-Based Video Generation [11.655256653219604]
Controllability in text-to-video (T2V) generation is often a challenge.
We introduce the concept of keyframing, allowing the subject trajectory and overall appearance to be guided by both a moving bounding box and corresponding prompts.
Despite the simplicity of the bounding box guidance, the resulting motion is surprisingly natural, with emergent effects including perspective and movement toward the virtual camera as the box size increases.
arXiv Detail & Related papers (2023-12-31T10:51:52Z) - DreamVideo: Composing Your Dream Videos with Customized Subject and
Motion [52.7394517692186]
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.
arXiv Detail & Related papers (2023-12-07T16:57:26Z) - TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Models [75.20168902300166]
We propose TrackDiffusion, a novel video generation framework affording fine-grained trajectory-conditioned motion control.
A pivotal component of TrackDiffusion is the instance enhancer, which explicitly ensures inter-frame consistency of multiple objects.
generated video sequences by our TrackDiffusion can be used as training data for visual perception models.
arXiv Detail & Related papers (2023-12-01T15:24:38Z)
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.