Free-Form Motion Control: A Synthetic Video Generation Dataset with Controllable Camera and Object Motions
- URL: http://arxiv.org/abs/2501.01425v2
- Date: Fri, 03 Jan 2025 05:42:56 GMT
- Title: Free-Form Motion Control: A Synthetic Video Generation Dataset with Controllable Camera and Object Motions
- Authors: Xincheng Shuai, Henghui Ding, Zhenyuan Qin, Hao Luo, Xingjun Ma, Dacheng Tao,
- Abstract summary: 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.
- Score: 78.65431951506152
- License:
- Abstract: Controlling the movements of dynamic objects and the camera within generated videos is a meaningful yet challenging task. Due to the lack of datasets with comprehensive motion annotations, existing algorithms can not simultaneously control the motions of both camera and objects, resulting in limited controllability over generated contents. To address this issue and facilitate the research in this field, 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, simulating common and complex real-world scenarios. The complete 6D pose information facilitates models learning to disentangle the motion effects from objects and the camera in a video. To validate the effectiveness and generalization of SynFMC, we further propose a method, Free-Form Motion Control (FMC). FMC enables independent or simultaneous control of object and camera movements, producing high-fidelity videos. Moreover, it is compatible with various personalized text-to-image (T2I) models for different content styles. Extensive experiments demonstrate that the proposed FMC outperforms previous methods across multiple scenarios.
Related papers
- MotionAgent: Fine-grained Controllable Video Generation via Motion Field Agent [58.09607975296408]
We propose MotionAgent, enabling fine-grained motion control for text-guided image-to-video generation.
The key technique is the motion field agent that converts motion information in text prompts into explicit motion fields.
We construct a subset of VBench to evaluate the alignment of motion information in the text and the generated video, outperforming other advanced models on motion generation accuracy.
arXiv Detail & Related papers (2025-02-05T14:26:07Z) - 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) - 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) - MotionMaster: Training-free Camera Motion Transfer For Video Generation [48.706578330771386]
We propose a novel training-free video motion transfer model, which disentangles camera motions and object motions in source videos.
Our model can effectively decouple camera-object motion and apply the decoupled camera motion to a wide range of controllable video generation tasks.
arXiv Detail & Related papers (2024-04-24T10:28:54Z) - Direct-a-Video: Customized Video Generation with User-Directed Camera Movement and Object Motion [34.404342332033636]
We introduce Direct-a-Video, a system that allows users to independently specify motions for multiple objects as well as camera's pan and zoom movements.
For camera movement, we introduce new temporal cross-attention layers to interpret quantitative camera movement parameters.
Both components operate independently, allowing individual or combined control, and can generalize to open-domain scenarios.
arXiv Detail & Related papers (2024-02-05T16:30:57Z) - MotionCtrl: A Unified and Flexible Motion Controller for Video Generation [77.09621778348733]
Motions in a video primarily consist of camera motion, induced by camera movement, and object motion, resulting from object movement.
This paper presents MotionCtrl, a unified motion controller for video generation designed to effectively and independently control camera and object motion.
arXiv Detail & Related papers (2023-12-06T17:49:57Z) - 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) - Learn the Force We Can: Enabling Sparse Motion Control in Multi-Object
Video Generation [26.292052071093945]
We propose an unsupervised method to generate videos from a single frame and a sparse motion input.
Our trained model can generate unseen realistic object-to-object interactions.
We show that YODA is on par with or better than state of the art video generation prior work in terms of both controllability and video quality.
arXiv Detail & Related papers (2023-06-06T19:50:02Z)
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.