MotionCom: Automatic and Motion-Aware Image Composition with LLM and Video Diffusion Prior
- URL: http://arxiv.org/abs/2409.10090v1
- Date: Mon, 16 Sep 2024 08:44:17 GMT
- Title: MotionCom: Automatic and Motion-Aware Image Composition with LLM and Video Diffusion Prior
- Authors: Weijing Tao, Xiaofeng Yang, Miaomiao Cui, Guosheng Lin,
- Abstract summary: MotionCom is a training-free motion-aware diffusion based image composition.
It enables seamless integration of target objects into new scenes with dynamically coherent results.
- Score: 51.672193627686
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This work presents MotionCom, a training-free motion-aware diffusion based image composition, enabling automatic and seamless integration of target objects into new scenes with dynamically coherent results without finetuning or optimization. Traditional approaches in this area suffer from two significant limitations: they require manual planning for object placement and often generate static compositions lacking motion realism. MotionCom addresses these issues by utilizing a Large Vision Language Model (LVLM) for intelligent planning, and a Video Diffusion prior for motion-infused image synthesis, streamlining the composition process. Our multi-modal Chain-of-Thought (CoT) prompting with LVLM automates the strategic placement planning of foreground objects, considering their potential motion and interaction within the scenes. Complementing this, we propose a novel method MotionPaint to distill motion-aware information from pretrained video diffusion models in the generation phase, ensuring that these objects are not only seamlessly integrated but also endowed with realistic motion. Extensive quantitative and qualitative results highlight MotionCom's superiority, showcasing its efficiency in streamlining the planning process and its capability to produce compositions that authentically depict motion and interaction.
Related papers
- Instance-Level Moving Object Segmentation from a Single Image with Events [84.12761042512452]
Moving object segmentation plays a crucial role in understanding dynamic scenes involving multiple moving objects.
Previous methods encounter difficulties in distinguishing whether pixel displacements of an object are caused by camera motion or object motion.
Recent advances exploit the motion sensitivity of novel event cameras to counter conventional images' inadequate motion modeling capabilities.
We propose the first instance-level moving object segmentation framework that integrates complementary texture and motion cues.
arXiv Detail & Related papers (2025-02-18T15:56:46Z) - 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) - 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) - Video Diffusion Models are Training-free Motion Interpreter and Controller [20.361790608772157]
This paper introduces a novel perspective to understand, localize, and manipulate motion-aware features in video diffusion models.
We present a new MOtion FeaTure (MOFT) by eliminating content correlation information and filtering motion channels.
arXiv Detail & Related papers (2024-05-23T17:59:40Z) - Motion Inversion for Video Customization [31.607669029754874]
We present a novel approach for motion in generation, addressing the widespread gap in the exploration of motion representation within video models.
We introduce Motion Embeddings, a set of explicit, temporally coherent embeddings derived from given video.
Our contributions include a tailored motion embedding for customization tasks and a demonstration of the practical advantages and effectiveness of our method.
arXiv Detail & Related papers (2024-03-29T14:14:22Z) - 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) - Learning Variational Motion Prior for Video-based Motion Capture [31.79649766268877]
We present a novel variational motion prior (VMP) learning approach for video-based motion capture.
Our framework can effectively reduce temporal jittering and failure modes in frame-wise pose estimation.
Experiments over both public datasets and in-the-wild videos have demonstrated the efficacy and generalization capability of our framework.
arXiv Detail & Related papers (2022-10-27T02:45:48Z) - AMP: Adversarial Motion Priors for Stylized Physics-Based Character
Control [145.61135774698002]
We propose a fully automated approach to selecting motion for a character to track in a given scenario.
High-level task objectives that the character should perform can be specified by relatively simple reward functions.
Low-level style of the character's behaviors can be specified by a dataset of unstructured motion clips.
Our system produces high-quality motions comparable to those achieved by state-of-the-art tracking-based techniques.
arXiv Detail & Related papers (2021-04-05T22:43:14Z)
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.