InteractPro: A Unified Framework for Motion-Aware Image Composition
- URL: http://arxiv.org/abs/2409.10090v2
- Date: Mon, 21 Jul 2025 05:23:53 GMT
- Title: InteractPro: A Unified Framework for Motion-Aware Image Composition
- Authors: Weijing Tao, Xiaofeng Yang, Miaomiao Cui, Guosheng Lin,
- Abstract summary: We introduce InteractPro, a comprehensive framework for dynamic motion-aware image composition.<n>At its core is InteractPlan, an intelligent planner that leverages a Large Vision Language Model (LVLM) for scenario analysis and object placement.<n>Based on each scenario, InteractPlan selects between our two specialized modules: InteractPhys and InteractMotion.
- Score: 51.672193627686
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce InteractPro, a comprehensive framework for dynamic motion-aware image composition. At its core is InteractPlan, an intelligent planner that leverages a Large Vision Language Model (LVLM) for scenario analysis and object placement, determining the optimal composition strategy to achieve realistic motion effects. Based on each scenario, InteractPlan selects between our two specialized modules: InteractPhys and InteractMotion. InteractPhys employs an enhanced Material Point Method (MPM)-based simulation to produce physically faithful and controllable object-scene interactions, capturing diverse and abstract events that require true physical modeling. InteractMotion, in contrast, is a training-free method based on pretrained video diffusion. Traditional composition approaches suffer from two major limitations: requiring manual planning for object placement and generating static, motionless outputs. By unifying simulation-based and diffusion-based methods under planner guidance, InteractPro overcomes these challenges, ensuring richly motion-aware compositions. Extensive quantitative and qualitative evaluations demonstrate InteractPro's effectiveness in producing controllable, and coherent compositions across varied scenarios.
Related papers
- A Structure-aware and Motion-adaptive Framework for 3D Human Pose Estimation with Mamba [18.376143217023934]
We propose a structure-aware and motion-adaptive framework to capture spatial joint topology.<n>Through the above key modules, our algorithm enables structure-aware and motion-adaptive pose lifting.
arXiv Detail & Related papers (2025-07-26T07:59:52Z) - HOComp: Interaction-Aware Human-Object Composition [62.93211305213214]
HOComp is a novel approach for compositing a foreground object onto a human-centric background image.<n> Experimental results on our dataset show that HOComp effectively generates human-object interactions with consistent appearances.
arXiv Detail & Related papers (2025-07-22T17:59:21Z) - Learning Video Generation for Robotic Manipulation with Collaborative Trajectory Control [72.00655365269]
We present RoboMaster, a novel framework that models inter-object dynamics through a collaborative trajectory formulation.<n>Unlike prior methods that decompose objects, our core is to decompose the interaction process into three sub-stages: pre-interaction, interaction, and post-interaction.<n>Our method outperforms existing approaches, establishing new state-of-the-art performance in trajectory-controlled video generation for robotic manipulation.
arXiv Detail & Related papers (2025-06-02T17:57:06Z) - MotionDiff: Training-free Zero-shot Interactive Motion Editing via Flow-assisted Multi-view Diffusion [20.142107033583027]
MotionDiff is a training-free zero-shot diffusion method that leverages optical flow for complex multi-view motion editing.
It outperforms other physics-based generative motion editing methods in achieving high-quality multi-view consistent motion results.
MotionDiff does not require retraining, enabling users to conveniently adapt it for various down-stream tasks.
arXiv Detail & Related papers (2025-03-22T08:32:56Z) - 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) - Learning semantical dynamics and spatiotemporal collaboration for human pose estimation in video [3.2195139886901813]
We present a novel framework that learns multi-level semantical dynamics and multi-frame human pose estimation.<n>Specifically, we first design a multi-masked context and pose reconstruction strategy.<n>This strategy stimulates the model to explore multi-temporal semantic relationships among frames by progressively masking the features of optical (patch) cubes and frames.
arXiv Detail & Related papers (2025-02-15T00:35:34Z) - 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) - A Unified Framework for Motion Reasoning and Generation in Human Interaction [28.736843383405603]
We introduce Versatile Interactive Motion-language model, which integrates both language and motion modalities.<n>VIM is capable of simultaneously understanding and generating both motion and text modalities.<n>We evaluate VIM across multiple interactive motion-related tasks, including motion-to-text, text-to-motion, reaction generation, motion editing, and reasoning about motion sequences.
arXiv Detail & Related papers (2024-10-08T02:23:53Z) - 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) - Motion Flow Matching for Human Motion Synthesis and Editing [75.13665467944314]
We propose emphMotion Flow Matching, a novel generative model for human motion generation featuring efficient sampling and effectiveness in motion editing applications.
Our method reduces the sampling complexity from thousand steps in previous diffusion models to just ten steps, while achieving comparable performance in text-to-motion and action-to-motion generation benchmarks.
arXiv Detail & Related papers (2023-12-14T12:57:35Z) - Controllable Human-Object Interaction Synthesis [77.56877961681462]
We propose Controllable Human-Object Interaction Synthesis (CHOIS) to generate synchronized object motion and human motion in 3D scenes.
Here, language descriptions inform style and intent, and waypoints, which can be effectively extracted from high-level planning, ground the motion in the scene.
Our module seamlessly integrates with a path planning module, enabling the generation of long-term interactions in 3D environments.
arXiv Detail & Related papers (2023-12-06T21:14:20Z) - UniQuadric: A SLAM Backend for Unknown Rigid Object 3D Tracking and
Light-Weight Modeling [7.626461564400769]
We propose a novel SLAM backend that unifies ego-motion tracking, rigid object motion tracking, and modeling.
Our system showcases the potential application of object perception in complex dynamic scenes.
arXiv Detail & Related papers (2023-09-29T07:50:09Z) - Alignment-free HDR Deghosting with Semantics Consistent Transformer [76.91669741684173]
High dynamic range imaging aims to retrieve information from multiple low-dynamic range inputs to generate realistic output.
Existing methods often focus on the spatial misalignment across input frames caused by the foreground and/or camera motion.
We propose a novel alignment-free network with a Semantics Consistent Transformer (SCTNet) with both spatial and channel attention modules.
arXiv Detail & Related papers (2023-05-29T15:03:23Z) - 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) - Modeling Motion with Multi-Modal Features for Text-Based Video
Segmentation [56.41614987789537]
Text-based video segmentation aims to segment the target object in a video based on a describing sentence.
We propose a method to fuse and align appearance, motion, and linguistic features to achieve accurate segmentation.
arXiv Detail & Related papers (2022-04-06T02:42:33Z) - 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.