Towards High-Consistency Embodied World Model with Multi-View Trajectory Videos

• URL: http://arxiv.org/abs/2511.12882v2
• Date: Wed, 19 Nov 2025 09:11:12 GMT
• Title: Towards High-Consistency Embodied World Model with Multi-View Trajectory Videos
• Authors: Taiyi Su, Jian Zhu, Yaxuan Li, Chong Ma, Zitai Huang, Hanli Wang, Yi Xu,
• Abstract summary: Embodied world models aim to predict and interact with the physical world through visual observations and actions.<n>MTV-World introduces Multi-view Trajectory-Video control for precise visuomotor prediction.<n>MTV-World achieves precise control execution and accurate physical interaction modeling in complex dual-arm scenarios.
• Score: 24.111891848073288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embodied world models aim to predict and interact with the physical world through visual observations and actions. However, existing models struggle to accurately translate low-level actions (e.g., joint positions) into precise robotic movements in predicted frames, leading to inconsistencies with real-world physical interactions. To address these limitations, we propose MTV-World, an embodied world model that introduces Multi-view Trajectory-Video control for precise visuomotor prediction. Specifically, instead of directly using low-level actions for control, we employ trajectory videos obtained through camera intrinsic and extrinsic parameters and Cartesian-space transformation as control signals. However, projecting 3D raw actions onto 2D images inevitably causes a loss of spatial information, making a single view insufficient for accurate interaction modeling. To overcome this, we introduce a multi-view framework that compensates for spatial information loss and ensures high-consistency with physical world. MTV-World forecasts future frames based on multi-view trajectory videos as input and conditioning on an initial frame per view. Furthermore, to systematically evaluate both robotic motion precision and object interaction accuracy, we develop an auto-evaluation pipeline leveraging multimodal large models and referring video object segmentation models. To measure spatial consistency, we formulate it as an object location matching problem and adopt the Jaccard Index as the evaluation metric. Extensive experiments demonstrate that MTV-World achieves precise control execution and accurate physical interaction modeling in complex dual-arm scenarios.

Related papers

• Mirage2Matter: A Physically Grounded Gaussian World Model from Video [87.9732484393686]
  We present Simulate Anything, a graphics-driven world modeling and simulation framework.<n>Our approach reconstructs real-world environments into a photorealistic scene representation using 3D Gaussian Splatting (3DGS)<n>We then leverage generative models to recover a physically realistic representation and integrate it into a simulation environment via a precision calibration target.
  arXiv  Detail & Related papers  (2026-01-24T07:43:57Z)
• Walk through Paintings: Egocentric World Models from Internet Priors [65.30611174953958]
  We present the Egocentric World Model (EgoWM), a simple, architecture-agnostic method that transforms any pretrained video diffusion model into an action-conditioned world model.<n>Rather than training from scratch, we repurpose the rich world priors of Internet-scale video models and inject motor commands through lightweight conditioning layers.<n>Our approach scales naturally across embodiments and action spaces, ranging from 3-DoF mobile robots to 25-DoF humanoids.
  arXiv  Detail & Related papers  (2026-01-21T18:59:32Z)
• ByteLoom: Weaving Geometry-Consistent Human-Object Interactions through Progressive Curriculum Learning [19.292101162897975]
  We introduce ByteLoom, a framework that generates realistic HOI videos with geometrically consistent object illustration.<n>We first propose an RCM-cache mechanism that leverages Relative Coordinate Maps (RCM) as a universal representation to maintain object's geometry consistency.<n>We then design a training curriculum that enhances model capabilities in a progressive style and relaxes the demand of hand mesh.
  arXiv  Detail & Related papers  (2025-12-28T09:38:36Z)
• DynamicVerse: A Physically-Aware Multimodal Framework for 4D World Modeling [67.95038177144554]
  We introduce DynamicVerse, a physical-scale, multimodal 4D world modeling framework for dynamic real-world video.<n>We employ vision, geometric, and multimodal models to interpret metric-scale static geometry, real-world dynamic motion, instance-level masks, and holistic captions.<n> DynamicVerse delivers a large-scale dataset consisting of 100K+ videos with 800K+ annotated masks and 10M+ frames from internet videos.
  arXiv  Detail & Related papers  (2025-12-02T18:24:27Z)
• Geometry-aware 4D Video Generation for Robot Manipulation [28.709339959536106]
  We propose a 4D video generation model that enforces multi-view 3D consistency of videos by supervising the model with cross-view pointmap alignment during training.<n>This geometric supervision enables the model to learn a shared 3D representation of the scene, allowing it to predict future video sequences from novel viewpoints.<n>Compared to existing baselines, our method produces more visually stable and spatially aligned predictions across multiple simulated and real-world robotic datasets.
  arXiv  Detail & Related papers  (2025-07-01T18:01:41Z)
• 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)
• Physical Informed Driving World Model [47.04423342994622]
  DrivePhysica is an innovative model designed to generate realistic driving videos that adhere to essential physical principles.<n>We achieve state-of-the-art performance in driving video generation quality (3.96 FID and 38.06 FVD on the Nuscenes dataset) and downstream perception tasks.
  arXiv  Detail & Related papers  (2024-12-11T14:29:35Z)
• IRASim: A Fine-Grained World Model for Robot Manipulation [24.591694756757278]
  We present IRASim, a novel world model capable of generating videos with fine-grained robot-object interaction details.<n>We train a diffusion transformer and introduce a novel frame-level action-conditioning module within each transformer block to explicitly model and strengthen the action-frame alignment.
  arXiv  Detail & Related papers  (2024-06-20T17:50:16Z)
• 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)
• COPILOT: Human-Environment Collision Prediction and Localization from Egocentric Videos [62.34712951567793]
  The ability to forecast human-environment collisions from egocentric observations is vital to enable collision avoidance in applications such as VR, AR, and wearable assistive robotics. We introduce the challenging problem of predicting collisions in diverse environments from multi-view egocentric videos captured from body-mounted cameras. We propose a transformer-based model called COPILOT to perform collision prediction and localization simultaneously.
  arXiv  Detail & Related papers  (2022-10-04T17:49:23Z)
• TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
  TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
  arXiv  Detail & Related papers  (2021-04-08T20:01:00Z)
• Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction [24.72947291987545]
  Key challenge for an agent learning to interact with the world is to reason about physical properties of objects. We propose a novel approach for modeling the dynamics of a robot's interactions directly from unlabeled 3D point clouds and images.
  arXiv  Detail & Related papers  (2020-08-02T11:04:49Z)

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.