KineBench: Benchmarking Embodied World Models via IDM-Free Kinematic Grounding

• URL: http://arxiv.org/abs/2607.19876v1
• Date: Wed, 22 Jul 2026 08:04:17 GMT
• Title: KineBench: Benchmarking Embodied World Models via IDM-Free Kinematic Grounding
• Abstract summary: evaluating the physical consistency of embodied world models is a critical open challenge.<n>Existing frameworks almost exclusively rely on Inverse Dynamics Models(IDMs) for action extraction.<n>We present KineBench, an IDM-free closed-loop benchmark for EWMs.
• Score: 58.541098216700796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evaluating the physical consistency of embodied world models(EWMs) is a critical open challenge. While closed-loop evaluation via simulator rollouts offers a more faithful assessment of physical plausibility than open-loop alternatives, existing frameworks almost exclusively rely on Inverse Dynamics Models(IDMs) for action extraction. Due to the intricate mapping from 2D pixel space to 3D kinematic space, the learned IDMs can be brittle to data outside their training distribution, resulting in unreliable action extraction from the generated videos with novel objects and scenarios. This creates an unavoidable attribution ambiguity between world model inaccuracies and extractor errors. To reduce this ambiguity, we present KineBench, an IDM-free closed-loop benchmark for EWMs, built upon an explicit kinematic grounding pipeline. Given a generated video, KineBench employs cascaded visual foundation models to directly extract 6D end-effector poses from individual frames, which are then executed in a physics simulator for closed-loop validation. Beyond execution-based task success, KineBench incorporates two classical 3D kinematic metrics--Spectral Arc Length (SPARC) and the Maruyama Manipulability Index--to characterize trajectory smoothness and kinematic feasibility from a robot-centric perspective. Built on 20 diverse manipulation tasks in ManiSkill3, KineBench evaluates EWMs across four progressive suites: basic execution, task transfer, visual out-of-distribution generalization, and complexity-conditioned scaling. Evaluation across frontier models reveals task-complexity-bounded nonlinear scaling in embodied video generation, providing empirical guidance for future data-scaling strategies.

Related papers

• Stream3Dv2: Geometric-Semantic Fusion Enhanced Streaming Zero-Shot 3D Scene Understanding [7.823301846592561]
  Stream3Dv2 is a training-free framework designed for robust streaming 3D perception.<n>We introduce a comprehensive geometric-semantic fusion mechanism that resolves geometric noise and semantic ambiguity.<n>Experiments on public datasets demonstrate that Stream3Dv2 consistently outperforms existing baselines in foundational open-vocabulary streaming 3D segmentation and detection.
  arXiv  Detail & Related papers  (2026-08-21T14:13:02Z)
• Real-World Cooperative Bimanual Dexterous Grasp of Large Objects from Single-View Observations [18.70687400045831]
  Bimanual dexterous grasping of large objects is a critical challenge in robotic manipulation.<n>We propose a real-world bimanual grasping framework that includes a multimodal dataset capturing joint angles, visual observations and force signals.<n>Our approach enables the synthesis of executable bimanual grasps from single-view inputs, reducing dependence on complete 3D object models.
  arXiv  Detail & Related papers  (2026-08-11T02:24:27Z)
• Instruct-Particulate: Scaling Feed-Forward 3D Object Articulation with Kinematic Control [86.64250947574749]
  Reconstructing articulated 3D objects is important for animation, gaming, and robotic simulations.<n>Recent neural networks can estimate the articulated structure of 3D objects, but their generalization remains limited by the scarcity of annotated data.<n>We introduce Instruct-Particulate, a model that takes a 3D mesh together with a target kinematic specification.
  arXiv  Detail & Related papers  (2026-06-12T17:59:36Z)
• $μ_0$: A Scalable 3D Interaction-Trace World Model [54.38434730157622]
  We present a scalable world model based on 3D traces.<n>Our TraceExtract system automatically extracts 3D supervision.<n>These results establish 3D traces as a scalable and transferable representation for cross-embodiment manipulation.
  arXiv  Detail & Related papers  (2026-06-11T17:59:56Z)
• Ada3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic Manipulation [53.750389076941396]
  Diffusion-based visuomotor policies capture multimodal action distributions through iterative denoising, but their high inference latency limits real-time robotic control.<n>We propose Ada3Drift, which learns a training-time drifting field that attracts predicted actions toward expert demonstration modes.<n>Ada3Drift achieves state-of-the-art performance while requiring $10times$ fewer function evaluations than diffusion-based alternatives.
  arXiv  Detail & Related papers  (2026-03-12T14:36:53Z)
• DragMesh: Interactive 3D Generation Made Easy [12.832539752284466]
  DragMesh is a robust framework for real-time interactive 3D articulation.<n>Our core contribution is a novel decoupled kinematic reasoning and motion generation framework.
  arXiv  Detail & Related papers  (2025-12-06T13:10:44Z)
• UniMo: Unifying 2D Video and 3D Human Motion with an Autoregressive Framework [54.337290937468175]
  We propose UniMo, an autoregressive model for joint modeling of 2D human videos and 3D human motions within a unified framework.<n>We show that our method simultaneously generates corresponding videos and motions while performing accurate motion capture.
  arXiv  Detail & Related papers  (2025-12-03T16:03:18Z)
• IDSplat: Instance-Decomposed 3D Gaussian Splatting for Driving Scenes [25.939318593012484]
  Reconstructing dynamic driving scenes is essential for developing autonomous systems through sensor-realistic simulation.<n>We present IDSplat, a self-supervised 3D Gaussian Splatting framework that reconstructs dynamic scenes with explicit instance decomposition and learnable motion trajectories.<n>Our method achieves competitive reconstruction quality while maintaining instance-level decomposition and generalizes across diverse sequences and view densities without retraining.
  arXiv  Detail & Related papers  (2025-11-24T15:48:08Z)
• Towards High-Consistency Embodied World Model with Multi-View Trajectory Videos [24.111891848073288]
  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.
  arXiv  Detail & Related papers  (2025-11-17T02:17:04Z)
• URDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model [76.08429266631823]
  We propose an end-to-end automatic reconstruction framework based on a 3D multimodal large language model (MLLM)<n>URDF-Anything utilizes an autoregressive prediction framework based on point-cloud and text multimodal input to jointly optimize geometric segmentation and kinematic parameter prediction.<n> Experiments on both simulated and real-world datasets demonstrate that our method significantly outperforms existing approaches.
  arXiv  Detail & Related papers  (2025-11-02T13:45:51Z)
• Joint Flow Trajectory Optimization For Feasible Robot Motion Generation from Video Demonstrations [8.133207162076877]
  We propose a framework for grasp pose generation and object trajectory imitation under the video-based Learning-from-Demonstration (LfD) paradigm.<n>Rather than directly imitating human hand motions, our method treats demonstrations as object-centric guides.<n>We validate our approach in both simulation and real-world experiments across diverse real-world manipulation tasks.
  arXiv  Detail & Related papers  (2025-09-25T03:11:07Z)
• Mixed Diffusion for 3D Indoor Scene Synthesis [55.94569112629208]
  We present MiDiffusion, a novel mixed discrete-continuous diffusion model designed to synthesize plausible 3D indoor scenes.<n>We show it outperforms state-of-the-art autoregressive and diffusion models in floor-conditioned 3D scene synthesis.
  arXiv  Detail & Related papers  (2024-05-31T17:54:52Z)

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.