KineWorld: Action-Induced Transport Fields for Embodied World Modeling

Ziying Song; Yuchen Liu; Zhuoran Xu; Ziyang Liu; Jian Jin; Jiangtao Su; Haibao Yu; Lei Yang

cs.CVOct 5, 2026

KineWorld: Action-Induced Transport Fields for Embodied World Modeling

Authors: Ziying Song, Yuchen Liu, Zhuoran Xu, Ziyang Liu, Jian Jin, Jiangtao Su, Haibao Yu, Lei Yang, +1 more

Organizations: Nanyang Technological University · North University of China · Psibot · The Hong Kong Polytechnic University · China Academy of Information and Communications Technology · University of Hong Kong · Peking University

Abstract

Embodied world models predict the visual consequences of candidate actions before execution. However, existing action-conditioned world models often adopt uniformly weighted visual generation objectives that can be misaligned with embodied prediction needs. Even with explicit motion conditioning, these objectives can underemphasize spatially sparse changes that are critical to interaction. We propose KineWorld, a transport-aware world-modeling framework that extends robot kinematics from motion conditioning to the spatial allocation of generative supervision. Kinematic Transport Lifting (KTL) constructs renderer-derived, camera-aligned transport fields from commanded robot motion. Transport-Aware World Diffusion (TAWD) calibrates their motion support on the video-latent grid and reweights future-RGB flow matching through a normalized mixture of uniform and transport-focused distributions. We train KineWorld using ALOHA-AgileX bimanual manipulation data from RoboTwin 2.0. KineWorld achieves an EWMScore-P of 68.95 in single-view evaluation and a TWB-Score of 54.82 in multi-view evaluation. These results support a shift from appearance fitting toward action-consequence modeling for embodied decision-making.

Figures & tables

Figure 1: (a) Token-conditioned WAMs use numerical trajectories or learned action embeddings for video generation ( Zhu et al., 2024 ; Guo et al., 2025 ; Liao et al., 2025 ) , but typically apply uniform RGB supervision , potentially underemphasizing interaction-critical local changes . (b) Auxiliary-cue WAMs incorporate motion-focused auxiliary cues , such as masks ( Chen et al., 2026c ) , action images ( Zhen et al., 2026 ) , optical flow ( Chen et al., 2026b ) , or projected kinematic fields ( Yang et al., 2026 ) , to improve motion localization. These cues alone do not directly change the spatial weighting of the primary RGB objective, potentially leaving motion localization and supervision priorities misaligned . (c) KineWorld introduces action-guided RGB supervision by lifting camera-aligned kinematic transport onto the video latent grid and reweighting flow matching, enabling action-consequence-oriented supervision while preserving the total token weight.

Figure 2: Overview of KineWorld. Kinematic Transport Lifting (KTL) converts the commanded robot rollout into camera-aligned transport and injects its latent representation as a clean structural condition. Transport-Aware World Diffusion (TAWD) calibrates the transport support and derives mean-one token weights for the flow-matching objective, emphasizing action-responsive regions under a fixed weight budget. The weighting branch is used only during training, while the inherited action expert remains unchanged.

$\operatorname{Method}$	$\operatorname{Overall}$	$\operatorname{Generation\ Quality}$			$\operatorname{Embodied\ Capability}$
$\operatorname{Method}$	$\operatorname{EWM}\uparrow$	$\operatorname{Vis.}\uparrow$	$\operatorname{Mot.}\uparrow$	$\operatorname{Cont.}\uparrow$	$\operatorname{Phys.}\uparrow$	$\operatorname{3D}\uparrow$	$\operatorname{Ctrl.}\uparrow$
JF_World ( DreamX Team et al., 2026 )	64.85	66.52	30.66	56.91	69.27	97.88	88.06
BWM-Super ( BWM Team, 2026 )	64.30	67.20	30.16	58.36	64.15	97.35	87.19
BWM-Turbo ( BWM Team, 2026 )	63.96	66.99	30.10	57.22	64.46	97.74	86.05
FlowWAM-FiveAges ( Chen et al., 2026b )	63.87	66.60	29.92	54.47	65.97	98.48	88.09
WoVR_Plus ( Jiang et al., 2026 )	57.89	58.37	30.52	57.91	47.95	85.31	80.75

Table 1: Single-View Reference Profiles. EWM denotes EWMScore-P ( Shang et al., 2026 ) ; metric definitions and aggregation appear in Appendix A.3.1 . These aggregates do not constitute a shared RoboTwin 2.0 evaluation. Source status is documented in Appendix A.3 .

$\operatorname{Method}$	$\operatorname{Overall\ Fidelity}$				$\operatorname{View\ PSNR\ (dB)}$			$\operatorname{Boundary\ PSNR\ (dB)}$
$\operatorname{Method}$	$\operatorname{PSNR}\uparrow$	$\operatorname{SSIM}\uparrow$	$\operatorname{N\mbox{-}MAE}\downarrow$	$\operatorname{Mot.\ Corr.}\uparrow$	$\operatorname{Head}\uparrow$	$\operatorname{Left}\uparrow$	$\operatorname{Right}\uparrow$	$\operatorname{First}\uparrow$	$\operatorname{Last}\uparrow$
KineWorld (white wrist flow)	35.35	0.9622	16.532	0.9132	36.10	36.13	33.82	42.01	32.32
KineWorld (all-view RAFT)	42.76	0.9860	5.713	0.9953	41.88	43.28	43.13	42.65	42.76

Table 5: KTL Ablation. Three-view validation diagnostic on 100 episodes (300 videos per variant). Dataset scope and metric definitions appear in Appendix A.8.1 .

Figure 3: Retained qualitative comparison between Wan2.2 and KineWorld. Decoded episode-550 frames at $t=1.0$ , $2.0$ , $3.0$ , $4.0$ , and $5.0$ s from 24-fps videos. This is a descriptive rollout comparison, not an action- or checkpoint-matched ablation.

Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

Partition	Episode indices per task	Episodes	Membership
Development training	0–35	1,800	Disjoint from validation
Validation	36–39	200	Included in combined training
Combined training	0–39	2,000	Development $\cup$ validation
Held-out	40–49	500	Disjoint from both training sets

Appendix

Table A.1: RoboTwin 2.0 Clean-50 Data Splits. Indices are local to each of the 50 tasks. Counts refer to unique episodes; combined training includes the validation partition.

$\operatorname{Method}$	$\operatorname{Overall}$	$\operatorname{Visual}$			$\operatorname{Motion}$			$\operatorname{Content}$			$\operatorname{Physics}$		$\operatorname{3D}$		$\operatorname{Control}$
$\operatorname{Method}$	EWM	AES	IMG	JEPA	DYN	FL	MS	BG	PC	SC	INT	TA	DEP	PER	IF	SA
JF_World ( DreamX Team et al., 2026 )	64.85	43.38	63.25	92.93	22.90	5.81	63.26	84.52	14.29	71.93	81.40	57.15	98.55	97.20	85.60	90.53
BWM-Super ( BWM Team, 2026 )	64.30	44.39	62.38	94.83	22.01	5.77	62.70	84.31	18.92	71.86	79.20	49.09	97.51	97.20	85.20	89.19
BWM-Turbo ( BWM Team, 2026 )	63.96	44.69	60.27	96.00	22.23	5.76	62.31	82.97	17.33	71.37	78.60	50.32	98.48	97.00	82.80	89.31
FlowWAM-FiveAges ( Chen et al., 2026b )	63.87	44.00	64.68	91.12	22.26	5.80	61.69	79.62	15.08	68.71	79.60	52.33	99.15	97.80	87.20	88.98
WoVR Plus ( Jiang et al., 2026 )	57.89	40.34	46.78	87.99	22.92	5.80	62.83	86.99	15.46	71.27	67.20	28.69	85.02	85.60	73.00	88.50

Appendix

Table A.2: Complete Single-View Reference Profiles.

\operatorname{EWM}

: EWMScore-P;

\operatorname{AES}

: Aesthetic Quality;

\operatorname{IMG}

: Image Quality;

\operatorname{JEPA}

: JEPA Similarity;

\operatorname{DYN}

: Dynamic Degree;

\operatorname{FL}

: Flow Score;

\operatorname{MS}

: Motion Smoothness;

\operatorname{BG}

: Background Consistency;

\operatorname{PC}

: Photometric Consistency;

\operatorname{SC}

: Subject Consistency;

\operatorname{INT}

: Interaction Quality;

\operatorname{TA}

: Trajectory Accuracy;

\operatorname{DEP}

: Depth Accuracy;

\operatorname{PER}

: Perspectivity;

\operatorname{IF}

: Instruction Following;

\operatorname{SA}

: Semantic Alignment. Original score scales are preserved. † denotes the author-supplied candidate profile.

$\operatorname{Method}$	$\operatorname{AES}\uparrow$	$\operatorname{IMG}\uparrow$	$\operatorname{BG}\uparrow$	$\operatorname{SC}\uparrow$	$\operatorname{PC}\uparrow$	$\operatorname{INT}\uparrow$	$\operatorname{PER}\uparrow$	$\operatorname{IF}\uparrow$	$\operatorname{DYN}$	$\operatorname{FL}$
Wan2.2-TI2V-5B ( Wan Team, 2025 )	42.10	44.42	85.17	80.90	0.96	68.68	88.52	76.00	–	–
KineWorld	45.26	61.21	87.19	75.53	16.43	79.20	97.30	85.84	0.53	1.88

Appendix

Table A.3: Complete Single-View Local Diagnostics. Additional components for Table 4 ; DYN and FL retain the local evaluator scale.

Figure A.1: Single-view local diagnostic profiles. Reported AES–IF values from Table A.3 , retaining the normalized score scale and metric abbreviations in Table A.2 . Scores are compared within each metric. The profiles describe the reported local systems rather than a checkpoint-matched KTL or TAWD ablation.

Figure A.2: Conceptual illustration of the KineWorld pipeline. KTL renders robot states and uses RAFT to construct camera-aligned transport, which conditions the generator. TAWD detaches the transport path, aggregates calibrated activity on the latent grid, and converts it into a transport distribution $\pi_{i}$ and mean-one weights $w_{i}=N\pi_{i}$ for future-RGB flow-matching errors. TAWD changes training-time supervision only; it adds no inference steps or parameters.

Figure A.3: Conceptual visualization of transport-support construction. Two manipulation scenes illustrate camera views, rendered robot motion, schematic 3D fields, image-plane transport, and normalized support. KTL estimates transport using RAFT between rendered robot states rather than analytic 3D-field projection. The final column illustrates $\rho^{\mathrm{tr}}$ , not the mean-one loss weights $w$ . These panels are illustrative rather than measured experiment outputs.

Panel of Figure 12 — Figure A.4: Analytical illustration of TAWD token-weight allocation. For the constructed exponential support over $N=1000$ future RGB latent tokens at $\beta=1$ , (a) shows descending mean-one weights and (b) their cumulative share relative to uniform weighting. The top 10% receive 36.63% of the total token weight. The green curve shows the general upper bound $(1+q)/2$ for $q>0$ ; the cumulative share has $C_{0}=0$ . These curves illustrate the weighting rule, not measured training statistics or loss/gradient shares.

$\operatorname{Method}$	$\operatorname{Appearance}$		$\operatorname{Motion}$			$\operatorname{Consistency}$
$\operatorname{Method}$	$\operatorname{AES}\uparrow$	$\operatorname{IMG}\uparrow$	$\operatorname{DYN}\uparrow$	$\operatorname{FL}\uparrow$	$\operatorname{MS}\uparrow$	$\operatorname{BG}\uparrow$	$\operatorname{PS}\uparrow$	$\operatorname{SC}\uparrow$
Linear expansion	41.86	45.43	22.47	0.37	58.75	85.00	1.02	81.30
VFIMamba (no TTA)	40.30	45.50	41.65	8.37	63.43	90.50	54.22	86.97
RIFE HDv3	39.53	45.95	43.23	10.85	63.47	90.42	65.62	87.13

Appendix

Table A.4: Interpolation Strategy. Diagnostic on the same 16 episodes with fixed generated keyframes. Linear expansion uses linear frame interpolation; VFIMamba and RIFE HDv3 use learned interpolation.

Figure A.5: Interpolation diagnostics on 16 local episodes. The methods expand the same generated keyframes; values match Table A.4 . AES, IMG, DYN, FL, MS, BG, and SC follow Table A.2 ; PS denotes Photometric Smoothness. This comparison evaluates temporal reconstruction with the generator fixed, rather than the isolated effect of TAWD.

$\operatorname{Temporal\ Variant}$	Local Motion Metrics (%)
$\operatorname{Temporal\ Variant}$	$\operatorname{DYN}\uparrow$	$\operatorname{FL}\uparrow$	$\operatorname{PS}\uparrow$
Keyframe-6 (6 fps)	45.80	12.20	26.40
Linear expansion	22.47	0.37	1.02
Keyframe hold-4	33.09	9.69	13.78
VFIMamba (no TTA)	41.65	8.37	54.22
RIFE HDv3	43.23	10.85	65.62

Appendix

Table A.5: Temporal Reconstruction. Comparison on the same 16 local episodes with the generator fixed. Keyframe-6 reports the sparse generated keyframes directly at 6 fps, whereas the remaining variants produce 24-fps outputs. Bold marks the best results among the 24-fps reconstruction variants.

\operatorname{DYN}

\operatorname{FL}

, and

\operatorname{PS}

denote Dynamic Degree, Flow Score, and Photometric Smoothness.

$\operatorname{Method}$	$\operatorname{Pixel\ Difference}$			$\operatorname{Repetition}$	$\operatorname{Optical\ Flow}$
$\operatorname{Method}$	$\operatorname{A\mbox{-}MAD}$	$\operatorname{CPF}$	$\operatorname{FL\mbox{-}MAD}$	$\operatorname{NDP}$	$\operatorname{FB\ Mean}$	$\operatorname{FB\ P95}$	$\operatorname{Jerk}$
Linear expansion	0.006884	0.049167	0.096238	0.588374	0.109187	0.418630	0.026805
VFIMamba (no TTA)	0.008584	0.038254	0.096187	0.570205	0.324903	1.399471	0.078122
RIFE HDv3	0.008832	0.039097	0.096191	0.563069	0.337443	1.428315	0.075933

Appendix

Table A.6: Frame Diagnostics for the 16-episode interpolation ablation. Definitions, interpretation, and metric provenance are provided in Appendix A.8.3 .

$\operatorname{Method}$	Paired Local VLM Metrics (%)
$\operatorname{Method}$	$\operatorname{INT}\uparrow$	$\operatorname{PER}\uparrow$	$\operatorname{IF}\uparrow$
Linear expansion	67.40	87.40	73.40
RIFE HDv3	66.60	87.80	72.80
RIFE $-$ Linear (pp)	$-0.80$	$+0.40$	$-0.60$

Appendix

Table A.7: Representative-100 paired comparison on identical episodes.

\operatorname{INT}

\operatorname{PER}

, and

\operatorname{IF}

denote Interaction Quality, Perspectivity, and Instruction Following. Values are local VLM-judge percentages.

$\operatorname{Metric}$	RIFE versus Linear (episodes)
$\operatorname{Metric}$	$\operatorname{Improved}$	$\operatorname{Tied}$	$\operatorname{Regressed}$
Interaction Quality	8	80	12
Perspectivity	8	86	6
Instruction Following	12	73	15

Appendix

Table A.8: Paired Outcome counts for RIFE versus linear expansion on Representative-100. Each row sums to 100; ties denote unchanged VLM-judge scores.

$\operatorname{Method}$	$\operatorname{Measured\ Time\ (s)}$		Relative Efficiency
$\operatorname{Method}$	$\operatorname{Mean}\downarrow$	$\operatorname{Total}\downarrow$	$\operatorname{Speedup}\uparrow$
VFIMamba (no TTA)	1168.24	18691.88	1.00 $\times$
RIFE HDv3	6.02	96.32	194.07 $\times$

Appendix

Table A.9: Interpolation Efficiency measured on 16 episodes.

\operatorname{Mean}

is per-episode time,

\operatorname{Total}

is summed accelerator time, and

\operatorname{Speedup}

is relative to VFIMamba.

$\operatorname{Flow\mbox{-}Condition\ Scale}$	Local Motion Diagnostics
$\operatorname{Flow\mbox{-}Condition\ Scale}$	$\operatorname{DYN}\uparrow$	$\operatorname{FL}\uparrow$
1.00	0.224695	0.003716
1.25	0.227275	0.003652
1.50	0.235240	0.003520

Appendix

Table A.10: Inference-Time Flow-Condition Scale on 16 local episodes.

\operatorname{DYN}

and

\operatorname{FL}

denote Dynamic Degree and Flow Score. This is an auxiliary inference diagnostic rather than a KineWorld component ablation.

A. Tri-view consistency components
$\operatorname{Method}$	$\operatorname{NPSNR}\uparrow$	$\operatorname{SSIM}\uparrow$	$\operatorname{VLM-I}\uparrow$	$\operatorname{VLM-II}\uparrow$	$\operatorname{VLM-III}\uparrow$	$\operatorname{VQA}\uparrow$
BWM	73.43	87.47	85.11	83.94	95.31	65.94
WoVR_Plus	74.62	87.91	83.79	83.94	94.84	69.11
DreamDojo	59.78	71.58	77.14	73.26	88.19	47.82
Motus	60.13	76.31	63.68	67.56	87.22	45.33
Genie Envisioner	58.74	74.44	69.40	57.13	77.93	36.72

Appendix

Table A.11: Complete retained multi-view component profiles. All seven methods and numerical entries are preserved from the supplied comparison; these are external aggregates, not verified RoboTwin 2.0 local re-evaluations. The normalized score scale and preference direction are unchanged; NPSNR is not PSNR in dB. Bold denotes the largest retained values at the reported precision, not a verified local ranking.

Figure A.6: Additional qualitative rollout comparisons (1/3). Wan2.2 and KineWorld outputs for retained episode identifiers 45, 125, 212, 300, and 336. Each row shows the decoded input followed by five uniformly spaced rollout frames.

Figure A.7: Additional qualitative rollout comparisons (2/3). Wan2.2 and KineWorld outputs for retained episode identifiers 391, 488, 586, 704, and 732, using the same deterministic frame-sampling rule as Figure A.6 .

Figure A.8: Additional qualitative rollout comparisons (3/3). Wan2.2 and KineWorld outputs for retained episode identifiers 780, 865, 938, 976, and 982, using the same deterministic frame-sampling rule as Figure A.6 .

Figure A.9: Episode 1, bottle manipulation. Columns are Left wrist, Head, and Right wrist; within each cell, the left image is GT and the right image is KineWorld. Eleven uniformly sampled frame indices are shown.

Figure A.10: Episode 50, bimanual food placement. Columns are Left wrist, Head, and Right wrist; within each cell, the left image is GT and the right image is KineWorld. Eleven uniformly sampled frame indices are shown.

Figure A.11: Episode 100, switch interaction. Columns are Left wrist, Head, and Right wrist; within each cell, the left image is GT and the right image is KineWorld. Eleven uniformly sampled frame indices are shown.

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Jun 15, 2026, cs.CVJ/K move · Enter open · S save

Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation

Jie Zhang, Xiaoyue Chen, Anzhe Chen, Dayiheng Liu+35

Qwen Team

Video World Models Camera-Conditioned Video Generation Embodied Artificial Intelligence World Models Human-To-Robot Transfer Technical Report+1

Open paper AskPDF arXiv

We introduce Qwen-RobotWorld, a language-conditioned video world model for embodied intelligence. With natural language as a unified action interface, it predicts physically grounded future visual trajectories from current observations across robotic manipulation, autonomous driving, indoor navigation, and human-to-robot transfer. This unified formulation provides three promising application directions: synthetic data generation for policy training augmentation, scalable virtual environments for policy evaluation, and language-guided planning signals for downstream robot control. This is achieved through a three-part design: a) Double-Stream MMDiT with MLLM Action Encoding, where a 60-layer double-stream diffusion transformer couples frozen Qwen2.5-VL semantics with video-VAE latents through layer-wise joint attention; b) Embodied World Knowledge (EWK), an 8.6M video-text corpus (200M+ frames) with action-language mapping over 20+ embodiments and 500+ action categories; and c) General+Expert Progressive Curriculum, a two-stage training strategy that first learns general visual priors and then injects embodied specialization under a shared language interface. Extensive results show strong competitiveness: ranks 1st overall on EWMBench and DreamGen Bench, outperforms all open-source models on WorldModelBench and PBench. Additional zero-shot analyses on RoboTwin-IF benchmark further support robust generalization and multi-view consistency.

Abstract

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Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation

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