Omni reference-to-video (R2V) generation is emerging as a general paradigm that enables flexible video customization from diverse and compositional visual references. However, existing benchmarks fall short of these emerging capabilities: their test cases cover only a limited range of reference types and compositions, and their evaluation protocols largely assess holistic reference consistency, overlooking whether reference factors are properly preserved, disentangled, and routed. Meanwhile, the high cost of constructing omni R2V training data makes suitable training resources scarce. To address these gaps, we introduce OmniVBench and the Omni-R2V Dataset, establishing a shared foundation for evaluating and training omni R2V models. OmniVBench expands R2V evaluation across broader reference types, richer reference compositions, and more flexible instruction-driven customization, covering 7 task families and 18 fine-grained tasks. Complementing this task design, we introduce factor-grounded evaluation with 12,172 case-specific checklist items, explicitly assessing whether intended reference factors are faithfully preserved, correctly disentangled and bound to their targets, and realized according to the instruction. We further introduce the Omni-R2V Dataset, bringing industrial-grade training resources for diverse R2V tasks to the broader research community. Drawing primarily on a large-scale corpus of professional video footage, it comprises 340K processed training samples spanning diverse reference types and multi-reference compositions. Specifically, we develop task-specific pipelines for reference-target pair construction, offering a practical and scalable recipe for omni R2V data construction. Extensive evaluation of advanced open- and closed-source R2V models reveals substantial performance variation across diverse tasks and distinct capability patterns across evaluation dimensions.
Figures & tables
Figure 1: Overview of OmniVBench and the Omni-R2V Dataset .
Benchmark
Content Ref.
Motion Ref.
Style Ref.
Structure Ref.
Narrative Ref.
Multiple Refs.
Object
Character
Scene
Action
Camera Motion
Style
Greybox
Line Art
Rough Storyboard
Multi-Panel Storyboard
Story
Preceding- Shot
Multi- Content
Cross- Aspect
OpenS2V-Eval Yuan et al. [2025]
✓
✓
✓
VACE-Bench Jiang et al. [2025]
✓
✓
✓
✓
✓
UniVBench Wei et al. [2026b]
✓
✓
✓
✓
IntelligentVBench Pan et al. [2026]
✓
✓
✓
✓
FashionVideoBench Wu et al. [2026]
✓
✓
✓
✓
✓
Table 1: Comparison of reference task coverage between OmniVBench and existing reference-based video generation benchmarks.
Dataset
# Size
Reference Modality
Reference Task Coverage
Processed Data Provided
Image
Video
Content
Motion
Style
Structure
Narrative
Multi- Content
Cross- Aspect
OpenS2V-5M [ Yuan et al., 2025 ]
5.4M
✓
✓
✓
✓
Phantom-Data Chen et al. [2025c]
1M
✓
✓
✓
MuSS Zhang et al. [2026a]
30K
✓
✓
Omni-R2V (Ours)
340K
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Table 2: Comparison of Omni-R2V Dataset with existing R2V datasets in terms of scale, reference modalities, reference task coverage, and processed data provision (i.e., whether processed samples are directly provided without requiring users to download and process raw source videos).
Figure 2: Representative reference–target pairs from the Omni-R2V Dataset.
Figure 3: Representative examples of OmniVBench. Each category encompasses fine-grained generation tasks defined by distinct reference types and control objectives, with examples illustrating the reference inputs and corresponding generated outputs.
Figure 4: Statistics of OmniVBench. We present the distribution of benchmark samples from multiple perspectives, including (a) the word cloud of textual prompts, (b) prompt length distributions across task categories, (c) reference video length distribution, and (d) the number and modality composition of reference inputs per sample.
Figure 5: Overview of the Omni-R2V Dataset construction pipeline.
Model
Content
Motion
Style
Structure
Narrative
Multi- Content
Cross- Aspect
Overall
Open-Source Models
UniVideo [ Wei et al., 2026a ]
66.01
44.52
47.40
48.84
36.97
58.00
46.09
49.69
OmniWeaving [ Pan et al., 2026 ]
62.23
50.58
41.40
63.19
38.03
47.49
39.25
48.88
LoomVideo [ Wu et al., 2026 ]
61.24
52.09
42.03
62.53
46.98
49.02
44.86
51.25
Bernini [ Liu et al., 2026 ]
69.27
55.92
50.62
69.44
42.02
60.32
51.03
56.95
MiniMax H3 [ MiniMax, 2026 ]
78.86
65.32
66.24
72.94
73.90
77.22
72.36
72.41
Table 3: Fine-grained comparison of different models on OmniVBench. Each task score is averaged over Reference Fidelity (RF), Instruction Realization (IR), and Visual Quality (VQ). The first-place and second-place results are highlighted accordingly.
Figure 6: Model performance across RF, IR, and VQ.
Figure 7: Fine-grained comparison of Reference Fidelity (RF) across R2V task families.
Model
Content
Motion
Style
Structure
Narrative
Multi-Content
Cross-Aspect
IRDR
IRTC
IRDR
IRTC
IRDR
IRTC
IRDR
IRTC
IRDR
IRTC
IRDR
IRTC
IRDR
IRTC
UniVideo Wei et al. [2026a]
62.32
73.47
57.27
70.14
15.39
50.78
71.13
82.80
26.03
33.62
57.48
54.68
53.23
53.92
OmniWeaving Pan et al. [2026]
78.36
85.87
39.95
51.05
59.33
67.13
67.23
92.09
23.78
36.35
48.55
67.66
40.55
54.20
LoomVideo Wu et al. [2026]
87.91
88.65
57.28
68.38
89.64
68.42
76.62
93.95
58.88
37.48
65.00
74.09
55.83
63.27
Bernini Liu et al. [2026]
83.19
91.90
72.65
61.46
68.15
72.58
77.54
97.11
28.57
37.81
65.77
78.01
55.94
64.08
MiniMax H3 MiniMax [2026]
98.21
97.28
94.14
94.99
91.94
85.65
95.42
98.14
95.05
89.47
95.93
97.80
93.61
90.71
Table 4: Fine-grained comparison of Instruction Realization (IR) on OmniVBench. IRDR measures reference-factor disentanglement and routing, while IRTC measures target compliance.
Table 12
Appendix figures & tables14 assets
Supplementary material from the paper’s appendix.
Appendix
Figure A.1: Prompt statistics of Omni-R2V Dataset .
Task Family
Sub-Task
# Cases
Content
Object
33
Character
54
Scene
25
Subtotal
112
Motion
Action
47
Camera Motion
48
Appendix
Table A.1: Detailed composition of OmniVBench. We report the number of evaluation cases for each sub-task.
L2 Sub-dimensions
Evaluation criteria
Content Fidelity
Entity/Scene Identity Fidelity; Surface Fidelity; Part Fidelity
Table A.3: Instruction Realization (IR) L2 sub-dimensions and evaluation criteria.
L2 Sub-dimensions
Evaluation component
Technical Quality
Technical branch of DOVER++ Wu et al. [2023]
Aesthetic Quality
Aesthetic Predictor V2.5 discus0434 [2024]
Physical Plausibility
Coherence/Physics dimension of UnifiedReward 2.0 Wang et al. [2025b]
Appendix
Table A.4: Video Quality (VQ) L2 sub-dimensions and automatic evaluation components.
Score
Instruction Realization
Reference Fidelity
1
The instruction is not executed, or the output is unrelated to the requested task.
The required reference information is absent, or the output has no meaningful correspondence with the reference.
2
The output shows an attempt to follow the instruction but largely fails or introduces incorrect entities.
The required reference information is only weakly preserved, with substantial deviations that make the correspondence difficult to recognize.
3
The instruction is partially fulfilled, but key requirements are missing or incorrectly realized.
The required reference information is recognizable but only partially preserved, with clear inconsistencies or errors.
4
The main requirements are fulfilled, with minor deviations in details.
The required reference information is largely preserved, with minor local or temporal inconsistencies.
5
All applicable instruction requirements are fulfilled, including specified entities, attributes, positions, durations, and quantities.
The required reference information is faithfully preserved throughout the relevant portions of the video, with no substantive inconsistencies.
Appendix
Table A.5: Five-point rating criteria for human evaluation of Instruction Realization (IR) and Reference Fidelity (RF). Only requirements applicable to each sample are assessed.
Criterion
Content
Motion
Style
Structure
Narrative
Multi-Content
Cross-Aspect
Avg.
Reference Usability
97.5
90.0
100.0
97.0
92.5
96.0
94.5
95.4
Reference–Target Consistency
100.0
92.5
98.0
98.5
98.5
96.0
95.5
97.0
Instruction Correctness
94.5
86.5
96.5
94.5
99.0
94.0
94.5
94.2
Overall
97.3
89.7
98.2
96.7
96.7
95.3
94.8
95.5
Appendix
Table A.6: Human evaluation of Omni-R2V Dataset quality across the seven task families. Each entry reports the average Pass rate (%) of two annotators.
Figure A.2: Example of factor-grounded evaluation on a multi-reference case, showing the case-specific RF/IR checklists and corresponding model scores.
Figure A.3: Qualitative comparison on the content reference task.
Figure A.4: Qualitative comparison on the structure reference task.
Figure A.5: Qualitative comparison on the narrative reference task.
Figure A.6: Qualitative comparison on the narrative reference task.
Figure A.7: Qualitative comparison on the style reference task.
Figure A.8: Qualitative comparison on the cross-aspect reference task.