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ARXIV:2603.17508 · IMAGE-TO-CODE GENERATION · SUBMITTED 19 MAR · 21:58 UTC · FRESHNESS STALE
ARXIV:2603.17508IMAGE-TO-CODE GENERATIONSUBMITTED 19 MAR · 21:58 UTCFRESHNESS STALEarXiv
Omni-I2C is a benchmark for evaluating Large Multimodal Models in generating executable code from complex digital graphics.
Opportunity summary
Pain Omni-I2C is a benchmark for evaluating Large Multimodal Models in generating executable code from complex digital graphics.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence partial
Omni-I2C is a benchmark for evaluating Large Multimodal Models in generating executable code from complex digital graphics. We argue that this task represents a non-trivial challenge for the current generation of LMMs: it demands…
We present Omni-I2C, a comprehensive benchmark designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executable code. We argue that this task represents a non-trivial challenge…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Data and code are available at https://github.com/MiliLab/Omni-I2C. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper…
Image-to-Code Generation moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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Omni-I2C is a benchmark for evaluating Large Multimodal Models in generating executable code from complex digital graphics.
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10.48550/arXiv.2603.17508Omni-I2C is a benchmark for evaluating Large Multimodal Models in generating executable code from complex digital graphics.
Abstract
We present Omni-I2C, a comprehensive benchmark designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executable code. We argue that this task represents a non-trivial challenge for the current generation of LMMs: it demands an unprecedented synergy between high-fidelity visual perception -- to parse intricate spatial hierarchies and symbolic details -- and precise generative expression -- to synthesize syntactically sound and logically consistent code. Unlike traditional descriptive tasks, Omni-I2C requires a holistic understanding where any minor perceptual hallucination or coding error leads to a complete failure in visual reconstruction. Omni-I2C features 1080 meticulously curated samples, defined by its breadth across subjects, image modalities, and programming languages. By incorporating authentic user-sourced cases, the benchmark spans a vast spectrum of digital content -- from scientific visualizations to complex symbolic notations -- each paired with executable reference code. To complement this diversity, our evaluation framework provides necessary depth; by decoupling performance into perceptual fidelity and symbolic precision, it transcends surface-level accuracy to expose the granular structural failures and reasoning bottlenecks of current LMMs. Our evaluation reveals a substantial performance gap among leading LMMs; even state-of-the-art models struggle to preserve structural integrity in complex scenarios, underscoring that multimodal code generation remains a formidable challenge. Data and code are available at https://github.com/MiliLab/Omni-I2C.
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Proof status
partial0 refs; 0 sources; 50% coverage.
What was readable
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Viability
Time to MVP
Commercial
Export
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Dimensions overall score 8.0
PROBLEM
Omni-I2C is a benchmark for evaluating Large Multimodal Models in generating executable code from complex digital graphics. We argue that this task represents a non-trivial challenge for the current generation of LMMs: it demands an unprecedented synergy between high-fidelity vi...
METHOD
We present Omni-I2C, a comprehensive benchmark designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executable code. We argue that this task represents a non-trivial challenge for the current generation of...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Data and code are available at https://github.com/MiliLab/Omni-I2C. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
WHY NOW
Image-to-Code Generation moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Omni-I2C features 1080 meticulously curated samples
Explicitly stated in the abstract with specific sample count
partial
it demands an unprecedented synergy between high-fidelity visual perception -- to parse intricate spatial hierarchies and symbolic details -- and precise generative expression
Directly stated in the abstract as a core argument of the paper
partial
even state-of-the-art models struggle to preserve structural integrity in complex scenarios
Directly stated in abstract as evaluation finding
partial
the benchmark spans a vast spectrum of digital content -- from scientific visualizations to complex symbolic notations
Explicitly described in the abstract with specific examples
partial
by decoupling performance into perceptual fidelity and symbolic precision
Directly stated in abstract as a methodological feature
partial
multimodal code generation remains a formidable challenge
Direct conclusion stated in abstract based on evaluation results
partial
By incorporating authentic user-sourced cases
Explicitly mentioned in abstract as a design feature
partial
any minor perceptual hallucination or coding error leads to a complete failure in visual reconstruction
Directly stated in abstract as a characteristic of the task
partial
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Concepts
Methods
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Omni-I2C is a benchmark for evaluating Large Multimodal Models in generating executable code from complex digital graphics.
Segment
Image-to-Code Generation
Adoption evidence
Public code linked for build inspection
Commercial read
8.0/10 public viability
Direct
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reason
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proof status
unverified
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Build readiness
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passport absent
stale
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Artifact maturity
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stale
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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