MaLiang-Harness: A Programmable Path to Image and Video Generation
Executable programs offer explicit control over how images and videos are constructed, but generating runnable code is only the beginning of visual creation. A program can execute correctly while violating the requested composition, appearance, or motion. We define this discrepancy as the Program-to-Visual (P2V) gap and introduce MaLiang-Harness, a unified framework for organiz
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Executable programs offer explicit control over how images and videos are constructed, but generating runnable code is only the beginning of visual creation. A program can execute correctly while violating the requested composition, appearance, or motion. We define this discrepancy as the Program-to-Visual (P2V) gap and introduce MaLiang-Harness, a unified framework for organiz
This work addresses the critical 'Program-to-Visual (P2V) gap' in generative AI, offering a structured approach to ensure generated visual content aligns with programmatic intent. For ML engineers, understanding this framework is key to building more reliable and controllable visual generation systems, moving beyond mere code generation to verifiable visual outcomes.