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  "path": "/t/more-sophisticated-image-usage-hinoko-photo-method/1381743#post_2",
  "publishedAt": "2026-05-25T17:46:12.000Z",
  "site": "https://community.openai.com",
  "textContent": "# Hinoko-Space-Method V0.0.1\n\nA low-cost AI narrative space workflow for independent creators.\n\n  1. Core Philosophy\n\n\n\nHinoko-Space-Method is a workflow focused on building reusable narrative spaces for AI-assisted filmmaking and storytelling.\n\nInstead of relying on expensive 3D pipelines or fully generated environments, the method uses:\n\nreal-world spatial references\nmodular scene structures\nmulti-angle scene consistency\nAI-assisted atmospheric completion\n\nto create low-cost virtual narrative spaces for independent creators.\n\n  2. Core Concept\n\n\n\nThe method treats spaces not as backgrounds, but as narrative infrastructure.\n\nA scene should remain emotionally and spatially recognizable even when characters are removed.\n\n  3. Spatial Memory Structure\n\n\n\nEach narrative space is constructed using reusable scene references:\n\nFront\nLeft\nRight\nEmpty\nWith Objects\n\nThis creates spatial memory for AI generation and improves continuity consistency.\n\n  4. Real-World Spatial Skeleton\n\n\n\nReal cities, public spaces, and architectural references can be used as spatial skeletons.\n\nThe workflow encourages:\n\nfictionalization\nde-branding\natmospheric reinterpretation\n\ninstead of direct replication.\n\n  5. AI Completion Principle\n\n\n\nThe creator defines:\n\nstructure\nspace\nemotional logic\n\nAI assists with:\n\natmosphere\nlighting\ntexture\ncontinuity completion\n\n  6. Creator-Oriented Ethics\n\n\n\nHinoko-Space-Method is designed to:\n\nreduce creative production barriers\nsupport independent creators\nencourage original narrative works\ndiscourage malicious impersonation and exploitative generation\n\nLicense\nCC BY-NC-ND 4.0\n© 2026 Kobayashi Hinoko\n\nYou may share this method for non-commercial purposes with full attribution. Derivative works and commercial reuse are not permitted.\n\nMethods\n\n## 1. Scene Construction\n\nScene construction begins by establishing a reusable three-view spatial structure derived from either real-world references or AI-assisted spatial layouts.\n\nThe three-view structure serves as a spatial anchor, supporting environmental continuity and preserving scene consistency across generated outputs.\n\n### 1-1 Why are three-view references necessary?\n\nAI-generated scenes may exhibit the following issues:\n\n  * Spatial drift\n  * Environmental detail inconsistency\n  * Perspective instability\n\n\n\nBy providing multiple spatial viewpoints, creators constrain spatial interpretation while allowing AI to infer and complete missing visual information.\n\n### 1-2 Core Principles\n\nThe objective is not to delegate world creation to AI, but to extend human creative capability through structured spatial guidance.\n\n* * *\n\n## 2. Character Construction\n\nCharacter construction utilizes one or more reference images to preserve character identity and reduce visual distortion across generated scenes.\n\nThe objective is to maintain consistency in appearance while allowing flexible scene adaptation.\n\n### 2-1 Usage Principles\n\nWhen using real-life references, appropriate authorization and consent should be obtained before generation.\n\n* * *\n\n## 3. Combined Use\n\nBy combining three-view spatial references, character references, and designated audio direction, creators can guide AI-assisted video generation while maintaining narrative and visual consistency.\n\n**END**\n\nGo：codeberg\nSearch：Hinoko-Space-Method\n\nYou can see detailed case studies.",
  "title": "More sophisticated image usage：Hinoko-Photo-Method"
}