Maximising Impact with AI: Ethical Strategies for Developing a Top-Rated Game on a Modest Budget
summary
vTime Games builds Borderbots VR to a 4.9/5 rating using an ethical, low‑cost AI stack that actually ships: an in‑house TTS (Awson) for placeholder audio (three hours generated, ~100x cheaper than commercial services), a GPT prompt service that auto‑assigns animations for 1,350 script lines, an iPhone facial capture pipeline (Helen) with a Shazam‑style audio aligner to sync studio and phone takes, and a custom speech‑to‑face model; the result speeds iteration, catches timing issues, and reliably falls back to re‑record or ML generation.
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chapters (26)
- 0:00AI in game dev
- 1:13From BBC Model B to Borderbots VR
- 3:03VTime Games and machine learning
- 4:06Origin of Borderbots VR
- 6:17Reviving Border Patrol with Team 17
- 7:40Narrative tools for the game
- 10:13Node-based scripting challenges
- 11:17Voice cloning and facial capture
- 12:59Script prototyping and actor recording
- 14:02Budget-friendly VR strategies
- 16:56Facial capture and audio sync
- 19:11iPhone power in production
- 20:35Machine learning in Borderbots VR
- 21:17Ethical AI in game dev
- 21:39Animating robot characters
- 23:25GPT for animation assignment
- 26:38Awson text-to-speech engine
- 27:43Placeholder audio and script iteration
- 30:35Feedback-driven character changes
- 31:38Cost savings with in-house TTS
- 33:06Speech-to-face animation model
- 34:53Training data and model flexibility
- 36:38ML-driven animations
- 38:05ML lessons in game dev
- 38:48In-house ML expertise
- 40:33Q&A on robot animations
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