Synthèse
Each film combined multiple Google AI models. The agents called them through a shared CLI toolkit called genmedia: Gemini image generation (Nano Banana) produced character reference sheets, storyboard frames, and scene compositions. The agents kept characters visually consistent across a film through reference chaining: they generated headshots first, then used those as input for body sheets, then used body sheets as input for scene tests. Each generation call included these accumulated referenc
Each film combined multiple Google AI models. The agents called them through a shared CLI toolkit called genmedia: Gemini image generation (Nano Banana) produced character reference sheets, storyboard frames, and scene compositions. The agents kept characters visually consistent across a film through reference chaining: they generated headshots first, then used those as input for body sheets, then used body sheets as input for scene tests. Each generation call included these accumulated references as anchors. Veo 3.
1 generated the video. Clips run four to eight seconds at 720p. The agents chose different generation modes depending on the shot: text-to-video for simple compositions, image-to-video for shots anchored to storyboard frames, frame interpolation when they needed a precise start and end frame. For shots longer than eight seconds, they fed the last frame of one clip as the first frame of the next. Veo 3.
1 also generates audio inside each clip: ambient sound, room tone, and lip-synced character dialogue. One team (Lambda) built their film around this capability. They structured the script like a musical score with movement markings (Allegretto, Accelerando, Adagio) because the sync between generated speech and lip movement gave pauses real weight. Lyria 3 generated original music. One editor composed a three-movement jazz score before any video was shot and used it as the master clock for the production.
Teams also coerced Lyria into producing sound effects by framing prompts as "soundscapes. " Gemini Flash TTS generated character voices and narration from named voice personas with style direction ("world-weary narrator, slow measured pace"). TTS pacing was hard to predict. One team's narrator delivered at 108 words per minute instead of the planned 130, blowing out the runtime by a full minute. A different team had a similar problem but decided the slow pace fit their character, a 68-year-old projectionist.