标签：#videogeneration

The Problem Nobody Talks About Everyone's posting AI-generated videos — characters speaking with lip-sync, manga panels coming alive, virtual idols dancing. The pitch: "just describe what you want." I tried. For months. Here's what I got: Character's face morphed by frame 2 "Slowly looks up" became "violent head shake" Voice-over sounded like Google Translate Same prompt, 3 runs, 3 completely different results No idea what to include or how long the prompt should be Tutorials were either too vague ("be detailed") or too technical (parameter tuning from line 1). The real issue: video prompts are structurally different from text/image prompts. You need to simultaneously control visuals, motion, audio, camera, and consistency constraints — in the right order, at the right length. What I Found A Skill in the Model Studio official repo called happyhorse-prompt-studio . It doesn't teach you theory — it asks you questions and assembles the prompt for you . 4-phase flow: 1. Inspiration Menu Shows you 4 "flavors" of what HappyHorse can do: Flavor What it does A · Voiced Manga Drama Characters talk to each other, with voice + lip-sync B · Character Voice PV Single character self-introduction, 8-10 sec C · Manga Panel Motion Static manga panel starts breathing D · Virtual Idol MV Idol performance with choreography 2. Discovery Asks you conversationally: character appearance, scene, emotion, dialogue, voice type, art style, camera. 3. Prompt Assembly Assembles using the HappyHorse Formula : Scene + Subject + Motion + Audio + Quality Key techniques: @「Image n」 syntax locks character identity across shots Dialogue ≤15 characters (split shots if longer) Japanese prompts work best (HappyHorse is JP-optimized) Always end with キャラの顔・髪・衣装が変わらない (face/hair/outfit stays unchanged) 4. Quality Check Auto-reviews: completeness, compliance, cost estimate, optimization tips. Before vs. After Dimension Writing myself With Prompt Studio Attempts needed 10-20 before one usable 2-3 to satisfacti

Video AI systems consistently fail to track what happens when the camera looks away: when a scene pans away from an object in motion and returns, current models re-render the object in its original position rather than showing the logical result of off-screen change. Scaling to more parameters makes this failure worse, not better, according to WRBench , a new benchmark that tests what researchers call "world model reliability." The benchmark presents AI video systems with scenes where something happens off-screen — the camera pans away while an object is in motion, or while a light changes, or while an open door should stay open — then pans back to see what the system believes should have happened. A system that genuinely models the world would track what occurred during the off-screen interval. Current systems mostly don't. Key facts What: A new benchmark tests whether video AI systems can track what happens to parts of a scene the camera isn't currently showing. Across 23 models, the answer is mostly no — and making the models larger made the problem worse, not better. When: 2026-06-19 Primary source: read the source (arXiv 2606.20545) The benchmark covers twenty-three different video generation models and nearly ten thousand video clips across six categories of off-screen change, each designed to test a different aspect of world continuity: objects in motion, light sources changing, object states such as open or closed doors, and several others. This gives a comprehensive picture rather than a single narrow test. The most striking finding is the scaling result. The researchers tested one of the more capable video generation systems at two different sizes: a smaller version and one with more than ten times as many parameters. More parameters didn't help. Scaling made the off-screen tracking problem measurably worse. The larger model produced more realistic-looking frames, but it was less accurate about what should have happened to the parts of the scene it wasn't