H3 Video Generation Test: Why Does Character Control in Complex Scenes Rely on Luck?
Hdfjds · reddit · 2026-08-23
The author shares their experience using the H3 model for complex video generation, highlighting the difficulty of maintaining consistency and control.
Test Scenario:
Attempting to generate a scene with a 3D kitchen background and three specific characters, with precise control over each character's actions.
Issues Encountered:
- High Randomness: Six runs produced completely different results.
- Uncontrollable Actions: Characters might enter from the wrong location or perform unrequested extra actions.
- Seed Dependency: Changing only the seed (with the same prompt) leads to vastly different outputs.
Reflection:
The author feels that generation currently relies mostly on "luck" rather than precise control. They ask the community how they cope: is it by iteratively tweaking prompts, generating large volumes to select the best, or accepting imperfect results? This reflects the current technical bottleneck of video models in complex narrative and precise control.
More from Multimodal
- Locked-in consistency: 10-min AI video holds 7 speaking characters across 4 locations — mygreenmyblue · 2026-08-23
- Midjourney + H3 generates high-quality title sequence in one shot — techhalla · 2026-08-23
- Running MiniMax H3 video generation locally on a 12GB laptop: face detail is the bottleneck — sarasa_0505 · 2026-08-23
- User deploys Sora-equivalent video generation model locally — nptacek · 2026-08-23
- Flashback: exploring immersive virtual worlds in latent space via prompts — nptacek · 2026-08-23
- Minimax Leads in Prompt Adherence, Flux 3 Wins on Atmosphere and Texture — Grinderius · 2026-08-23