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Generating video from image references in Higgsfield AI

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Demo summary

The video demonstrates the process of using character sheets and location references to generate a video clip of a character entering a room using Higgsfield AI.

Step-by-step

  1. Switch the mode to Video in the interface
  2. Upload the character sheet and location reference images
  3. Paste the prompt into the generation field
  4. Select the 21x9 aspect ratio for cinematic output
  5. Run a batch of eight generations to test the prompt
  6. Skim through the generated videos to check composition before watching in full

Options

  • Use 16x9 aspect ratio instead of 21x9
  • Translate prompts into Chinese to bypass character limits and improve model performance

Watch out for

  • The order of reference images must match the order they are described in the prompt (e.g., character first, then environment)
  • There is a hard cap of 3,000 characters per prompt

Tips

  • Use batches of eight to quickly identify if the model is making consistent mistakes
  • If generations are failing, use Claude to refine the prompts or fix the shot list structure
  • Include 'failure modes' or negative handles directly inside the prompt to prevent issues like object drifting
  • Stop the generation and fix the prompt immediately if you notice recurring errors in a batch
  • Only watch the full video if the initial composition looks correct to save time

All demos from “How to Make AI Films That Don't Look AI (Full Tutorial)”

  1. 2:561:52Building a cinematic shot list with ClaudeThe creator demonstrates uploading a custom 'skill' zip file to Claude to generate a detailed shot list and style prefix for an AI film project.ClaudeAI Storyboard Generator
  2. 9:422:36Iterative environment generation with Higgsfield AIThe user demonstrates generating and refining a 3D-consistent apartment location using text prompts and the Higgsfield AI image generator.HiggsfieldAI Image Generator
  3. 19:083:46Image-to-image editing for scene consistencyThe creator shows how to use an existing generation as a reference image to perform specific edits like adding pipes and changing lighting in Higgsfield AI.HiggsfieldAI Inpainting
  4. 48:143:52Generating video from image references in Higgsfield AICurrentThe video demonstrates the process of using character sheets and location references to generate a video clip of a character entering a room using Higgsfield AI.HiggsfieldImage to Video
  5. 2:02:332:35Troubleshooting AI video motion and FPSThe creator demonstrates how to identify and fix frame rate issues and 'morphing' artifacts in Higgsfield AI video generations by adjusting prompt parameters.HiggsfieldAI Video Generator
  6. 2:50:294:47Generating emotional character performancesThe user demonstrates prompting Higgsfield AI for specific emotional physical cues, such as a trembling lip and sobbing, to create a dramatic character breakdown.HiggsfieldText to Video
  7. Watch “How to Make AI Films That Don't Look AI (Full Tutorial)” →

Image to Video

  1. 3:260:20Create cinematic transitions between imagesThe user uploads two different images and applies the 'Raven' transition effect to create a seamless camera movement traveling through space between the scenes.Roboverse
  2. 5:421:16Multi-shot image-to-video generationThe user demonstrates Higgsfield's video interface by uploading an image and using the Kling 3.0 model with a multi-shot prompt to create a 10-second cinematic sequence with specific camera movements.Roboverse
  3. 4:031:42Animate AI scenes with Kling 3.0The narrator demonstrates converting a static scene into a video by selecting the Kling 3.0 model, adding motion prompts, and setting the duration and quality to 1080p.Money4Life
  4. 7:541:25Animate backgrounds with Higgsfield Kling 3.0The user demonstrates generating a clean background in Higgsfield and then animating it using the Kling 3.0 video model with a start-frame reference.Creating with Conor
  5. 4:150:38Automated video creation with Higgsfield SupercomputerThe video shows the Higgsfield Supercomputer agent performing research, generating images, and then automatically converting those images into a video with audio using the C-Dance 2.0 model.Malva AI
  6. 4:500:57Cinematic Video Generation with HiggsfieldThe video demonstrates using Higgsfield to generate high-quality images with GPT Image 2 and then animating them using the SeaDance 2.0 model.Malva AI
  7. 4:501:02Animate images using C Dance 2.0The demonstration shows connecting an image node to a video generation node using the C Dance 2.0 model, including adjusting aspect ratio and motion settings to produce a cinematic video.Mr.Endatah
  8. 1:39:231:28Motion control with Kling 3.0The user demonstrates copying movements from a motion library onto a static character image using the Kling 3.0 motion control model.Artturi Jalli
  9. 5:040:26Cinematic video generation with HiggsfieldThe demo shows generating an image using GPT Image 2 and then using the 'animate' feature to create a cinematic clip with SeaArt 2.0.Malva AI
  10. 2:401:13Create AI video from image and prompt nodesThe demonstrator connects a previously generated image to a video generator node and adds a cinematic prompt to create a 12-second video with complex camera movements.Mhd Labs
  11. 4:071:49Convert an AI image to video with motion promptsThe demo shows connecting an image node to a video generation node (using the Cedance 2.0 model) and adding a motion prompt to make the generated cat stand up and jump.Artturi Jalli
  12. 48:143:52Generating video from image references in Higgsfield AICurrentThe video demonstrates the process of using character sheets and location references to generate a video clip of a character entering a room using Higgsfield AI.Higgsfield AI
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