Hi everyone — I’m sharing Kling AI Video on behalf of its product team and would welcome feedback on its video-creation workflow.
The project is a browser-based workspace for turning text prompts into video clips, animating still images, and transferring movement from a reference video to a character image. It brings Kling alongside Veo, Wan and Seedance, with image creation/editing, talking avatars and text-to-speech supporting the wider creative process. The aim is to help creators, marketers and designers move from an idea or visual reference to a social video, product demonstration or storytelling asset without local model installation.
Try the workflow: open Kling AI Video, choose text-to-video or image-to-video, add a prompt or reference image, and select the generation settings. A free trial is available. This is a hosted application that uses existing models; we are not claiming to have trained those models or to be their official developer.
For evaluating this kind of interface, I’d particularly appreciate suggestions on:
- How to make the difference between prompt-driven generation, image animation and motion-reference control clear to new users.
- What examples would best help users understand when to use each workflow.
- How you would compare prompt adherence, subject consistency and motion quality without relying on a single impressive sample.
This is a project introduction and request for practical feedback, not a course solution or a model-performance benchmark.