Wan 3.5 AI Video Generator | Create Clips from Text/Image

What

Wan 3.5 is a hosted generative video workspace designed for digital creators, visual planners, and marketing teams producing short, iterative video clips. It addresses the challenges of visual drift and erratic camera motion by combining text-to-video prompting with frame-guided inputs such as first frames, last frames, and reference stills. The platform positions itself as a shot-planning and clip-review desk powered by Wan video model variants, including Wan 2.7 and Wan 2.6.

Features

• Dual Text and Frame-Led Generation: Users can create net-new video clips from text prompts or attach reference stills, first frames, and last frames to enforce identity and scene continuity across camera moves.
• Selectable Model Architecture: The generator includes a live model selector featuring options such as Wan 2.7 and Wan 2.6, allowing operators to choose engines based on specific motion behaviors or input constraints.
• Integrated Camera and Motion Directives: The input workspace is engineered to interpret structured camera paths, subject actions, and quoted on-screen copy directly within the prompt field.
• Real-Time Per-Shot Credit Preview: The interface dynamically calculates and displays the exact credit cost on the action button according to the selected model, duration, and resolution before the user initiates a run.

Helpful Tips

• Use Anchor Frames for Brand Assets: When working with recognizable packaging, talent, or typography, upload an opening or closing reference frame rather than relying on pure text-to-video generation to preserve visual identity.
• Isolate Variables During Re-Runs: If a generated clip fails to match the brief, modify only one parameter at a time—such as camera travel language, model selection, or resolution—to pinpoint what caused the issue without burning excess balance.
• Verify Button Pricing Before Generation: Always confirm the live credit count printed on the generator button prior to running a shot, as adjustments to duration or resolution immediately alter consumption rates.
• Prepare Still Assets in Dedicated Environments: Generate source stills or complex lettering in dedicated image-generation tools first, then import those assets into the video workspace as reference frames.

OpenClaw Skills

The source material does not document any native integrations or API connections with OpenClaw. In a hypothetical workflow, an OpenClaw agent could ingest creative briefs, automatically format structured shot prompts with camera directives, and queue reference frames to programmatically generate storyboards. If implemented, such an automation could accelerate video ad production and rapid pre-visualization by reducing the need for manual, shot-by-shot form configuration.