AI Photo Singing Video Generator

Core Problem: From Static Image to Animated Singing

Given a static portrait photo and a singing audio clip, how can one generate a video where the subject appears to sing naturally? This is common for creating greeting videos, humorous content, or virtual avatar performances. The key challenge is audio-driven animation, which requires synchronizing lip movements, facial muscle actions, and emotional expression with the song's rhythm and lyrics, while preserving the person's features, background, and overall composition. Simple auto-dubbing tools often fail to produce realistic singing motions, while full video editing workflows are too complex for quick generation.

Workflow and Technical Implementation

This skill offers a streamlined pipeline to address this problem:

1. Input Validation and Preparation: The process begins with strict checks on two mandatory inputs:

   • Portrait Photo: Must be an accessible, clear image. The skill records technical attributes like MIME type, dimensions, aspect ratio, and the presence of an Alpha channel (transparency).
   • Singing Audio: Must be a short song snippet (not spoken dialogue). Its format, duration, and size are validated.
   • Contextual details (like intended use or background preferences) are reused when known, and only missing items are requested. Local files are transferred securely via a bundled upload assistant.

2. Model Gating and Configuration: Before calling the generation service, it queries the model list (beatra.models.list) to filter models that support the image_to_video capability and are compatible with the specific image and audio formats provided. This is a crucial technical gatekeeping step to ensure feasibility.

3. Task Submission and Execution: Using the bundled mcp_client.py tool, it constructs a frozen request (client_request_id) containing the image, audio, a prompt (to guide singing style), and duration, then calls the beatra.videos.animate endpoint. The audio duration dictates the video duration, and the system selects the smallest supported integer duration that won't truncate the audio.

4. Polling and Result Delivery: After submission, it continuously polls the task status (beatra.tasks.get) until completion. The final video asset or link is delivered, with a report on the actual status, model used, dimensions, duration, and billing details. A final review checks for consistency in facial features, singing synchronization, and background stability.

Applicability Boundaries and Key Constraints

• Focused on Singing: This skill is optimized specifically for "singing" animation. Other needs should route to appropriate workflows: e.g., speech narration (digital human), pet talking, general image-to-video, or music-led video production.
• Audio is the Sole Driver: Lyrics text has no effect; a real audio file is required to drive the lip and expression animation.
• Billing and State Management: Changing input assets (photo, audio) or key parameters (prompt, model) creates a new, independent paid task. Task replay, querying, and cancellation are supported, but strict rules against duplicate billing are enforced.
• Quality Expectations: The goal is to produce expressive singing motions and stable video, but perfect lip-sync or precise retention of every detail is not guaranteed. For requests needing major portrait modifications, the request should be routed to an appropriate image editing workflow first.
• Security and Updates: The runtime tool (mcp_client.py) silently checks for updates (without interfering with the current task) and downloads exclusively from official, fixed URLs to ensure integrity.