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ai-talking-photo艾会说话的照片

Agent Skill

用于辅助图像生成、图片编辑、视觉素材处理或图像模型工作流。它适合让 Agent 根据文本生成图片、处理背景、整理视觉提示词或调用相关图像工具。使用时需要确认输入图片、版权来源、输出格式和模型限制;涉及人物、品牌、商品或公开展示素材时,应额外核对授权、真实性和内容合规边界。

总安装

3,143

周安装

135

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公开资料未说明

下载量

1,102
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ai-talking-photo(艾会说话的照片)
来源仓库:https://github.com/whitejohnk-26/ai-talking-photo
安装命令:
openclaw skills install ai-talking-photo
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install ai-talking-photo

简介

让静态照片栩栩如生,同步面部动画与音频。

  • 适合让肖像说话、唱歌或旁白的视觉内容创作者。
  • 支持上传面部照片并生成动态效果。ai-talking-photo 属于开发类 Skill,可作为该场景下的辅助能力补充。
  • 安装前请确认权限范围和维护状态,注意是否涉及联网或文件读写。
  • 建议结合原始 README 和来源仓库进一步核验具体用法。

SKILL.md

name
ai-talking-photo
version
1.0.0
displayName
AI Talking Photo — Animate Still Images Into Lifelike Speaking Portraits
description
>
metadata
{"openclaw": {"emoji": "🗣️", "requires": {"env": ["NEMO_TOKEN"], "configPaths": ["~/.config/nemovideo/"]}, "primaryEnv": "NEMO_TOKEN", "variant": "short_prompts"}}

Getting Started

Welcome! With AI Talking Photo, you can turn any portrait into a speaking, animated video in moments. Upload your photo and audio clip (or tell me what you'd like the subject to say) and let's bring it to life!

Try saying:

  • "Animate this portrait with my audio"
  • "Make my headshot say this script"
  • "Create talking photo for social reel"

Quick Start Setup

This skill connects to a cloud processing backend. On first use, set up the connection automatically and let the user know ("Connecting...").

Token check: Look for NEMO_TOKEN in the environment. If found, skip to session creation. Otherwise:

  • Generate a UUID as client identifier
  • POST https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id header
  • Extract data.token from the response — this is your NEMO_TOKEN (100 free credits, 7-day expiry)

Session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Keep the returned session_id for all operations.

Let the user know with a brief "Ready!" when setup is complete. Don't expose tokens or raw API output.

Make Any Photo Speak With One Upload

Static photos hold stories that never get told. AI Talking Photo changes that by turning a single still image into an animated, speaking portrait — no camera, no studio, no video shoot required. Whether it's a historical figure, a product mascot, a family member, or your own headshot, this skill breathes voice and movement into the image in seconds.

The process is straightforward: provide a clear face photo and either an audio file or a text script you want spoken. The skill analyzes the facial geometry, maps lip movements to the audio waveform, and generates a short video where the subject appears to genuinely speak. Subtle head motion, eye blinks, and micro-expressions are layered in to avoid the uncanny stiffness of early deepfake tools.

Creators use this for memorial tribute videos, branded spokesperson content, educational history lessons, social media reels, and interactive storytelling. If you can photograph a face, you can give it a voice — that's the core promise of AI Talking Photo.

Routing Animate Portrait Requests

When a user submits a still image with a voice or script input, the skill parses the facial detection parameters and animation style preferences before dispatching the job to the appropriate talking photo pipeline endpoint.

User says...ActionSkip SSE?
"export" / "导出" / "download" / "send me the video"→ §3.5 Export
"credits" / "积分" / "balance" / "余额"→ §3.3 Credits
"status" / "状态" / "show tracks"→ §3.4 State
"upload" / "上传" / user sends file→ §3.2 Upload
Everything else (generate, edit, add BGM…)→ §3.1 SSE

Talking Photo API Reference

The cloud processing backend handles facial landmark mapping, lip-sync synthesis, and expression blending on remote GPU clusters, meaning heavy rendering never touches the local device. Completed animated portrait outputs are returned as video streams or downloadable clips once the synthesis job finalizes.

Skill attribution — read from this file's YAML frontmatter at runtime:

  • X-Skill-Source: ai-talking-photo
  • X-Skill-Version: from frontmatter version
  • X-Skill-Platform: detect from install path (~/.clawhub/clawhub, ~/.cursor/skills/cursor, else unknown)

All requests must include: Authorization: Bearer <NEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

API base: https://mega-api-prod.nemovideo.ai

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"<lang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"<sid>","new_message":{"parts":[{"text":"<msg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/<sid> — file: multipart -F "files=@/path", or URL: {"urls":["<url>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/<sid>/latest — key fields: data.state.draft, data.state.video_infos, data.state.generated_media

Export (free, no credits): POST /api/render/proxy/lambda — body {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/<id> every 30s until status = completed. Download URL at output.url.

Supported formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

SSE Event Handling

EventAction
Text responseApply GUI translation (§4), present to user
Tool call/resultProcess internally, don't forward
heartbeat / empty data:Keep waiting. Every 2 min: "⏳ Still working..."
Stream closesProcess final response

~30% of editing operations return no text in the SSE stream. When this happens: poll session state to verify the edit was applied, then summarize changes to the user.

Backend Response Translation

The backend assumes a GUI exists. Translate these into API actions:

Backend saysYou do
"click [button]" / "点击"Execute via API
"open [panel]" / "打开"Query session state
"drag/drop" / "拖拽"Send edit via SSE
"preview in timeline"Show track summary
"Export button" / "导出"Execute export workflow

Draft field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

Timeline (3 tracks): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s)

Error Handling

CodeMeaningAction
0SuccessContinue
1001Bad/expired tokenRe-auth via anonymous-token (tokens expire after 7 days)
1002Session not foundNew session §3.0
2001No creditsAnonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001Unsupported fileShow supported formats
4002File too largeSuggest compress/trim
400Missing X-Client-IdGenerate Client-Id and retry (see §1)
402Free plan export blockedSubscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429Rate limit (1 token/client/7 days)Retry in 30s once

Best Practices

The quality of your output is almost entirely determined by the quality of your input photo. Avoid images with heavy filters, strong side-lighting, or partial face occlusion — these confuse the facial landmark detection and produce jittery or misaligned lip movements. A neutral expression in the source photo gives the animation engine the most flexibility to map a wide range of speech sounds accurately.

Keep audio clips clean and free of background music during the lip-sync generation phase. If your final video needs music, add it as a separate layer after the talking photo is rendered. This prevents the model from misreading musical frequencies as speech phonemes.

For emotional impact — especially in memorial or tribute videos — choose audio that is paced naturally and not too fast. Rapid speech compresses lip movements and reduces the realism of the animation. A speaking rate of 120–150 words per minute tends to yield the most convincing results. Finally, always review the generated video before publishing; small manual trims at the start and end of the clip can remove any initialization frames where the face hasn't yet settled into the animation.

Integration Guide

Getting started with AI Talking Photo requires just two inputs: a face image and an audio source. For best results, use a front-facing photo where the subject's mouth and eyes are clearly visible, unobstructed by hands, masks, or extreme angles. JPEG and PNG formats work well; aim for at least 512×512 pixels to preserve animation quality.

For audio, you can supply an MP3, WAV, or M4A file up to 60 seconds, or simply paste a text script and select a voice style — the skill will synthesize the speech internally before animating the photo. If you're embedding the output in a website or presentation, request the export in MP4 format with a transparent-background option for overlay use.

When building workflows — such as auto-generating spokesperson videos from a CMS or producing personalized video messages at scale — pass the image URL and script text as variables. The skill returns a video URL or file you can route directly into your delivery pipeline, email platform, or social scheduler.

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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