diff --git a/public/banners/from-audio-to-a-4-minute-ai-music-video.jpg b/public/banners/from-audio-to-a-4-minute-ai-music-video.jpg index a496451..aa533f2 100644 Binary files a/public/banners/from-audio-to-a-4-minute-ai-music-video.jpg and b/public/banners/from-audio-to-a-4-minute-ai-music-video.jpg differ diff --git a/public/banners/from-audio-to-a-4-minute-ai-music-video.webp b/public/banners/from-audio-to-a-4-minute-ai-music-video.webp index dc43544..2f0e611 100644 Binary files a/public/banners/from-audio-to-a-4-minute-ai-music-video.webp and b/public/banners/from-audio-to-a-4-minute-ai-music-video.webp differ diff --git a/public/og/from-audio-to-a-4-minute-ai-music-video.jpg b/public/og/from-audio-to-a-4-minute-ai-music-video.jpg index ba435db..ac8e31d 100644 Binary files a/public/og/from-audio-to-a-4-minute-ai-music-video.jpg and b/public/og/from-audio-to-a-4-minute-ai-music-video.jpg differ diff --git a/public/og/thumb/from-audio-to-a-4-minute-ai-music-video-400.webp b/public/og/thumb/from-audio-to-a-4-minute-ai-music-video-400.webp index b97cc5d..0a05461 100644 Binary files a/public/og/thumb/from-audio-to-a-4-minute-ai-music-video-400.webp and b/public/og/thumb/from-audio-to-a-4-minute-ai-music-video-400.webp differ diff --git a/public/og/thumb/from-audio-to-a-4-minute-ai-music-video-800.webp b/public/og/thumb/from-audio-to-a-4-minute-ai-music-video-800.webp index 7972d27..fdf1556 100644 Binary files a/public/og/thumb/from-audio-to-a-4-minute-ai-music-video-800.webp and b/public/og/thumb/from-audio-to-a-4-minute-ai-music-video-800.webp differ diff --git a/public/og/zh-from-audio-to-a-4-minute-ai-music-video.jpg b/public/og/zh-from-audio-to-a-4-minute-ai-music-video.jpg index ba435db..ac8e31d 100644 Binary files a/public/og/zh-from-audio-to-a-4-minute-ai-music-video.jpg and b/public/og/zh-from-audio-to-a-4-minute-ai-music-video.jpg differ diff --git a/src/content/posts/from-audio-to-a-4-minute-ai-music-video.md b/src/content/posts/from-audio-to-a-4-minute-ai-music-video.md index f76a1df..d0df97c 100644 --- a/src/content/posts/from-audio-to-a-4-minute-ai-music-video.md +++ b/src/content/posts/from-audio-to-a-4-minute-ai-music-video.md @@ -3,7 +3,7 @@ title: "From Audio to a 4-Minute Music Video: Orchestrating Gemini Argon 4, AI D description: "A complete production breakdown of building a 4-minute 1080p anime OP music video (Crescent Bay): AI music generation, Gemini Argon 4 storyboard prompting, iterative image-to-video rerolls, and Filmora VFX editing." pubDate: 2026-10-25 category: case-studies -tags: ["ai-video", "music-video", "gemini", "filmora", "ai-music", "creative-workflows"] +tags: ["ai-video", "music-video", "gemini", "filmora", "face-swapping", "ai-music", "creative-workflows"] ogImage: "/og/from-audio-to-a-4-minute-ai-music-video.jpg" banner: "/banners/from-audio-to-a-4-minute-ai-music-video.jpg" draft: false @@ -78,6 +78,12 @@ Passing still images into video diffusion models (Veo, Kling, Runway) for 5 to 1 * **Micro-Motions Over Large Gestures:** In diffusion models, smaller motions look far more realistic. Complex full-body turns or rapid running almost always cause torso tearing; * **Multiple Rerolls to Harvest Usable Takes:** Producing a 10-second clip often takes 3 or more attempts. Even if a take begins to morph at second 6, the first 5 flawless seconds can be harvested and trimmed during editing. +### ③ Locking Facial Identity via Local FaceFusion +Even when hairstyle and clothing descriptors are nailed down in prompts, text-to-image and video diffusion models exhibit subtle bone-structure and facial drift across 20+ distinct scenes. Crucially, raw diffusion cannot guarantee that the protagonist in the final video matches the hero cover art. +* **Offline Local GPU Pipeline:** Deployed **FaceFusion** locally on an NVIDIA RTX 3060 12GB; +* **Beyond 2D Affine Warping:** Bypassed basic 128px insightface warping (which causes plastic edge shimmering) in favor of high-resolution **320 DFM deep neural models**, achieving seamless 3D contour integration and natural skin lighting; +* **Restoration & Detection:** Paired with **SCRFD** face detection and **GFPGAN** neural enhancement to lock in a striking, consistent East Asian heroine face across both key close-up video cuts and the official 2.7K cover artwork. + --- ## 4. Filmora Post-Production: Turning Raw Clips into a Music Video @@ -115,6 +121,7 @@ Producing this 4-minute *Crescent Bay* music video clarified what modern AI cont 2. **Tool Synergy Wins:** * **Gemini (Argon 4)** handles conceptualization, world-building, and structured storyboard prompting; * **AI Image & Video Diffusion** generate high-fidelity raw audio-visual assets; + * **FaceFusion (Local RTX 3060)** locks protagonist facial identity and consistency across key close-ups and hero cover art; * **Filmora** provides precise timeline control, clip assembly, rhythm synchronization, and post-processing VFX. This collaborative workflow makes it possible for a solo creator to deliver an entire 4-minute anime music video—a project that previously required a dedicated production team—in a matter of days from a single workstation. diff --git a/src/content/posts/zh/from-audio-to-a-4-minute-ai-music-video.md b/src/content/posts/zh/from-audio-to-a-4-minute-ai-music-video.md index 4857db9..157060b 100644 --- a/src/content/posts/zh/from-audio-to-a-4-minute-ai-music-video.md +++ b/src/content/posts/zh/from-audio-to-a-4-minute-ai-music-video.md @@ -3,7 +3,7 @@ title: "从一首歌到 4 分钟完整 MV：我如何用 Gemini、AI 生成与 F description: "基于《月牙湾》1080P 高清动漫 OP 制作实录：详解 AI 音乐重构、Gemini Argon 4 提示词分镜规划、文字生图与图生视频多次抽卡调优，以及 Filmora 剪辑与特效合成的真实生产实践。" pubDate: 2026-10-25 category: case-studies -tags: ["ai-video", "music-video", "gemini", "filmora", "ai-music", "creative-workflows"] +tags: ["ai-video", "music-video", "gemini", "filmora", "face-swapping", "ai-music", "creative-workflows"] ogImage: "/og/zh-from-audio-to-a-4-minute-ai-music-video.jpg" banner: "/banners/from-audio-to-a-4-minute-ai-music-video.jpg" draft: false @@ -78,6 +78,12 @@ AI 视频制作中耗费最多时间的，就是**“生图 -> 挑图 -> 垫图 * **动作幅度控制**：AI 生成长镜头时，动作幅度越小越逼真。大步奔跑或大幅度转身很容易引发身体部位撕裂； * **多次生成挑选最佳片段**：一个 10 秒的视频镜头，常常需要生成 3 次以上。有时候前 4 秒很自然、第 5 秒开始崩坏，这没关系——只要有几秒是完美的，就可以保留下来放到剪辑软件中裁剪使用。 +### ③ 面部一致性锁颜：本地 FaceFusion 深度模型解决“多镜头变脸” +即便在提示词中固定了发型与服饰，文生图与图生视频在 20 多个连续镜头中，人物五官骨相依然会出现微小的“角色漂移”，更无法直接保证封面海报与成片女主拥有完全一致的面孔。 +* **本地算力部署**：利用本机显卡（RTX 3060 12GB）离线运行 **FaceFusion** 神经网络换脸流水线； +* **拒绝 2D 贴纸感**：放弃容易产生贴纸感和边缘闪烁的通用 128 像素简易算法，选用训练精度更高的 **320 规格深度神经网络模型（DFM）**，让光影与 3D 轮廓深度融合； +* **高清面部修复**：配合 **SCRFD** 灵敏人脸检测与 **GFPGAN** 高清修复，将精挑细选的高辨识度东方少女面容（与封面海报保持 100% 同款面庞）无缝置换入主角核心特写镜头中，彻底消除“一转镜头换了个人”的违和感。 + --- ## 4. Filmora 剪辑与后期合成：真正让素材变成 MV 的关键一步 @@ -115,6 +121,7 @@ AI 生成出来的只是一个个孤立的视频切片，**把这些碎片变成 2. **多工具协同才是最优解**： * **Gemini (Argon 4)** 负责文字创意、世界观定调与分镜拆解； * **AI 绘图与视频扩散** 负责提供高质感的视听资产； + * **FaceFusion (本地 RTX 3060)** 负责锁定女主角核心特写镜头与封面海报的面部一致性； * **Filmora** 负责精准的时间轴控制、镜头拼接、声画对齐与视觉特效。 这种“AI 生成资产 + 人工剪辑雕琢”的工作流，让个人或小型团队在过去需要整个制作公司才能完成的 4 分钟完整 MV 制作，变成了一个人在工作站前几天内即可交付的现实。