不可能を、可能にしたプロジェクト

Mile 80がOpenArtを使ってアウトプットを大幅に拡大した方法、そしてワークフローが変わると何が可能になるのか

Ryan Shefferにとって、OpenArtとの関係は、大部分の作業をこなす1つの機能から始まりました。「一番長く使っていた機能はダントツで、開始フレームと終了フレームを指定して動画を作る機能でした」と彼は語ります。ショットの最初のフレームを生成し、最後のフレームを生成すれば、あとはモデルがその間の動きを埋めてくれるのです。単純に聞こえますが、これこそがShefferのスタジオ Mile 80は、世界で最も知られたブランドのいくつかにAI生成ショットを静かに導入してきました。最近のBMWの番組では、Mile 80がMini CooperとRolls Royceの動画を手がけ、Shefferによれば、そのシーズンのBMW関連の映像のほとんどには、OpenArtで生成または部分的に生成されたショットが少なくとも1つ含まれていたそうです。彼の言葉を借りれば「動画にAIが使われているとは思いもしないような」ハイエンドクライアントの類です。

Sheffer founded Mile 80 nearly a decade ago after years of working as what he calls "the nerdy guy" in post-production and visual effects. The studio built its name in live events: 360-degree screens, spatial storytelling, the kind of shows where a single project might need hundreds of assets rendered at 30K resolution. It's a business built on technical difficulty, and Sheffer has spent his career finding the next hard problem before everyone else does. That instinct is what brought him to AI early, and it's also what makes him refreshingly honest about where it actually helps.

開始フレームと終了フレームがすべてを変えた理由

Shefferさんに、チームがOpenArtを単体のジェネレーターとして扱うのをやめ、それを軸に組み立て始めてから何が起きたのかを聞きました。

転機が訪れたのは、OpenArtがMCPサーバーを公開したときでした。Mile 80は独自のインターフェースをその上に重ね始め、Claudeを使ってショットリストの作成から個々のフレームの批評まで、クリエイティブプロセス全体を管理し、実際の生成リクエストは下層のOpenArtにルーティングしています。「私たちはその上にUIを載せ始めました」とSheffer氏は説明します。「基本的に今はClaudeだけで作っていて、とても興味深いです」 だから今では、私たちのUIはOpenArtにとってのClaudeのような存在です。"

In practice, that means Mile 80's artists talk to Claude the way they'd talk to a coordinator on a physical set. They define characters, environments, and reference images, then Claude builds what Sheffer calls a production bible and shot list, and routes generation requests to OpenArt for the actual start and end frames. Two of his artists, Camila and Francez, built a custom Claude skill that takes a script, breaks it into individual shots, and fires off the OpenArt requests automatically. "It works really well," Sheffer says.

Shefferが最も誇りに思う作品の一つが、OpenArtで生成したフレームだけで作り上げたRolls RoyceのアニメーションMile 80です。彼いわく、AIが関わった前例のないタイプのラグジュアリーブランドの仕事だといいます。「これ以上ハイエンドなブランドは思いつきません」と彼は語ります。

OpenArt's contribution here goes beyond just start and end frames. Rather than generating a finished background, Mile 80 prompts OpenArt to build visuals with a green screen already baked into the shot, then relies on its own compositing tools, the same ones it's always used, to pull that green screen out and layer the shot into the scene. It's the detail that lets Sheffer's team keep working the way a VFX shop always has, just with a generated element standing in for what used to be a live-action plate. The same approach produced a character Mile 80 built for LangChain, an animated figure named Newton generated with a green screen baked in and then composited out, set to appear as part of a New York City subway campaign.

不可能なはずだったプロジェクト

The clearest example of what that pipeline makes possible is Gammarama, a live event series the client wanted positioned as something like the new TED talks: more serious than the brand's usual output, without losing its sense of fun. Sheffer's team had five weeks to develop the entire visual identity and then deliver on it. "There was only five weeks to develop the entire visual identity," he says, "what the deliverables would be, each of the video scripts, each of the videos, storyboards, and then execute and deliver for a large screen display."

Sheffer氏は、AIベースのパイプラインがなければこのスケジュールがどうなっていたかを率直に語ります。「絶対に不可能だったでしょう」と彼は言います。 「あれを実現できたなんて、本当に信じられません。」 締め切りに間に合わせるだけのために、チームはコンセプトをよりシンプルで平凡なものに縮小せざるを得なかったでしょう。複数のビジュアルスタイルを混ぜ合わせる要素も、画面上のアニメーション要素も減らし、クライアントが実際に受け取った重層的でスタイライズされたアイデンティティではなく、素直な編集に近いものになっていたはずです。彼は、野心的なバージョンを実現可能にするほどチームの AI ワークフローを前進させたのは、リードアニメーターの Fran のおかげだと語ります。

What made the timeline work wasn't a single clever trick so much as the team's willingness to treat "if this were possible, how would we do it" as the starting question, rather than "can we even attempt this." Green screen and alpha-channel generation handled the layering, while Claude, connected through MCP, handled direction and shot management. And because OpenArt could turn a concept into a usable start and end frame quickly, the team could iterate on a look before committing real production time to it.

丁寧な作品と粗悪品の違い

Sheffer's read on generative AI is more particular than the average AI-optimist take, partly because he's been burned by the hype cycle before. Back in 2013, before founding Mile 80, he ran a company that automatically generated short news videos from trending social posts, pulling content based on hashtag and location spikes and stitching it into a reel with an early large language model. It worked, in the sense that it got views. It also produced, by his own account, a video about a viral photo of a slice of pizza at the World Series that did numbers, and that was the moment he started backing away from the business. "I did see AI slop immediately as the core issue," he says. "That's funnily enough kind of why Mile 80 was started."

That history is why he's specific about what AI tools are actually good for. "The generative AI tools are the world's fastest, and in some ways dumbest, employees you have," he says. "You have to be incredibly specific with what you ask for. And if you correctly ask them for it, they will give it to you." He compares the goal to how David Fincher uses visual effects in nearly every shot of every film he makes, so seamlessly that audiences never register them as effects at all. "No one thinks of him as a visual effects director ever," Sheffer says. "Why? It's so good. It's integrated into the scene." The alternative, in his view, is the same failure mode people associate with the least-liked Marvel visual effects work: technically present, but not actually serving the story. "That's the same thing as AI slop," he says. "It's not understanding how to use the tool."

彼は自分自身の使い方については、どこで線を引くかも同じくらい明確です。「バナナがジョークを言う動画を10万本生成するために、OpenArtのAPIアクセスなんて絶対に欲しくない」と彼は言い、これを単なる冗談以上の意味で語ります。大量かつ無秩序な生成こそ、彼が前の会社を沈めるのを目の当たりにした失敗のパターンそのものなのです。

もっと大きな挑戦を、同じ締め切りで

Shefferは、生成が速くなることでMile 80が実際に得たものをごまかしません。それはクライアント数の増加ではなく、既存の締め切りの中で格段に野心的な仕事ができるようになったことです。「確かに速くなった」と彼は言う。「でも、今ならゴジラを登場させられるプロジェクトも、以前の方法ではゴジラを作る時間なんてなかった。アイデアからビジュアル化までの力が大きく向上したんです」 かつては締め切りに合わせてこっそり簡略化されていたようなコンセプトも、今ではブリーフが本来求めていたとおりの野心的なまま実現できます。

唯一変わっていない制約が、クライアントレビューです。「1日で動画を作れても、結局は承認プロセスを通さなければなりません」とSheffer氏は言います。生成のステップがどれだけ速くなっても、この承認サイクルがプロジェクトの実際のペースを決めるのです。ここで見過ごせない違いがあります。ここでの価値はスケジュールの短縮ではなく、すでにあるスケジュールの中に収められる内容の上限が大きく引き上げられることなのです。

次に立ちはだかる難題

The newest thread Mile 80 is pulling on is live action. Rather than generating a scene from scratch, the team is testing what happens when real footage, shot quickly and simply, gets AI effects, creature work, or set extension added in post. Sheffer thinks there's real room to run here: "I could see a workflow for sure of filming with an iPhone and getting out high quality Hollywood blockbuster style," he says, enough that he half-jokes about the approach eventually undercutting the traditional camera market.

The limiting factor, in his view, is still human emotion. "Our eyes are so used to, and our brains are so used to, knowing that's a human being, that it's always the hardest thing to fake," he says. That's part of why he's also been thinking through quicker face-replacement pipelines, layering real webcam performance onto a generated character rather than trying to generate the performance itself. It's a fitting place for the story to land: even the team most willing to push a pipeline as far as it can go still draws the line at the same place, the parts of the work that come from being human.