Irvan Masyur-Sutan

Di Twitter ini, Kalo Spoke Up yang gue engga sukain bakal di massa atau di persekusi gak?

[Console Age @console_age]

Gw ngumpulin video buat ngejawabin orang di WhatsApp.

Silahkan disimpan 🙏🏻

[Irvan Masyur-Sutan @iMasyour]

@dailyplus62 @tempodotco Lu diem deh, kalo lu bikin statement yang ada beban mbg ngelanjut lagi

[Irvan Masyur-Sutan @iMasyour]

@mxvdxn @SamyazaSpeaks @dzine_ai sini bang code referal, thank you udah referensiin haha

[Glitter Gal @GlitterPixely]

Super simple, straight to the point, character sheet prompt optimized for Seedance. The simpler, the better sometimes. Here is my attempt to not weaken reference consistency (˶ᵔ ᵕ ᵔ˶) ‹3

Prompt ⤵️ + extra prompt in comments.

Create a professional animation-ready master character sheet optimized for Seedance 2.0 reference-to-video consistency.

Use the uploaded character image as the STRICT identity reference.

PRESERVE EXACTLY:
face shape, eye shape, hairstyle, body proportions, outfit design, accessories, color palette, materials, silhouette and art style.

Do not redesign the character.

STYLE:
[match the uploaded image’s art style], high-end animation production model sheet, clean readable forms, strong silhouette clarity.

BACKGROUND:
Pure white seamless background only.

FORMAT:
16:9 horizontal layout, clean minimal composition, no clutter, no text labels, no watermark, no UI.

INCLUDE:
1. Full-body front neutral pose
2. Full-body side view
3. Full-body back view
4. Full-body three-quarter hero pose
5. Dynamic action pose
6. Calm idle pose
7. Neutral face close-up
8. Intense expression close-up
9. Hand/accessory/detail closeups
10. Small color palette swatches

SEEDANCE OPTIMIZATION:
Keep the character large and readable.
Keep proportions identical across all views.
Keep hairstyle and clothing perfectly consistent.
Avoid overlapping limbs.
Avoid extreme perspective distortion.
Avoid cropped body parts.
Avoid excessive visual noise.
Prioritize reference-sheet readability over illustration complexity.

LIGHTING:
Soft professional studio lighting with clear material separation.

QUALITY:
AAA animation studio quality, cohesive design language, clean presentation.

[GORM THE OLD @GORMTHEOLD25]

Grand Theft Auto: Every Era of Human History (A.I. Video)

[Irvan Masyur-Sutan @iMasyour]

@themide_ @daraababyyy DM

[DStudioproject @D_studioproject]

Making a Pocari Sweat commercial ad in pixel art style using GPT Image 2.0 x Seedance 2.0 on OpenArt

workflow⬇️

[DStudioproject @D_studioproject]

Making commercial sapporo ad using GPT Image 2 x Seedance 2.0 on Dreamina

workflow ⬇️

[INK @0xInk_]

Just created my own AAA game
with GPT image 2 and Seedance 2

[Min Zhou @fMinZhou]

GPT Image 2 is insanely good...I generated a 360° equirectangular panorama in Happycapy with just a skill + prompt.

Step 1: Select the generate-image skill

Step 2: Enter a prompt like: “Use a frontend 360 viewer to display an equirectangular image of […] using the GPT-Image-2 model.”

Wanna see how you all get creative with this

[R@aiaicreate @aiaicreate]

ComfyUI向け、ワンクリックでデータセット生成可能なワークフローが登場。Klein 9bに対応。
u/Capitan01R-氏Ref Latent Controller活用オリジナルが基盤。
LORa不要で忠実な画像保存を可能にするFlux2Klein技術と連携、データ作成効率化。

#ComfyUI #StableDiffusion

URLはリプ⬇️

[zostaff @zostaff]

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION.

Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline.

YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway.

Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point.
But detection is just the start.

A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates.

Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset.

Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes.

All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point.
OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes.

No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU.

The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay.

Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools.

Trading here: https://t.co/IEU82UZaxz

Computer vision didn't just enter tennis. It made the expensive stuff free.

[zostaff @zostaff]

https://t.co/N7a7JwQRPe

[zostaff @zostaff]

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON.

Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system.

YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it.

Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names.
But here's where it gets wild.

Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video.

Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector.

Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels.

No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU.

The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output.

Teams used to pay six figures for this data. Now you build it in an afternoon.

Computer vision didn't just enter basketball. It democratized it.

[zostaff @zostaff]

AI FOOTBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON.

You upload a regular match video. No sensors, no GPS trackers, just camera footage.

The neural network finds every player, referee, and ball on its own. Every frame, in real time.

KMeans clustering breaks down jersey colors pixel by pixel. The system splits players into teams automatically. Without a single manual hint.

Optical Flow tracks camera movement. Separates it from player movement. Perspective Transformation converts pixels into real meters.

Speed of every player. Distance covered. Ball possession percentage. All calculated automatically.

Four hours of tutorial from zero to a working system.

The model is trained on real Bundesliga matches. Runs on a regular GPU. Python code - take it and run.

Sports analytics is no longer behind closed doors. AI leveled the playing field.

[Irvan Masyur-Sutan @iMasyour]

extend the GTA 5! @XboxGamePassPC you may leave other games but not the GTA

[ComfyUI @ComfyUI]

Control ComfyUI with Claude Code https://t.co/uguIYqabuC

[ハカセ アイ(Ai-Hakase)🐾最新トレンドＡＩのためのＸ 🐾 @ai_hakase_]

🚀 AIの限界突破！「究極の表現自由」を手に入れよう✨

AIに「倫理的に答えられません」と拒絶されたことはありませんか？
最新の「Qwen 3.5 非検閲モデル」と「ComfyUI」を組み合わせれば、そんな悩みとはおさらばです！

👇️ このスレッドで解説していきます！

#AI #Qwen35 #ComfyUI

[ハカセ アイ(Ai-Hakase)🐾最新トレンドＡＩのためのＸ 🐾 @ai_hakase_]

【ゲーム制作の神ツール】1枚の絵がAIで即アニメーション化！
👉 https://t.co/WTWIJlIL9z
1枚のキャラ画像をアップするだけで、ゲーム用スプライトシートをAIが自動生成する「AutoSprite」が画期的です。

待機、歩行、ジャンプなどの動きをAIが自動補完し、面倒なトリミングや位置合わせもすべて自動。Unityなどのゲームエンジンに即取り込める形式で書き出せます。

開発工数を大幅に削減し、インディーゲーム制作を劇的に加速させる魔法のようなツールですね！🚀

#AI #ゲーム制作

[TechHalla @techhalla]

Been grinding on Seedance 2.0 like crazy for the last 2 months. Dropping my best prompts below 👇

1. Putting yourself in the NBA