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⏰As of today, 3D Text auto Translates in-game using Localization Service 🎉 🥳I'm releasing my new plugin: "3D Text Pro" ⚡️Highlight Features: Curve 3D Text (Splines), Auto-Translate, Presets, Emojis Get It Here: 🧵Look at the thread for more information #ROBLOX #RobloxDev

KinqAndi

4,801 subscribers

22,900 views • 1 year ago •via X (Twitter)

Education Gaming Science & Technology

Anya Rossi• Live Now

Private livecam show

11 Comments

KinqAndi1 year ago

In-game auto-translation uses Roblox's LocalizationService!

KinqAndi1 year ago

You can use spline nodes to curve your text how you prefer!

KinqAndi1 year ago

You can save & load presets for a one-time click text creation!

KinqAndi1 year ago

And *drumroll* EMOJI SUPPORT!

KinqAndi1 year ago

Coming Soon Features: Tutorial: - Upload custom .ttf font - Mirroring - More font styles - etc

DV1 year ago

So awesome

KinqAndi1 year ago

🙏🙏

Megan ✧.*1 year ago

Cool!!

KinqAndi1 year ago

🙏

FrankieFms1 year ago

i bought it! 🎉

KinqAndi1 year ago

😁😁 Leave a review if you like it!! And thank you for the support 🙌🏼

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45,051 views • 1 year ago

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4,392,041 views • 5 months ago

Ok I've used Codex 5.4 Extra High for something that saved me so many hours and thousands of dollars and may interest you: -> how I automatically translate my game in 9 languages 1) I did the whole game in English, with no localization in mind 2) Then everybody told me that the reach is way higher by adding local languages 3) So I had a long chat with Codex to write a great spec to build a localization interface and super simple process for afterwards (I want to keep the simplicity) 4) Then translated the game in 9 languages! 5) Every localization is challenged a second time to improve 6) We are talking 580 keys per language - it took 1h35 to find all the hardcoded text and add keys - then it took 1h15 min to do all v1 translations 7) BONUS: it translates automatically all audio with ElevenLabs (using their text to speech skill) Total: Roughly 3 hours to implement a translation system and to translate the game Can any Japanese person tell me if the translation is good? There were 2 challenges: - translating once - keeping the game translated Now it's a blessing: I still work on the game like the usual, and if I add new text, it will automatically create the keys and translate in all languages just with basic instructions in Agents.md + a pre-commit hook to ensure it's done. I hope to use the tool for a professional or community-based human review in the future! Hope it helps:)

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175,909 views • 7 months ago

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279,983 views • 1 year ago

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"#AI will never create clean meshes" AI 👇 This is the new #AI model from NVIDIA called Meshtron (jeez what a name 😅). It generates meshes of complex 3D objects closely resembling those created by professional artists. It opens the door to a more realistic generation of detailed 3D assets for animation, gaming, and virtual environments. The current version accepts the following control inputs: - Point cloud: Determines the shape of the output mesh. - Face count: Determines the density of the output mesh. - Quad ratio: Switches between quad and triangle tessellation. - Creativity: Can be adjusted to generate extra details in the model. Meshtron can either serve as a stand-alone remesher to improve the quality of existing models, or be used in tandem with a text-to-3D or image-to-3D model to generate artist-grade meshes from scratch.

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2,176,749 views • 1 year ago

One team spent 3 hours per Roblox UGC item. I built them pipeline that does it in under 5 minutes, reference image to published, hands-off. Watch the whole run in 48 seconds 👇 Generated with Roblox new Cube 3D, then fitted, thumbnailed, titled, validated, and auto-fixed by a Claude skill and a Studio plugin I built. Reference image to live on the Marketplace, hands-off. Need rapid tooling like this or a 7-day game prototype? I'm taking on a couple of projects. DMs open.

One team spent 3 hours per Roblox UGC item. I built them pipeline that does it in under 5 minutes, reference image to published, hands-off. Watch the whole run in 48 seconds 👇 Generated with Roblox new Cube 3D, then fitted, thumbnailed, titled, validated, and auto-fixed by a Claude skill and a Studio plugin I built. Reference image to live on the Marketplace, hands-off. Need rapid tooling like this or a 7-day game prototype? I'm taking on a couple of projects. DMs open.

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76,539 views • 29 days ago

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Formlabs

833,774 views • 28 days ago

[CLIP] by Hand ✍️ The CLIP (Contrastive Language–Image Pre-training) model, a groundbreaking work by OpenAI, redefines the intersection of computer vision and natural language processing. It is the basis of all the multi-modal foundation models we see today. How does CLIP work? Goal: 🟨 Learn a shared embedding space for text and image [1] Given ↳ A mini batch of 3 text-image pairs ↳ OpenAI used 400 million text-image pairs to train its original CLIP model. Process 1st pair: "big table" [2] 🟪 Text → 2 Vectors (3D) ↳ Look up word embedding vectors using word2vec. [3] 🟩 Image → 2 Vectors (4D) ↳ Divide the image into two patches. ↳ Flatten each patch [4] Process other pairs ↳ Repeat [2]-[3] [5] 🟪 Text Encoder & 🟩 Image Encoder ↳ Encode input vectors into feature vectors ↳ Here, both encoders are simple one layer perceptron (linear + ReLU) ↳ In practice, the encoders are usually transformer models. [6] 🟪 🟩 Mean Pooling: 2 → 1 vector ↳ Average 2 feature vectors into a single vector by averaging across the columns ↳ The goal is to have one vector to represent each image or text [7] 🟪 🟩 -> 🟨 Projection ↳ Note that the text and image feature vectors from the encoders have different dimensions (3D vs. 4D). ↳ Use a linear layer to project image and text vectors to a 2D shared embedding space. 🏋️ Contrastive Pre-training 🏋️ [8] Prepare for MatMul ↳ Copy text vectors (T1,T2,T3) ↳ Copy the transpose of image vectors (I1,I2,I3) ↳ They are all in the 2D shared embedding space. [9] 🟦 MatMul ↳ Multiply T and I matrices. ↳ This is equivalent to taking dot product between every pair of image and text vectors. ↳ The purpose is to use dot product to estimate the similarity between a pair of image-text. [10] 🟦 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [11] 🟦 Softmax: ∑ ↳ Sum each row for 🟩 image→🟪 text ↳ Sum each column for 🟪 text→ 🟩 image [12] 🟦 Softmax: 1 / sum ↳ Divide each element by the column sum to obtain a similarity matrix for 🟪 text→🟩 image ↳ Divide each element by the row sum to obtain a similarity matrix for 🟩 image→🟪 text [13] 🟥 Loss Gradients ↳ The "Targets" for the similarity matrices are Identity Matrices. ↳ Why? If I and T come from the same pair (i=j), we want the highest value, which is 1, and 0 otherwise. ↳ Apply the simple equation of [Similarity - Target] to compute gradients of for both directions. ↳ Why so simple? Because when Softmax and Cross-Entropy Loss are used together, the math magically works out that way. ↳ These gradients kick off the backpropagation process to update weights and biases of the encoders and projection layers (red borders).

[CLIP] by Hand ✍️ The CLIP (Contrastive Language–Image Pre-training) model, a groundbreaking work by OpenAI, redefines the intersection of computer vision and natural language processing. It is the basis of all the multi-modal foundation models we see today. How does CLIP work? Goal: 🟨 Learn a shared embedding space for text and image [1] Given ↳ A mini batch of 3 text-image pairs ↳ OpenAI used 400 million text-image pairs to train its original CLIP model. Process 1st pair: "big table" [2] 🟪 Text → 2 Vectors (3D) ↳ Look up word embedding vectors using word2vec. [3] 🟩 Image → 2 Vectors (4D) ↳ Divide the image into two patches. ↳ Flatten each patch [4] Process other pairs ↳ Repeat [2]-[3] [5] 🟪 Text Encoder & 🟩 Image Encoder ↳ Encode input vectors into feature vectors ↳ Here, both encoders are simple one layer perceptron (linear + ReLU) ↳ In practice, the encoders are usually transformer models. [6] 🟪 🟩 Mean Pooling: 2 → 1 vector ↳ Average 2 feature vectors into a single vector by averaging across the columns ↳ The goal is to have one vector to represent each image or text [7] 🟪 🟩 -> 🟨 Projection ↳ Note that the text and image feature vectors from the encoders have different dimensions (3D vs. 4D). ↳ Use a linear layer to project image and text vectors to a 2D shared embedding space. 🏋️ Contrastive Pre-training 🏋️ [8] Prepare for MatMul ↳ Copy text vectors (T1,T2,T3) ↳ Copy the transpose of image vectors (I1,I2,I3) ↳ They are all in the 2D shared embedding space. [9] 🟦 MatMul ↳ Multiply T and I matrices. ↳ This is equivalent to taking dot product between every pair of image and text vectors. ↳ The purpose is to use dot product to estimate the similarity between a pair of image-text. [10] 🟦 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [11] 🟦 Softmax: ∑ ↳ Sum each row for 🟩 image→🟪 text ↳ Sum each column for 🟪 text→ 🟩 image [12] 🟦 Softmax: 1 / sum ↳ Divide each element by the column sum to obtain a similarity matrix for 🟪 text→🟩 image ↳ Divide each element by the row sum to obtain a similarity matrix for 🟩 image→🟪 text [13] 🟥 Loss Gradients ↳ The "Targets" for the similarity matrices are Identity Matrices. ↳ Why? If I and T come from the same pair (i=j), we want the highest value, which is 1, and 0 otherwise. ↳ Apply the simple equation of [Similarity - Target] to compute gradients of for both directions. ↳ Why so simple? Because when Softmax and Cross-Entropy Loss are used together, the math magically works out that way. ↳ These gradients kick off the backpropagation process to update weights and biases of the encoders and projection layers (red borders).

Tom Yeh

67,834 views • 2 years ago

On deck in our #CVPR2025 series: ChatGarment 👚✨ By Siyuan Bian, Chenghao Xu, Yuliang Xiu, Artur Grigorev, Zhen Liu, Cewu Lu, Michael J. Black and Yao Feng. Feed it an image, video or text, and watch a tailored 3D outfit appear. The video walks through: 1️⃣ Image-based sewing pattern estimation 2️⃣ Text-to-garment generation 3️⃣ Text-based editing Why it matters • Designers preview new looks before cutting fabric • Game and film studios drop physics-ready clothes onto avatars in minutes • Researchers study cloth–body dynamics without manual labeling 🎯 Crafting catwalk looks or decking out game avatars? Just type a prompt and ChatGarment whips up, shrinks, or totally restyles the outfit—no scissors required! 👋 Swing by our #CVPR2026 booth 1333 to meet some of the minds behind ChatGarment, and stay tuned for more CVPR paper videos! 📄 Paper link in the thread/comment. #GarmentTech #3DFashion #DigitalHuman #ComputerVision #AI #MachineLearning

On deck in our #CVPR2025 series: ChatGarment 👚✨ By Siyuan Bian, Chenghao Xu, Yuliang Xiu, Artur Grigorev, Zhen Liu, Cewu Lu, Michael J. Black and Yao Feng. Feed it an image, video or text, and watch a tailored 3D outfit appear. The video walks through: 1️⃣ Image-based sewing pattern estimation 2️⃣ Text-to-garment generation 3️⃣ Text-based editing Why it matters • Designers preview new looks before cutting fabric • Game and film studios drop physics-ready clothes onto avatars in minutes • Researchers study cloth–body dynamics without manual labeling 🎯 Crafting catwalk looks or decking out game avatars? Just type a prompt and ChatGarment whips up, shrinks, or totally restyles the outfit—no scissors required! 👋 Swing by our #CVPR2026 booth 1333 to meet some of the minds behind ChatGarment, and stay tuned for more CVPR paper videos! 📄 Paper link in the thread/comment. #GarmentTech #3DFashion #DigitalHuman #ComputerVision #AI #MachineLearning

Meshcapade

17,541 views • 1 year ago