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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 görüntüleme • 1 yıl önce •via X (Twitter)

Eğitim Oyun Bilim & Teknoloji

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

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

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

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And *drumroll* EMOJI SUPPORT!

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

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So awesome

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🙏🙏

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Cool!!

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i bought it! 🎉

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

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1/ The first big announcement: Google DeepMind has unveiled Genie 3 A new frontier for world models, the first AI that generates interactive 3D worlds in real time at 24 fps. The breakthrough: minutes of consistency at 720p using only text descriptions. No pre-built 3D models required. Let's break it down - with some examples:

1/ The first big announcement: Google DeepMind has unveiled Genie 3 A new frontier for world models, the first AI that generates interactive 3D worlds in real time at 24 fps. The breakthrough: minutes of consistency at 720p using only text descriptions. No pre-built 3D models required. Let's break it down - with some examples:

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Tencent Hy

58,152 görüntüleme • 1 yıl önce

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

AK

249,708 görüntüleme • 3 yıl önce

I'm genuinely blown away by this. The leap from text descriptions straight to 3D models? It's next-level. Think about the possibility: a stream of prompts turns into a treasure trove of 3D pieces. Gather them, and you've got a full scene ready to come to life. The thought alone was exciting, but seeing it in action? Mind-bending. Take user 'NeonGlitch86' for instance. They've crafted an entire miniature world using nothing but Luma AI's #Genie text-to-3D tool combined with Blender's magic. "Totally geeking out with Luma AI. Today's project was a scene built from scratch with generated 3D models. Slapped some Mixamo rigs onto my characters and placed them into Blender. It's like a playground for bringing scene sketches to life." - NeonGlitch86

I'm genuinely blown away by this. The leap from text descriptions straight to 3D models? It's next-level. Think about the possibility: a stream of prompts turns into a treasure trove of 3D pieces. Gather them, and you've got a full scene ready to come to life. The thought alone was exciting, but seeing it in action? Mind-bending. Take user 'NeonGlitch86' for instance. They've crafted an entire miniature world using nothing but Luma AI's #Genie text-to-3D tool combined with Blender's magic. "Totally geeking out with Luma AI. Today's project was a scene built from scratch with generated 3D models. Slapped some Mixamo rigs onto my characters and placed them into Blender. It's like a playground for bringing scene sketches to life." - NeonGlitch86

Linus ✦ Ekenstam

696,108 görüntüleme • 2 yıl önce

Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation paper page: Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as composing multiple actions and defining precise durations for parts of the motion. To address this, we introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive, yet fine-grained, input interface for users. Instead of a single prompt, users can specify a multi-track timeline of multiple prompts organized in temporal intervals that may overlap. This enables specifying the exact timings of each action and composing multiple actions in sequence or at overlapping intervals. To generate composite animations from a multi-track timeline, we propose a new test-time denoising method. This method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. At every step of denoising, our method processes each timeline interval (text prompt) individually, subsequently aggregating the predictions with consideration for the specific body parts engaged in each action. Experimental comparisons and ablations validate that our method produces realistic motions that respect the semantics and timing of given text prompts.

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AK

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NeuralAI

45,051 görüntüleme • 1 yıl önce

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:)

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:)

Nicolas Zullo

13,418 görüntüleme • 3 ay önce

In our research lab, we are building “real-time dreaming” - the ability to generate fully playable video worlds prompted from any text or image. Our real-time, action conditioned world model (currently running internally at 16fps at 832x480p) is trained on a combination of data, including proprietary Roblox 3D avatar/world interaction data. World models are different from multiplayer engines in that they store state and memory in video latents. Roblox is multiplayer, and we are actively researching optimal ways to simultaneously store state for thousands of players, and keep them in sync with their environment. Our world model leverages database technology which stores all user interactions on Roblox in a vector format that can be used to re-render video and interaction from any camera angle. We see several immediate uses for our Roblox world model. We will use it side-by-side text, image and video prompts as a way to launch auto-generation of immersive worlds. In Roblox Studio, a creator could walk around and use prompts to “paint” a world and then convert it into a 3D representation or direct to Roblox native as a way for many people to play simultaneously. All of this comes alive as we explore the notion of a “Dream Theater” - where one user is dreaming, while others watch and prompt them. 2/4

Roblox

4,395,373 görüntüleme • 5 ay önce

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🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

SpAItial AI

176,105 görüntüleme • 7 ay önce

Adobe.. has released a tool that combines Generative Gaussian Splats with a Diffusion layer and it's not all over the internet? WHAT IS GOING ON :D I had to test this out ofc! Substance 3D viewer, the new 3d viewer just released by Adobe, not only supports viewing of a large, large list of 3d formats, but it also supports generating of 3d models as Gaussian Splats. Combine that with the built-in 3d to image functionality and you basically have at least a few of the steps I've been doing for the last year or so using multiple tools. Exciting stuff! The new Photoshop Beta also supports the 3D viewer and you can import 3d files directly in photoshop as smart objects linked to the viewer. The elements you prompt in based on your models, can be easily exported to your clipboard without backgrounds making it very easy to use this in your normal image editing workflows. I have a suspicion that we'll see much more of this in other Adobe tools and I'm very curious to see this used for things like this in more 3d tools than "just" Adobe Neo (which is fantastic btw). #adobe #art #gaussiansplatting

Adobe.. has released a tool that combines Generative Gaussian Splats with a Diffusion layer and it's not all over the internet? WHAT IS GOING ON :D I had to test this out ofc! Substance 3D viewer, the new 3d viewer just released by Adobe, not only supports viewing of a large, large list of 3d formats, but it also supports generating of 3d models as Gaussian Splats. Combine that with the built-in 3d to image functionality and you basically have at least a few of the steps I've been doing for the last year or so using multiple tools. Exciting stuff! The new Photoshop Beta also supports the 3D viewer and you can import 3d files directly in photoshop as smart objects linked to the viewer. The elements you prompt in based on your models, can be easily exported to your clipboard without backgrounds making it very easy to use this in your normal image editing workflows. I have a suspicion that we'll see much more of this in other Adobe tools and I'm very curious to see this used for things like this in more 3d tools than "just" Adobe Neo (which is fantastic btw). #adobe #art #gaussiansplatting

Martin Nebelong

280,005 görüntüleme • 1 yıl önce

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.

Mark Price

76,981 görüntüleme • 1 ay önce

"#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.

"#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.

Gabriele Romagnoli

2,177,047 görüntüleme • 1 yıl önce

Rethink what flexible 3D printing resin can do. It gets stronger every year! The new Formlabs Flexible 80A Resin V2 is a resilient 3D printing elastomer that rivals the look, feel, and function of 80A cast urethanes, TPUs, and hard rubbers. The design was created by Lais Kriwat, CEO of Kriwat GmbH, a German company with expertise in orthopedic technology and patient-specific solutions, including gait analysis, custom orthotics, orthopedic footwear, bracing, and compression therapy. We manufactured the parts and shipped them today so he can get a first-hand feel for the new material. More information about the new material here:

Rethink what flexible 3D printing resin can do. It gets stronger every year! The new Formlabs Flexible 80A Resin V2 is a resilient 3D printing elastomer that rivals the look, feel, and function of 80A cast urethanes, TPUs, and hard rubbers. The design was created by Lais Kriwat, CEO of Kriwat GmbH, a German company with expertise in orthopedic technology and patient-specific solutions, including gait analysis, custom orthotics, orthopedic footwear, bracing, and compression therapy. We manufactured the parts and shipped them today so he can get a first-hand feel for the new material. More information about the new material here:

Formlabs

834,155 görüntüleme • 1 ay önce

[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 görüntüleme • 2 yıl önce

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

17,571 görüntüleme • 1 yıl önce