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(1/2) Check out 𝐌𝐞𝐬𝐡𝐆𝐏𝐓! MeshGPT generates triangle meshes by autoregressively sampling from a transformer model that produces tokens from a learned geometric vocabulary. As a result, we obtain clean and compact meshes :)

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Matthias Niessner

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396,324 просмотров • 2 лет назад •via X (Twitter)

ОбразованиеНаука и технологии
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(1/2) Check out "𝐏𝐨𝐥𝐲𝐃𝐢𝐟𝐟: Generating 3D Polygonal Meshes with Diffusion Models"! Our model operates directly on the polygons of 3D meshes and generates novel shapes as output through an iterative diffusion process.
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(1/2) Check out "𝐏𝐨𝐥𝐲𝐃𝐢𝐟𝐟: Generating 3D Polygonal Meshes with Diffusion Models"! Our model operates directly on the polygons of 3D meshes and generates novel shapes as output through an iterative diffusion process.

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We got it first - now it’s yours to play with. Scenario integrates Tencent Hy acclaimed model... HUNYUAN 3D 3.0 Clean, game-ready 3D meshes, from a single image or sketch. 👇👇
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We got it first - now it’s yours to play with. Scenario integrates Tencent Hy acclaimed model... HUNYUAN 3D 3.0 Clean, game-ready 3D meshes, from a single image or sketch. 👇👇

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Check out Easy Envelope, a new add-on for Blender that allows you to easily weight meshes with complex structures, such as cloaks, multi-layered skirts, and hair composed of meshes. Learn more and download: #blender #blender3d #b3d #gamedev #3d #3dart
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Check out Easy Envelope, a new add-on for Blender that allows you to easily weight meshes with complex structures, such as cloaks, multi-layered skirts, and hair composed of meshes. Learn more and download: #blender #blender3d #b3d #gamedev #3d #3dart

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Echo-2 is a physically-grounded world model from which we can distill meshes, point clouds, or 3DGS scene representations. Directly usable in a myriad of downstream applications from gaming to training robots. Want to build your own world? Try it here:
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Echo-2 is a physically-grounded world model from which we can distill meshes, point clouds, or 3DGS scene representations. Directly usable in a myriad of downstream applications from gaming to training robots. Want to build your own world? Try it here:

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(1/2) MonoNPHM will be presented as a #CVPR2024 Highlight! Our Neural Parametric Head Model parametrizes both geometry and appearance. With the learned model, we can then 3D reconstruct and track human heads from images or videos.
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(1/2) MonoNPHM will be presented as a #CVPR2024 Highlight! Our Neural Parametric Head Model parametrizes both geometry and appearance. With the learned model, we can then 3D reconstruct and track human heads from images or videos.

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ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies Contributions: 1) We propose ImmerseGen, a novel agent-guided 3D environment generation framework. It uses simplified geometric proxies with alpha-textured meshes to produce compact, photorealistic worlds ready for real-time mobile VR rendering. 2) We propose a novel RGBA texturing paradigm. It first synthesizes 8K terrain textures using a geometry-conditioned panorama generator via user-centric mapping, and then directly generates alpha-textured proxy assets, avoiding fidelity loss typically resulting from mesh decimation. 3) To automate scene creation from user prompts, we introduce VLM-based modeling agents equipped with a novel grid-based semantic analysis. This enables 3D spatial reasoning from 2D observations and ensures accurate asset placement. ImmerseGen further enhances immersion with dynamic effects and ambient audio for a multisensory experience. 4) Experiments on multiple scene-generation scenarios and live mobile VR applications show that ImmerseGen outperforms previous methods in visual quality, realism, spatial coherence, and rendering efficiency for immersive real-time VR experiences.
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ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies Contributions: 1) We propose ImmerseGen, a novel agent-guided 3D environment generation framework. It uses simplified geometric proxies with alpha-textured meshes to produce compact, photorealistic worlds ready for real-time mobile VR rendering. 2) We propose a novel RGBA texturing paradigm. It first synthesizes 8K terrain textures using a geometry-conditioned panorama generator via user-centric mapping, and then directly generates alpha-textured proxy assets, avoiding fidelity loss typically resulting from mesh decimation. 3) To automate scene creation from user prompts, we introduce VLM-based modeling agents equipped with a novel grid-based semantic analysis. This enables 3D spatial reasoning from 2D observations and ensures accurate asset placement. ImmerseGen further enhances immersion with dynamic effects and ambient audio for a multisensory experience. 4) Experiments on multiple scene-generation scenarios and live mobile VR applications show that ImmerseGen outperforms previous methods in visual quality, realism, spatial coherence, and rendering efficiency for immersive real-time VR experiences.

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Fuck yeah! MaskGCT - New open SoTA Text to Speech model! 🔥 > Zero-shot voice cloning > Emotional TTS > Trained on 100K hours of data > Long form synthesis > Variable speed synthesis > Bilingual - Chinese & English > Available on Hugging Face Fully non-autoregressive architecture: > Stage 1: Predicts semantic tokens from text, using tokens extracted from a speech self-supervised learning (SSL) model > Stage 2: Predicts acoustic tokens conditioned on the semantic tokens. Synthesised: "Would you guys personally like to have a fake fireplace, an electric one, in your house? Or would you rather have a real fireplace? Let me know down below. Okay everybody, that's all for today's video and I hope you guys learned a bunch of furniture vocabulary!" TTS scene keeps getting lit! 🐐
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Fuck yeah! MaskGCT - New open SoTA Text to Speech model! 🔥 > Zero-shot voice cloning > Emotional TTS > Trained on 100K hours of data > Long form synthesis > Variable speed synthesis > Bilingual - Chinese & English > Available on Hugging Face Fully non-autoregressive architecture: > Stage 1: Predicts semantic tokens from text, using tokens extracted from a speech self-supervised learning (SSL) model > Stage 2: Predicts acoustic tokens conditioned on the semantic tokens. Synthesised: "Would you guys personally like to have a fake fireplace, an electric one, in your house? Or would you rather have a real fireplace? Let me know down below. Okay everybody, that's all for today's video and I hope you guys learned a bunch of furniture vocabulary!" TTS scene keeps getting lit! 🐐

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Goalpara, Karbi Anglong and Cachar- I’ve been sampling pineapples from across Assam. The Cachar variant from yesterday stood out for its tangy flavour and a zero-waste circular farming model. We’re already India’s 2nd largest pineapple producer. We’re coming for No. 1.
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Goalpara, Karbi Anglong and Cachar- I’ve been sampling pineapples from across Assam. The Cachar variant from yesterday stood out for its tangy flavour and a zero-waste circular farming model. We’re already India’s 2nd largest pineapple producer. We’re coming for No. 1.

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Introducing our most accurate /search yet. We trained a model to return the excerpts that best answer your query, giving agents highly relevant context from each result. It's SOTA on SimpleQA while using 10x fewer tokens than processing full pages. Now on by default for free.
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Introducing our most accurate /search yet. We trained a model to return the excerpts that best answer your query, giving agents highly relevant context from each result. It's SOTA on SimpleQA while using 10x fewer tokens than processing full pages. Now on by default for free.

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(1/2) Check out 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧𝐀𝐯𝐚𝐭𝐚𝐫𝐬: Photorealistic Head Avatars with Rigged 3D Gaussians! We create photorealistic head avatars by animating 3D Gaussians on a parametric face model - edited and rendered in *real-time*!
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(1/2) Check out 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧𝐀𝐯𝐚𝐭𝐚𝐫𝐬: Photorealistic Head Avatars with Rigged 3D Gaussians! We create photorealistic head avatars by animating 3D Gaussians on a parametric face model - edited and rendered in *real-time*!

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🔥 2 BILLION $ATLA BURNED 🔥 Major milestone for the ATLA ecosystem. 2 BILLION tokens permanently removed from supply — cutting total allocation by nearly 2/3. 👇 Why it happened As ATLA demand and value grew, the team reviewed the original token allocation and decided to burn a large portion of undistributed tokens to strengthen the long-term token model. ⚠️ Important: This burn only affects undistributed tokens — current holders are not affected. Result: 📉 Lower future supply 📈 Stronger price fundamentals This makes $ATLA a much more limited and stronger asset🚀
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🔥 2 BILLION $ATLA BURNED 🔥 Major milestone for the ATLA ecosystem. 2 BILLION tokens permanently removed from supply — cutting total allocation by nearly 2/3. 👇 Why it happened As ATLA demand and value grew, the team reviewed the original token allocation and decided to burn a large portion of undistributed tokens to strengthen the long-term token model. ⚠️ Important: This burn only affects undistributed tokens — current holders are not affected. Result: 📉 Lower future supply 📈 Stronger price fundamentals This makes $ATLA a much more limited and stronger asset🚀

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Hamiltonian Monte Carlo frames sampling from a probability distribution as a physics problem. By endowing "particles" with momentum and simulating their energy and motion through Hamilton's equations you can efficiently explore a distribution.
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Hamiltonian Monte Carlo frames sampling from a probability distribution as a physics problem. By endowing "particles" with momentum and simulating their energy and motion through Hamilton's equations you can efficiently explore a distribution.

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We took a 30B model and split it in two to write tokens in parallel instead of one at a time. Introducing Nemotron-Labs-TwoTower: a diffusion language model from NVIDIA Research adapted from Nemotron-3-Nano-30B-A3B. Here’s how it works: one half holds the context, the other writes the tokens, with both reusing the pretrained model instead of training a new one from scratch. We found it kept 98.7% of the original model’s quality at 2.42× faster generation.

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(1/2) Intrinsic Image Diffusion for Single-view Material Estimation! We propose a probabilistic diffusion model to handle material & lighting ambiguities. We obtain sharp material estimates and facilitate high-fidelity relighting.
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(1/2) Intrinsic Image Diffusion for Single-view Material Estimation! We propose a probabilistic diffusion model to handle material & lighting ambiguities. We obtain sharp material estimates and facilitate high-fidelity relighting.

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We cooked and we fully built out a native Swift app from Burke Holland's awesome resize me and it totally worked with a 1 shot after advanced planning! Some deep insight from Pierce Boggan as always
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We cooked and we fully built out a native Swift app from Burke Holland's awesome resize me and it totally worked with a 1 shot after advanced planning! Some deep insight from Pierce Boggan as always

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Many 3D generators output Gaussian Splats (3DGS) for fast rendering, flexible deployment, and high visual fidelity. Static 3DGS aren't world models (no dynamics/semantics) but a true world model must allow distilling 3D-consistent representations for any given time step (3DGS/meshes). This post-distillation serves a dual purpose: 1) validates physical consistency of the model. 2) extracting explicit representations avoids continuously running a heavy generator, thus saves compute and facilitates real-time interaction.
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Many 3D generators output Gaussian Splats (3DGS) for fast rendering, flexible deployment, and high visual fidelity. Static 3DGS aren't world models (no dynamics/semantics) but a true world model must allow distilling 3D-consistent representations for any given time step (3DGS/meshes). This post-distillation serves a dual purpose: 1) validates physical consistency of the model. 2) extracting explicit representations avoids continuously running a heavy generator, thus saves compute and facilitates real-time interaction.

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Greptile just got a brand refresh done entirely in-house. we pulled inspiration from moments in the past that once felt futuristic, from the terminal to video games, 80s malls, neon genesis, and of course reptiles, their repeating scales, and their colorful environments. the result is a diverse color palette, intricate illustrations, and modular geometric forms that feel complex yet precise and ethereal. 🧵the process and thinking below:

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Open UI Local 1 Click Launcher Open UI from wan deeee bee is a cool AI app that live-generates UIs. It's like Vercel's V0 but open source, and works with both ChatGPT & open source models via ollama Make sure to check out Matthew Berman's tutorial, and run it with 1 click.

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Last week we released our Gemini 3.1 Pro Deep Think model. Here are a succession of examples of that model doing heat transfer analysis. No tools, just Deep Think and Image Generation. Step 1: generate a CAD file from a technical drawing.

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For the head angle, I learn from @ cillia new rigging file so check it out link below, it's super helpful and I learned a lot! #Vtuber #Live2d
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For the head angle, I learn from @ cillia new rigging file so check it out link below, it's super helpful and I learned a lot! #Vtuber #Live2d

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