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Hunyuan3D 2.0 for generating high-resolution textured 3D assets. Surpasses previous state-of-the-art models, both open-source and closed-source, in geometry details, condition alignment, and texture quality! 🤯 🤯

Gradio

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24,184 görüntüleme • 1 yıl önce •via X (Twitter)

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Which Machine Learning model delivers stronger trading results? Check out this free Substack post where I compare several powerful models that beat the market and show yearly returns of over 20%.

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Today, we’re launching Orpheus, an open-source TTS model that exceeds the capabilities of both open and closed-source models such as ElevenLabs and OpenAI! (1/6)

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Elias

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We are introducing Hunyuan3D-Part: an open-source part-level 3D shape generation model that outperforms all existing open and close-source models. Highlights: 🔹P3-SAM: The industry's first native 3D part segmentation model. 🔹X-Part: A part generation model that achieves state-of-the-art results in controllability and shape quality. Key-features: 1️⃣Eliminates the use of 2D SAM during training, relying solely on a large-scale dataset with 3.7 million shapes and clean part annotations. 2️⃣Introduces a new automated segmentation pipeline in 3D without user intervention. 3️⃣Implements a diffusion-based part decomposition pipeline utilizing both geometry and semantic clues. Code: Weights: Tech reports: 🔸P3-SAM： → Paper: → Project page: 🔸X-Part： → Paper: → Project page: Try it now： → (Light version) Hugging Face demo: → (Full version) Hunyuan3D Studio:

We are introducing Hunyuan3D-Part: an open-source part-level 3D shape generation model that outperforms all existing open and close-source models. Highlights: 🔹P3-SAM: The industry's first native 3D part segmentation model. 🔹X-Part: A part generation model that achieves state-of-the-art results in controllability and shape quality. Key-features: 1️⃣Eliminates the use of 2D SAM during training, relying solely on a large-scale dataset with 3.7 million shapes and clean part annotations. 2️⃣Introduces a new automated segmentation pipeline in 3D without user intervention. 3️⃣Implements a diffusion-based part decomposition pipeline utilizing both geometry and semantic clues. Code: Weights: Tech reports: 🔸P3-SAM： → Paper: → Project page: 🔸X-Part： → Paper: → Project page: Try it now： → (Light version) Hugging Face demo: → (Full version) Hunyuan3D Studio:

Tencent Hy

72,522 görüntüleme • 9 ay önce

Hello AI enthusiasts!👋 I’d like to share that we’re introducing the latest 3D foundation AI model AssetGen 2.0, which was designed to create high-quality 3D assets from text and image prompts. 💡AssetGen 2.0 consist of 2 models: one to generate the 3D Mesh, & a second one to generate textures. ℹ️ Technological Advancements: - Utilizes a single-stage 3D diffusion model for geometry estimation, leading to improved detail and fidelity compared to its predecessor, AssetGen 1.0. - TextureGen introduces methods for enhanced view consistency, texture in-painting, and higher texture resolution. 📌 Current Use and Future Plans: - Currently employed internally for creating 3D worlds. - Planned rollout to Horizon creators later this year. 👉 More details about this announcement: #Meta #AI #GenAI

Hello AI enthusiasts!👋 I’d like to share that we’re introducing the latest 3D foundation AI model AssetGen 2.0, which was designed to create high-quality 3D assets from text and image prompts. 💡AssetGen 2.0 consist of 2 models: one to generate the 3D Mesh, & a second one to generate textures. ℹ️ Technological Advancements: - Utilizes a single-stage 3D diffusion model for geometry estimation, leading to improved detail and fidelity compared to its predecessor, AssetGen 1.0. - TextureGen introduces methods for enhanced view consistency, texture in-painting, and higher texture resolution. 📌 Current Use and Future Plans: - Currently employed internally for creating 3D worlds. - Planned rollout to Horizon creators later this year. 👉 More details about this announcement: #Meta #AI #GenAI

Dilmer Valecillos

28,153 görüntüleme • 1 yıl önce

🤯DiffIR2VR-Zero: Zero-shot video restoration to high-resolution using pre-trained image restoration diffusion models. - Framework can handle video denoising and up to 8x super-resolution - outperforms trained models in generalizing across diverse datasets and extreme degradations - Compatible with every 2D restoration model

🤯DiffIR2VR-Zero: Zero-shot video restoration to high-resolution using pre-trained image restoration diffusion models. - Framework can handle video denoising and up to 8x super-resolution - outperforms trained models in generalizing across diverse datasets and extreme degradations - Compatible with every 2D restoration model

Gradio

20,342 görüntüleme • 2 yıl önce

🚀Introducing Hunyuan3D-PolyGen, our newly upgraded and industry-first art-grade 3D generative model. It brings effortless intelligent retopology, making AI-generated models ready for professional art pipelines. ✅ Superior Mesh Topology: Our self-developed mesh autoregressive model ensures higher-quality mesh topology that meets stringent art standards. ✅ Complex Object Modeling: Leveraging our high-compression BPT representation, we can generate models with 10K+ faces, enabling more complex geometry, higher topology precision, and better detail. ✅ Flexible Output: Supports both tri and quad meshes, meeting diverse pipeline requirements. Hunyuan3D-PolyGen enables direct application of AI-generated 3D assets in game development and significantly boosts artist modeling efficiency. It's a robust foundation for the future of 3D content creation. 👉Try it now:

🚀Introducing Hunyuan3D-PolyGen, our newly upgraded and industry-first art-grade 3D generative model. It brings effortless intelligent retopology, making AI-generated models ready for professional art pipelines. ✅ Superior Mesh Topology: Our self-developed mesh autoregressive model ensures higher-quality mesh topology that meets stringent art standards. ✅ Complex Object Modeling: Leveraging our high-compression BPT representation, we can generate models with 10K+ faces, enabling more complex geometry, higher topology precision, and better detail. ✅ Flexible Output: Supports both tri and quad meshes, meeting diverse pipeline requirements. Hunyuan3D-PolyGen enables direct application of AI-generated 3D assets in game development and significantly boosts artist modeling efficiency. It's a robust foundation for the future of 3D content creation. 👉Try it now:

Tencent Hy

161,189 görüntüleme • 1 yıl önce

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

AK

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📣 New research from GenAI at Meta, introducing Meta 3D Gen: A new system for end-to-end generation of 3D assets from text in <1min. Meta 3D Gen is a new combined AI system that can generate high-quality 3D assets, with both high-resolution textures and material maps end-to-end, producing results that are superior to existing solutions — at 3-10x the speed of existing work in this space. Details in the technical report ➡️

📣 New research from GenAI at Meta, introducing Meta 3D Gen: A new system for end-to-end generation of 3D assets from text in <1min. Meta 3D Gen is a new combined AI system that can generate high-quality 3D assets, with both high-resolution textures and material maps end-to-end, producing results that are superior to existing solutions — at 3-10x the speed of existing work in this space. Details in the technical report ➡️

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⚙️ 404—GEN has released the WORLD'S LARGEST open-source 3D model dataset ⚙️ • 21.5M+ AI-generated 3d models • Open-source and attribution-ready • Larger than all existing 3D datasets combined This is an unmatched scale for Gaussian Splatting research. More details below 🧵 ⬇️

404

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Run state-of-the-art RAG applications locally on your computer with ollama and use all the fantastic open-source models like llama3, msk's awesome models, or Command R from Cohere With Verba 1.0, we put it all in your hands 🙌 Get on board for a wild open-source ride, we're bridging any moat as open-source is here to win

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🎬Dive into Hunyuan 3D 2.1, featuring high-precision geometry and PBR textures for stunningly realistic rendering. Get started with our comprehensive open-source package and tutorials⬇️ Model: Github: Hunyuan 3D Creation Engine:

🎬Dive into Hunyuan 3D 2.1, featuring high-precision geometry and PBR textures for stunningly realistic rendering. Get started with our comprehensive open-source package and tutorials⬇️ Model: Github: Hunyuan 3D Creation Engine:

Tencent Hy

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Want high-quality 3D meshes with sharp geometric details? Try our newly released MeshFormer! It only takes 8 GPUs for two days of training, outperforming state-of-the-art models that use over a hundred GPUs! With 3D-native input guidance, representations, supervision, and post-processing, we significantly improve the training efficiency and geometric quality of feed-forward reconstruction models! Project page: Chong Zeng

Want high-quality 3D meshes with sharp geometric details? Try our newly released MeshFormer! It only takes 8 GPUs for two days of training, outperforming state-of-the-art models that use over a hundred GPUs! With 3D-native input guidance, representations, supervision, and post-processing, we significantly improve the training efficiency and geometric quality of feed-forward reconstruction models! Project page: Chong Zeng

Minghua Liu

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Another amazing project made with #raylib: Hawkeye A real-time 3D multi-drone flight visualizer for PX4, the open-source autopilot stack for drones and unmanned vehicles! 🤯 - Hawkeye: - Source code: by PX4 Autopilot

Another amazing project made with #raylib: Hawkeye A real-time 3D multi-drone flight visualizer for PX4, the open-source autopilot stack for drones and unmanned vehicles! 🤯 - Hawkeye: - Source code: by PX4 Autopilot

Ray

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Acto3D: an open-source user-friendly volume rendering software for high-resolution 3D fluorescence imaging in biology Read this Techniques and Resources Article by Naoki Takeshita, Kenta Yashiro Kenta Yashiro and colleagues:

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The quality, cost, and control you can achieve for upscaling + fixing plastic AI skin with open source models still amazes me... Models used: → Z-image-turbo for image gen (~3s) → SDXL + Lora for skin texture (~15s) → SeedVR2 for upscaling (~40s)

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KDTalker is really wild 🤯👇 - Can run on single 4090 or 3090 🤩 - Uses Diffusion for generating audio-driven Talking Portraits - Preserves character identity with fine facial details - Expressions change with the speech, giving high pose diversity

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Gradio

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We are building a state-of-the-art humanoid for $2500 ... and open-source 🤗 Prototype designed by Rob Knight 🧠

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Remi Cadene

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🚀 Meshy-5 Preview is HERE! Use code MESHY5 for 50% off → 🔥 New Upgrades: - De-lit Textures - Razor-sharp Geometry - High Geometry-Texture Alignment Watch the trailer now and see why Meshy is the closest to 3D production! #3dmodeling #3dprinting #3dart #CG #ai

🚀 Meshy-5 Preview is HERE! Use code MESHY5 for 50% off → 🔥 New Upgrades: - De-lit Textures - Razor-sharp Geometry - High Geometry-Texture Alignment Watch the trailer now and see why Meshy is the closest to 3D production! #3dmodeling #3dprinting #3dart #CG #ai

MeshyAI

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Dreaming Tulpa 🥓👑

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