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Introducing SAM 3D, the newest addition to the SAM collection, bringing common sense 3D understanding of everyday images. SAM 3D includes two models: 🛋️ SAM 3D Objects for object and scene reconstruction 🧑‍🤝‍🧑 SAM 3D Body for human pose and shape estimation Both models achieve state-of-the-art performance transforming static...

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AI at Meta

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858,319 görüntüleme • 7 ay önce •via X (Twitter)

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SAM 3D enables accurate 3D reconstruction from a single image, supporting real-world applications in editing, robotics, and interactive scene generation. Matt, a SAM 3D researcher, explains how the two-model design makes this possible for both people and complex environments. 🔗 Read the SAM 3D Objects research paper: 🔗 Read the SAM 3D Body research paper:
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SAM 3D enables accurate 3D reconstruction from a single image, supporting real-world applications in editing, robotics, and interactive scene generation. Matt, a SAM 3D researcher, explains how the two-model design makes this possible for both people and complex environments. 🔗 Read the SAM 3D Objects research paper: 🔗 Read the SAM 3D Body research paper:

AI at Meta

17,858 görüntüleme • 7 ay önce

Today we’re excited to unveil a new generation of Segment Anything Models: 1️⃣ SAM 3 enables detecting, segmenting and tracking of objects across images and videos, now with short text phrases and exemplar prompts. 🔗 Learn more about SAM 3: 2️⃣ SAM 3D brings the model collection into the 3rd dimension to enable precise reconstruction of 3D objects and people from a single 2D image. 🔗 Learn more about SAM 3D: These models offer innovative capabilities and unique tools for developers and researchers to create, experiment and uplevel media workflows.
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Today we’re excited to unveil a new generation of Segment Anything Models: 1️⃣ SAM 3 enables detecting, segmenting and tracking of objects across images and videos, now with short text phrases and exemplar prompts. 🔗 Learn more about SAM 3: 2️⃣ SAM 3D brings the model collection into the 3rd dimension to enable precise reconstruction of 3D objects and people from a single 2D image. 🔗 Learn more about SAM 3D: These models offer innovative capabilities and unique tools for developers and researchers to create, experiment and uplevel media workflows.

AI at Meta

1,087,885 görüntüleme • 7 ay önce

SAM 3D is helping advance the future of rehabilitation. See how researchers at Carnegie Mellon University are using SAM 3D to capture and analyze human movement in clinical settings, opening the doors to personalized, data-driven insights in the recovery process. 🔗 Learn more about SAM 3D:
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SAM 3D is helping advance the future of rehabilitation. See how researchers at Carnegie Mellon University are using SAM 3D to capture and analyze human movement in clinical settings, opening the doors to personalized, data-driven insights in the recovery process. 🔗 Learn more about SAM 3D:

AI at Meta

66,941 görüntüleme • 7 ay önce

Just made Pascal's item catalog infinite. Pulling 3D objects straight out of images, live in the browser. SAM 3D Objects + Three.js + WebGPU. 👇
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Just made Pascal's item catalog infinite. Pulling 3D objects straight out of images, live in the browser. SAM 3D Objects + Three.js + WebGPU. 👇

Wawa Sensei

46,595 görüntüleme • 2 ay önce

3D ghibli sam > 2d ghibli sam
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3D ghibli sam > 2d ghibli sam

AK

131,252 görüntüleme • 1 yıl önce

LLaVA-3D A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D-awareness for 3D scene understanding has been hindered by the lack of large-scale 3D vision-language datasets and powerful 3D encoders. In this paper, we introduce a simple yet effective framework called LLaVA-3D. Leveraging the strong 2D understanding priors from LLaVA, our LLaVA-3D efficiently adapts LLaVA for 3D scene understanding without compromising 2D understanding capabilities. To achieve this, we employ a simple yet effective representation, 3D Patch, which connects 2D CLIP patch features with their corresponding positions in 3D space. By integrating the 3D Patches into 2D LMMs and employing joint 2D and 3D vision-language instruction tuning, we establish a unified architecture for both 2D image understanding and 3D scene understanding. Experimental results show that LLaVA-3D converges 3.5x faster than existing 3D LMMs when trained on 3D vision-language datasets. Moreover, LLaVA-3D not only achieves state-of-the-art performance across various 3D tasks but also maintains comparable 2D image understanding and vision-language conversation capabilities with LLaVA.
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LLaVA-3D A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D-awareness for 3D scene understanding has been hindered by the lack of large-scale 3D vision-language datasets and powerful 3D encoders. In this paper, we introduce a simple yet effective framework called LLaVA-3D. Leveraging the strong 2D understanding priors from LLaVA, our LLaVA-3D efficiently adapts LLaVA for 3D scene understanding without compromising 2D understanding capabilities. To achieve this, we employ a simple yet effective representation, 3D Patch, which connects 2D CLIP patch features with their corresponding positions in 3D space. By integrating the 3D Patches into 2D LMMs and employing joint 2D and 3D vision-language instruction tuning, we establish a unified architecture for both 2D image understanding and 3D scene understanding. Experimental results show that LLaVA-3D converges 3.5x faster than existing 3D LMMs when trained on 3D vision-language datasets. Moreover, LLaVA-3D not only achieves state-of-the-art performance across various 3D tasks but also maintains comparable 2D image understanding and vision-language conversation capabilities with LLaVA.

AK

41,713 görüntüleme • 1 yıl önce

Use SAM 3D exclusively on the Adam 3D Mesh Editor⚡️ Generate 3D assets in seconds!
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Use SAM 3D exclusively on the Adam 3D Mesh Editor⚡️ Generate 3D assets in seconds!

adam

19,939 görüntüleme • 6 ay önce

3D Trailer for Sam Raimi’s “SEND HELP”. This will be Raimi’s third 3D film following Oz the Great and Powerful & Doctor Strange in the Multiverse of Madness.
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3D Trailer for Sam Raimi’s “SEND HELP”. This will be Raimi’s third 3D film following Oz the Great and Powerful & Doctor Strange in the Multiverse of Madness.

Sam Raimi Updates

41,014 görüntüleme • 5 ay önce

I am releasing the “MLSharp” app for macOS. MLSharp is a premium macOS application that transforms 2D images into immersive 3D Gaussian Splatting (3DGS) models using state-of-the-art AI.
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I am releasing the “MLSharp” app for macOS. MLSharp is a premium macOS application that transforms 2D images into immersive 3D Gaussian Splatting (3DGS) models using state-of-the-art AI.

むっちゃん

90,625 görüntüleme • 2 ay önce

Amodal3R: Amodal 3D Reconstruction from Occluded 2D Images TL;DR: Given partially visible objects within images, Amodal3R reconstructs semantically meaningful 3D assets with reasonable geometry and plausible appearance.
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Amodal3R: Amodal 3D Reconstruction from Occluded 2D Images TL;DR: Given partially visible objects within images, Amodal3R reconstructs semantically meaningful 3D assets with reasonable geometry and plausible appearance.

Alexandre Morgand

23,300 görüntüleme • 1 yıl önce

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:
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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:

Tencent Hy

72,522 görüntüleme • 9 ay önce

An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion discuss: We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.
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An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion discuss: We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.

AK

66,412 görüntüleme • 1 yıl önce

Multi-modal #LLMs understand a lot about humans. But do they understand our 3D pose? We train #PoseGPT to estimate, generate, and reason about 3D human pose (#SMPL) in images and text. This is the first true foundation model for understanding 3D humans.
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Multi-modal #LLMs understand a lot about humans. But do they understand our 3D pose? We train #PoseGPT to estimate, generate, and reason about 3D human pose (#SMPL) in images and text. This is the first true foundation model for understanding 3D humans.

Michael Black

81,398 görüntüleme • 2 yıl önce

Introducing HO-Cap: A Capture System and Dataset for 3D Reconstruction and Pose Tracking of Hand-Object Interaction. We built a multi-camera system and a semi-automatic method for annotating the shape and pose of hands and objects Project page:
1:00

Introducing HO-Cap: A Capture System and Dataset for 3D Reconstruction and Pose Tracking of Hand-Object Interaction. We built a multi-camera system and a semi-automatic method for annotating the shape and pose of hands and objects Project page:

Yu Xiang

57,228 görüntüleme • 1 yıl önce

Introducing Wonder 3D, a new generative AI model in Flow Studio for fast and detailed 3D asset creation. 💬 Text to 3D — Watch your words take shape. Turn a simple prompt into fully textured 3D characters and props. 📸 Image to 3D — Add depth to your visuals. Transform concept art or reference images into dimensional 3D models. 🖼️ Text to Image — Visualize ideas early. Turn descriptions into concept imagery before converting them into 3D. Built for endless storytelling. Give your imagination some dimension. Available in all tiers. Find it under Wonder Tools. 👉 Get started: 💡 Learn more:
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Introducing Wonder 3D, a new generative AI model in Flow Studio for fast and detailed 3D asset creation. 💬 Text to 3D — Watch your words take shape. Turn a simple prompt into fully textured 3D characters and props. 📸 Image to 3D — Add depth to your visuals. Transform concept art or reference images into dimensional 3D models. 🖼️ Text to Image — Visualize ideas early. Turn descriptions into concept imagery before converting them into 3D. Built for endless storytelling. Give your imagination some dimension. Available in all tiers. Find it under Wonder Tools. 👉 Get started: 💡 Learn more:

Autodesk Flow Studio

107,724 görüntüleme • 4 ay önce

Collecting a high quality dataset with 4M unique phrases and 52M corresponding object masks helped SAM 3 achieve 2x the performance of baseline models. Kate, a researcher on SAM 3, explains how the data engine made this leap possible. 🔗 Read the SAM 3 research paper:
2:05

Collecting a high quality dataset with 4M unique phrases and 52M corresponding object masks helped SAM 3 achieve 2x the performance of baseline models. Kate, a researcher on SAM 3, explains how the data engine made this leap possible. 🔗 Read the SAM 3 research paper:

AI at Meta

38,684 görüntüleme • 7 ay önce

🚀Turn Single Image into 3D Human🚀 #GeneMAN is a generalizable single-image 3D human reconstruction framework that turns in-the-wild images into high-quality 3D humans with ease 🔗Project: 📜Paper: 🧑‍💻Code:
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🚀Turn Single Image into 3D Human🚀 #GeneMAN is a generalizable single-image 3D human reconstruction framework that turns in-the-wild images into high-quality 3D humans with ease 🔗Project: 📜Paper: 🧑‍💻Code:

Ziwei Liu

26,953 görüntüleme • 1 yıl önce

Introducing 𝐒𝐀𝐑𝟑𝐃, which tokenizes 3D objects into multiscale tokens and generates 3D objects by autoregressive next-scale prediction. 𝐒𝐀𝐑𝟑𝐃 enables fast 3D generation and comprehensive 3D understanding. arXiv: Project:
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Introducing 𝐒𝐀𝐑𝟑𝐃, which tokenizes 3D objects into multiscale tokens and generates 3D objects by autoregressive next-scale prediction. 𝐒𝐀𝐑𝟑𝐃 enables fast 3D generation and comprehensive 3D understanding. arXiv: Project:

Xingang Pan

28,248 görüntüleme • 1 yıl önce

🤯 Mind-blowing! Just witnessed the magic of #Palworld! 🔥 Check out the incredible Rodin Gen-1 🤖 transforming images into 3D models in just 10 seconds! 🚀 #CG #3D #game #GenerativeAI #GenAI #diffusion
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🤯 Mind-blowing! Just witnessed the magic of #Palworld! 🔥 Check out the incredible Rodin Gen-1 🤖 transforming images into 3D models in just 10 seconds! 🚀 #CG #3D #game #GenerativeAI #GenAI #diffusion

Deemos

114,398 görüntüleme • 2 yıl önce

oh my.. hows this even possible Meta just dropped SAM 3D, you can auto select any object in still image and.. turn them into high quality 3D model the model is free to download and test in playground link in comments
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oh my.. hows this even possible Meta just dropped SAM 3D, you can auto select any object in still image and.. turn them into high quality 3D model the model is free to download and test in playground link in comments

el.cine

794,496 görüntüleme • 7 ay önce