Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

Today we presented our work on Action Recognition with 3D pose and tracking at #CVPR. We also released our code, models, and dataset of 1.5 million human trajectories. Code: Project Page:

Jathushan Rajasegaran

1,626 subscribers

132,016 Aufrufe • vor 3 Jahren •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

We are excited to share EMDB, a novel dataset of 3D human poses for in-the-wild monocular videos, including global trajectories. Data and toolkit code is now available. More details in the thread below. Project Page:

We are excited to share EMDB, a novel dataset of 3D human poses for in-the-wild monocular videos, including global trajectories. Data and toolkit code is now available. More details in the thread below. Project Page:

AIT Lab

14,613 Aufrufe • vor 3 Jahren

We just released HaMeR, our latest work for Hand Mesh Recovery! We reconstruct hands in 3D from a single image with big improvements in accuracy and robustness. Code and models are available. Also colab and Hugging Face demo!

We just released HaMeR, our latest work for Hand Mesh Recovery! We reconstruct hands in 3D from a single image with big improvements in accuracy and robustness. Code and models are available. Also colab and Hugging Face demo!

Georgios Pavlakos

38,336 Aufrufe • vor 2 Jahren

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:

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

57,357 Aufrufe • vor 1 Jahr

The code, dataset and demos for our #SIGGRAPH2024 work on generating embodied avatar movements with Codebook Matching are now available! Everything can be found on the project page, including a precompiled binary that you can play around with!

Sebastian Starke

18,400 Aufrufe • vor 2 Jahren

PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking paper page: introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework, for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion. Toward the goal of naturalism, we animate deformable characters using real-world motion capture data, we build 3D scenes to match the motion capture environments, and we render camera viewpoints using trajectories mined via structure-from-motion on real videos. We create combinatorial diversity by randomizing character appearance, motion profiles, materials, lighting, 3D assets, and atmospheric effects. Our dataset currently includes 104 videos, averaging 2,000 frames long, with orders of magnitude more correspondence annotations than prior work. We show that existing methods can be trained from scratch in our dataset and outperform the published variants. Finally, we introduce modifications to the PIPs point tracking method, greatly widening its temporal receptive field, which improves its performance on PointOdyssey as well as on two real-world benchmarks.

PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking paper page: introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework, for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion. Toward the goal of naturalism, we animate deformable characters using real-world motion capture data, we build 3D scenes to match the motion capture environments, and we render camera viewpoints using trajectories mined via structure-from-motion on real videos. We create combinatorial diversity by randomizing character appearance, motion profiles, materials, lighting, 3D assets, and atmospheric effects. Our dataset currently includes 104 videos, averaging 2,000 frames long, with orders of magnitude more correspondence annotations than prior work. We show that existing methods can be trained from scratch in our dataset and outperform the published variants. Finally, we introduce modifications to the PIPs point tracking method, greatly widening its temporal receptive field, which improves its performance on PointOdyssey as well as on two real-world benchmarks.

AK

122,560 Aufrufe • vor 3 Jahren

Excited to share our recent work, UniSH, which unifies dynamic 3D scene reconstruction and SMPL estimation within a single framework. (Left-top is input video). Code has been released! Project page: Paper:

Excited to share our recent work, UniSH, which unifies dynamic 3D scene reconstruction and SMPL estimation within a single framework. (Left-top is input video). Code has been released! Project page: Paper:

Yuan Liu

19,258 Aufrufe • vor 7 Monaten

The BEDLAM2.0 dataset (B2) is here, just in time to train your 3D human pose and shape estimation methods for CVPR. B2 goes beyond BEDLAM (B1) to include widely varied and natural camera motions and fields of view, more diverse body shapes, strand-based hair, more garments, shoes, more body motions, and more 3D scenes. Compared with B1, training on B2 produces more accurate 3D human pose, resulting in SOTA accuracy, particularly for estimates in world coordinates. B2 lets you jointly train camera motion and human motion regressors, and we also provide depth maps. Check out data, code, dataset statistics, and much more. BEDLAM2.0 will appear in the 2025 NeurIPS Datasets and Benchmarks Track. Joint work with Joachim Tesch, Giorgio Becherini, Prerana Achar, Anastasios Yiannakidis, Muhammed Kocabas, Priyanka Patel.

The BEDLAM2.0 dataset (B2) is here, just in time to train your 3D human pose and shape estimation methods for CVPR. B2 goes beyond BEDLAM (B1) to include widely varied and natural camera motions and fields of view, more diverse body shapes, strand-based hair, more garments, shoes, more body motions, and more 3D scenes. Compared with B1, training on B2 produces more accurate 3D human pose, resulting in SOTA accuracy, particularly for estimates in world coordinates. B2 lets you jointly train camera motion and human motion regressors, and we also provide depth maps. Check out data, code, dataset statistics, and much more. BEDLAM2.0 will appear in the 2025 NeurIPS Datasets and Benchmarks Track. Joint work with Joachim Tesch, Giorgio Becherini, Prerana Achar, Anastasios Yiannakidis, Muhammed Kocabas, Priyanka Patel.

Michael Black

27,536 Aufrufe • vor 9 Monaten

TidyBot: Personalized Robot Assistance with Large Language Models approach enables fast adaptation and achieves 91.2% accuracy on unseen objects in our benchmark dataset. We also demonstrate our approach on a real-world mobile manipulator called TidyBot, which successfully puts away 85.0% of objects in real-world test scenarios abs: project page: paper page:

TidyBot: Personalized Robot Assistance with Large Language Models approach enables fast adaptation and achieves 91.2% accuracy on unseen objects in our benchmark dataset. We also demonstrate our approach on a real-world mobile manipulator called TidyBot, which successfully puts away 85.0% of objects in real-world test scenarios abs: project page: paper page:

AK

326,009 Aufrufe • vor 3 Jahren

I'm excited to share our new work, VistaDream, which generates a 3D Gaussian field from a single-view image. The codes have already been released. Project page: (with interactive demos) Code: Paper:

I'm excited to share our new work, VistaDream, which generates a 3D Gaussian field from a single-view image. The codes have already been released. Project page: (with interactive demos) Code: Paper:

Yuan Liu

79,610 Aufrufe • vor 1 Jahr

I'll present #NLF, our SOTA 3D human pose and shape estimator at #NeurIPS2024 today: poster at East Exhibit Hall A-C #1304 from 11:00! Project: Models out for both PyTorch and TF! Further details to come soon!

I'll present #NLF, our SOTA 3D human pose and shape estimator at #NeurIPS2024 today: poster at East Exhibit Hall A-C #1304 from 11:00! Project: Models out for both PyTorch and TF! Further details to come soon!

István Sárándi

79,896 Aufrufe • vor 1 Jahr

3D Object Tracking without Training Data? In our nature Machine Intelligence paper ( we recast 3D tracking as an inverse neural rendering task where we fit a scene graph to an image that best explains this image. The method generalizes to completely unseen datasets and is explainable. Project and Code: Fun collaboration between Princeton Computer Science and Torc Robotics, with Julian Ost and Tanushree Banerjee leading this project.

3D Object Tracking without Training Data? In our nature Machine Intelligence paper ( we recast 3D tracking as an inverse neural rendering task where we fit a scene graph to an image that best explains this image. The method generalizes to completely unseen datasets and is explainable. Project and Code: Fun collaboration between Princeton Computer Science and Torc Robotics, with Julian Ost and Tanushree Banerjee leading this project.

Felix Heide

27,858 Aufrufe • vor 1 Jahr

Extract accurate depth from your 3D gaussians! Fast, accurate, and easy to use. Code of RaDe-GS is released! project page: code: paper: No need to change your 3D GS representations, we propose a play-and-plug rasterizer to extract accurate depth and normal from 3D gaussians, leading to the possibility of high-quality geometry reconstruction. It's quite efficient and fast, achieving 0.69mm errors on DTU dataset in only 5 minutes. Very easy to adopt the rasterizer in your any 3dgs projects.

Extract accurate depth from your 3D gaussians! Fast, accurate, and easy to use. Code of RaDe-GS is released! project page: code: paper: No need to change your 3D GS representations, we propose a play-and-plug rasterizer to extract accurate depth and normal from 3D gaussians, leading to the possibility of high-quality geometry reconstruction. It's quite efficient and fast, achieving 0.69mm errors on DTU dataset in only 5 minutes. Very easy to adopt the rasterizer in your any 3dgs projects.

Xiao-Xiao Long

31,305 Aufrufe • vor 2 Jahren

THE ANCIENTS KNEW THAT WE CREATE OUR REALITIES. Our reality is not what we were told. Nothing is solid and we see binary code with our eyes- ones and zeroes. We live in a hologram. We project our reality. We are the projectors.

THE ANCIENTS KNEW THAT WE CREATE OUR REALITIES. Our reality is not what we were told. Nothing is solid and we see binary code with our eyes- ones and zeroes. We live in a hologram. We project our reality. We are the projectors.

Diana PATRIOTS ARE UNITED❤️🇺🇸❤️🙏

11,458 Aufrufe • vor 9 Monaten

Revisiting our past work on Code extensions, we took at look at the big brother; Visual Studio

Revisiting our past work on Code extensions, we took at look at the big brother; Visual Studio

MDSec

18,260 Aufrufe • vor 2 Monaten

Introducing VGGT (CVPR'25), a feedforward Transformer that directly infers all key 3D attributes from one, a few, or hundreds of images, in seconds! No expensive optimization needed, yet delivers SOTA results for: ✅ Camera Pose Estimation ✅ Multi-view Depth Estimation ✅ Dense Point Cloud Reconstruction ✅ Point Tracking Project Page: Code & Weights:

Introducing VGGT (CVPR'25), a feedforward Transformer that directly infers all key 3D attributes from one, a few, or hundreds of images, in seconds! No expensive optimization needed, yet delivers SOTA results for: ✅ Camera Pose Estimation ✅ Multi-view Depth Estimation ✅ Dense Point Cloud Reconstruction ✅ Point Tracking Project Page: Code & Weights:

Jianyuan

203,282 Aufrufe • vor 1 Jahr

We are live and kicking! For our latest project 1 dataset 100 visualizations, we challenged ourselves to turn one simple dataset into 100 insightful and visually appealing data visualizations. Explore all of the #dataviz and stories on

We are live and kicking! For our latest project 1 dataset 100 visualizations, we challenged ourselves to turn one simple dataset into 100 insightful and visually appealing data visualizations. Explore all of the #dataviz and stories on

ferdio

20,330 Aufrufe • vor 3 Jahren

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

16,350 Aufrufe • vor 2 Jahren

Today at #GoogleIO we've announced: 🤖 Project Astra: our vision for AI assistants 🎨 Imagen 3 & Veo: our new image and video gen models ♊ Gemini 1.5 Flash: a light-weight multimodal model with long context & 1.5 Pro with 2M tokens... "Tuesday" as we call it Google DeepMind

Today at #GoogleIO we've announced: 🤖 Project Astra: our vision for AI assistants 🎨 Imagen 3 & Veo: our new image and video gen models ♊ Gemini 1.5 Flash: a light-weight multimodal model with long context & 1.5 Pro with 2M tokens... "Tuesday" as we call it Google DeepMind

Alexandre Moufarek

111,189 Aufrufe • vor 2 Jahren

Excited to share our work, Know3D, which connects LLMs' reasoning ability and knowledge to 3D generative models. This increases the controllability and plausibility of unseen parts in the generated 3D shapes. Paper: Project page:

Excited to share our work, Know3D, which connects LLMs' reasoning ability and knowledge to 3D generative models. This increases the controllability and plausibility of unseen parts in the generated 3D shapes. Paper: Project page:

Yuan Liu

14,330 Aufrufe • vor 4 Monaten

Tomorrow I'll talk at the #ICRA workshop on "Enhancing human mobility" on recent 3D human estimation works from our group led by Gerard Pons-Moll. And I've released PyTorch training code for NLF, and more tools. Here's a preview of the even stronger upcoming DINOv2-ViTg-based NLF

Tomorrow I'll talk at the #ICRA workshop on "Enhancing human mobility" on recent 3D human estimation works from our group led by Gerard Pons-Moll. And I've released PyTorch training code for NLF, and more tools. Here's a preview of the even stronger upcoming DINOv2-ViTg-based NLF

István Sárándi

12,124 Aufrufe • vor 1 Jahr