Загрузка видео...

Не удалось загрузить видео

На главную

Code and data are now online for CameraHMR, our state-of-the-art parametric 3D human pose and shape (HPS) estimation method that will appear at hashtag#3DV2025. There are 4 key contributions that make it so accurate and robust: 1. To get accurate 3D shape and pose as well as good alignment...

Michael_J_Black's profile picture

Michael Black

99,050 subscribers

21,696 просмотров • 1 год назад •via X (Twitter)

Наука и технологииОбразованиеИскусство
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 9

Фото профиля Jerin Philip
Jerin Philip1 год назад

Nice work, currently looking into using it. When is the release of CamSMPLify + HumanFoV + DenseKP expected? What's the hardware requirements to train this end-to-end? 4D-Humans mention 8 A100 GPUs for 7 days, does this require similar?

Фото профиля Michael Black
Michael Black1 год назад

All code will be out before 3DV takes place.

Фото профиля Digital Currency
Digital Currency2 лет назад

From 3D modeling to VR/AR development, our MSc in Metaverse program equips you with the technical skills to excel in the rapidly evolving digital world. Don't miss out—enroll today! #UNIC #MScMetaverse

Фото профиля Duke 'Burrito Haver' Zero
Duke 'Burrito Haver' Zero1 год назад

could you embed a sentiment layer keyed to facial expressions / body language?

Фото профиля Michael Black
Michael Black1 год назад

This method is just about pixels to parameters but, yes, relating these parameters to expressions and body language is something we are interested in. E.g., have a look at our work on generating moving people from audio:

Фото профиля lotsoflittleprojects
lotsoflittleprojects1 год назад

Will this model get folded into Meshcapade?

Фото профиля Michael Black
Michael Black1 год назад

The next Meshcapade release will have many goodies that go beyond CameraHMR. Coming soon!

Фото профиля Matt Jaynes
Matt Jaynes1 год назад

@camenduru Damn! Impressive!

Фото профиля JohnYue122333
JohnYue1223331 год назад

MPI, awesome!

Похожие видео

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.
0:30

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

27,714 просмотров • 1 год назад

WHAM defines the new state of the art in 3D human pose estimation from video. By a large margin. It’s fast, accurate, and it computes human pose in world coordinates. It’s also the first video-based method to be more accurate than single-image methods. 1/8
0:30

WHAM defines the new state of the art in 3D human pose estimation from video. By a large margin. It’s fast, accurate, and it computes human pose in world coordinates. It’s also the first video-based method to be more accurate than single-image methods. 1/8

Michael Black

118,503 просмотров • 2 лет назад

One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization paper page: Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world. Many existing methods solve this problem by optimizing a neural radiance field under the guidance of 2D diffusion models but suffer from lengthy optimization time, 3D inconsistency results, and poor geometry. In this work, we propose a novel method that takes a single image of any object as input and generates a full 360-degree 3D textured mesh in a single feed-forward pass. Given a single image, we first use a view-conditioned 2D diffusion model, Zero123, to generate multi-view images for the input view, and then aim to lift them up to 3D space. Since traditional reconstruction methods struggle with inconsistent multi-view predictions, we build our 3D reconstruction module upon an SDF-based generalizable neural surface reconstruction method and propose several critical training strategies to enable the reconstruction of 360-degree meshes. Without costly optimizations, our method reconstructs 3D shapes in significantly less time than existing methods. Moreover, our method favors better geometry, generates more 3D consistent results, and adheres more closely to the input image. We evaluate our approach on both synthetic data and in-the-wild images and demonstrate its superiority in terms of both mesh quality and runtime. In addition, our approach can seamlessly support the text-to-3D task by integrating with off-the-shelf text-to-image diffusion models.
0:43

One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization paper page: Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world. Many existing methods solve this problem by optimizing a neural radiance field under the guidance of 2D diffusion models but suffer from lengthy optimization time, 3D inconsistency results, and poor geometry. In this work, we propose a novel method that takes a single image of any object as input and generates a full 360-degree 3D textured mesh in a single feed-forward pass. Given a single image, we first use a view-conditioned 2D diffusion model, Zero123, to generate multi-view images for the input view, and then aim to lift them up to 3D space. Since traditional reconstruction methods struggle with inconsistent multi-view predictions, we build our 3D reconstruction module upon an SDF-based generalizable neural surface reconstruction method and propose several critical training strategies to enable the reconstruction of 360-degree meshes. Without costly optimizations, our method reconstructs 3D shapes in significantly less time than existing methods. Moreover, our method favors better geometry, generates more 3D consistent results, and adheres more closely to the input image. We evaluate our approach on both synthetic data and in-the-wild images and demonstrate its superiority in terms of both mesh quality and runtime. In addition, our approach can seamlessly support the text-to-3D task by integrating with off-the-shelf text-to-image diffusion models.

AK

179,061 просмотров • 3 лет назад

Physics-based Motion Retargeting from Sparse Inputs paper page: Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. Another challenge is that an avatar might have a different skeleton structure than a human and the mapping between them is unclear. In this work we address both of these challenges. We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies. Our method uses reinforcement learning to train a policy to control characters in a physics simulator. We only require human motion capture data for training, without relying on artist-generated animations for each avatar. This allows us to use large motion capture datasets to train general policies that can track unseen users from real and sparse data in real-time. We demonstrate the feasibility of our approach on three characters with different skeleton structure: a dinosaur, a mouse-like creature and a human. We show that the avatar poses often match the user surprisingly well, despite having no sensor information of the lower body available. We discuss and ablate the important components in our framework, specifically the kinematic retargeting step, the imitation, contact and action reward as well as our asymmetric actor-critic observations. We further explore the robustness of our method in a variety of settings including unbalancing, dancing and sports motions.
7:12

Physics-based Motion Retargeting from Sparse Inputs paper page: Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. Another challenge is that an avatar might have a different skeleton structure than a human and the mapping between them is unclear. In this work we address both of these challenges. We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies. Our method uses reinforcement learning to train a policy to control characters in a physics simulator. We only require human motion capture data for training, without relying on artist-generated animations for each avatar. This allows us to use large motion capture datasets to train general policies that can track unseen users from real and sparse data in real-time. We demonstrate the feasibility of our approach on three characters with different skeleton structure: a dinosaur, a mouse-like creature and a human. We show that the avatar poses often match the user surprisingly well, despite having no sensor information of the lower body available. We discuss and ablate the important components in our framework, specifically the kinematic retargeting step, the imitation, contact and action reward as well as our asymmetric actor-critic observations. We further explore the robustness of our method in a variety of settings including unbalancing, dancing and sports motions.

AK

106,527 просмотров • 3 лет назад

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 2D images into vivid, accurate reconstructions. 🔗 Learn more:
0:50

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 2D images into vivid, accurate reconstructions. 🔗 Learn more:

AI at Meta

861,882 просмотров • 11 месяцев назад

Champ Controllable and Consistent Human Image Animation with 3D Parametric Guidance In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion
2:10

Champ Controllable and Consistent Human Image Animation with 3D Parametric Guidance In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion

AK

194,605 просмотров • 2 лет назад

Drivable 3D Gaussian Avatars paper page: present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable model for human bodies rendered with Gaussian splats. Current photorealistic drivable avatars require either accurate 3D registrations during training, dense input images during testing, or both. The ones based on neural radiance fields also tend to be prohibitively slow for telepresence applications. This work uses the recently presented 3D Gaussian Splatting (3DGS) technique to render realistic humans at real-time framerates, using dense calibrated multi-view videos as input. To deform those primitives, we depart from the commonly used point deformation method of linear blend skinning (LBS) and use a classic volumetric deformation method: cage deformations. Given their smaller size, we drive these deformations with joint angles and keypoints, which are more suitable for communication applications. Our experiments on nine subjects with varied body shapes, clothes, and motions obtain higher-quality results than state-of-the-art methods when using the same training and test data.
6:20

Drivable 3D Gaussian Avatars paper page: present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable model for human bodies rendered with Gaussian splats. Current photorealistic drivable avatars require either accurate 3D registrations during training, dense input images during testing, or both. The ones based on neural radiance fields also tend to be prohibitively slow for telepresence applications. This work uses the recently presented 3D Gaussian Splatting (3DGS) technique to render realistic humans at real-time framerates, using dense calibrated multi-view videos as input. To deform those primitives, we depart from the commonly used point deformation method of linear blend skinning (LBS) and use a classic volumetric deformation method: cage deformations. Given their smaller size, we drive these deformations with joint angles and keypoints, which are more suitable for communication applications. Our experiments on nine subjects with varied body shapes, clothes, and motions obtain higher-quality results than state-of-the-art methods when using the same training and test data.

AK

327,442 просмотров • 2 лет назад

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

57,588 просмотров • 1 год назад

fal head of engineering batuhan the fal guy explains why they’ll never pre-train a model and why compute may be the real moat in AI: "We are never going to pre-train a model. That is not in the cards for us. fal is a generative media cloud, and wherever we see gaps, we are okay to adjust and evolve our form to fit in there." "Today the gaps are not in pre-training. There are multiple companies rushing to pre-train the best image and video models. There is a big gap in real-time video generation, which we think we cracked with H3 Max." "I don't think anyone in the world has enough inference compute today. We have been very aggressive building and procuring compute, anywhere from building our own data centers to renting as many GPUs as possible." "The moat is not the software that you build, not the training that you do, it's essentially your focus to be always one step ahead. As long as you're one step ahead, you're able to capture more value than anyone else." fal
2:30

fal head of engineering batuhan the fal guy explains why they’ll never pre-train a model and why compute may be the real moat in AI: "We are never going to pre-train a model. That is not in the cards for us. fal is a generative media cloud, and wherever we see gaps, we are okay to adjust and evolve our form to fit in there." "Today the gaps are not in pre-training. There are multiple companies rushing to pre-train the best image and video models. There is a big gap in real-time video generation, which we think we cracked with H3 Max." "I don't think anyone in the world has enough inference compute today. We have been very aggressive building and procuring compute, anywhere from building our own data centers to renting as many GPUs as possible." "The moat is not the software that you build, not the training that you do, it's essentially your focus to be always one step ahead. As long as you're one step ahead, you're able to capture more value than anyone else." fal

MTS

23,792 просмотров • 1 месяц назад

We are thrilled to announce a new feature on - Image as Pose! 🎉 This allows you to upload an image of a person as an initial image, and generate other images of people in the same pose. Get the same character in multiple poses and angles consistently🔥
0:50

We are thrilled to announce a new feature on - Image as Pose! 🎉 This allows you to upload an image of a person as an initial image, and generate other images of people in the same pose. Get the same character in multiple poses and angles consistently🔥

Leonardo.Ai

391,497 просмотров • 3 лет назад

“The problems we are facing may seem insurmountable, but there are always solutions to be found and applied. It is a question of mindset. We cannot remain stuck in the past, focused on differences and grievances. We must look forward, and shape the future we want for our people. That is how we really wish to see the invitation to meet here, as an opportunity to collaborate and find solutions, and make the investments required to get results. Time is not on our side, but together, we are much stronger.” President Kagame | Saudi-African Summit.
3:59

“The problems we are facing may seem insurmountable, but there are always solutions to be found and applied. It is a question of mindset. We cannot remain stuck in the past, focused on differences and grievances. We must look forward, and shape the future we want for our people. That is how we really wish to see the invitation to meet here, as an opportunity to collaborate and find solutions, and make the investments required to get results. Time is not on our side, but together, we are much stronger.” President Kagame | Saudi-African Summit.

Presidency | Rwanda

104,641 просмотров • 3 лет назад

Do you know how we simulate electromagnetic radiation around real systems like antennas, aircraft, and waveguides? We break the geometry into a mesh, a collection of small elements that lets the solver compute Maxwell’s equations locally instead of attacking the whole shape at once. This makes the simulations practical, fast, and accurate enough to use in real engineering.
0:29

Do you know how we simulate electromagnetic radiation around real systems like antennas, aircraft, and waveguides? We break the geometry into a mesh, a collection of small elements that lets the solver compute Maxwell’s equations locally instead of attacking the whole shape at once. This makes the simulations practical, fast, and accurate enough to use in real engineering.

Mathelirium

73,166 просмотров • 6 месяцев назад

Mike Dunleavy Jr. says the Warriors are not in a situation to fix everything in free agency but they’re looking to put together a good team going into training camp: “I think there’s where you want to be in September when training camp starts, and there’s where you want to be in the spring as you try and compete for the postseason and get in the postseason. We have a couple transaction cycles here in the summer and then once again in February where we feel like we can build and maximize our team. And so I think the biggest thing is to put together a good team going into camp. And as I mentioned before, a lot of the improvement for us is going to have to come from within our habits, taking care of the ball, doing the little things. Get that knocked out, you get on your way to having a good season. We need to stay healthy and then get those guys (Jimmy & Moses) back, and then we’ll see at the trade deadline and we’ll evaluate everything. But this is not a fix-everything over the next couple weeks in free agency. We’re not in that situation based on some stuff we have going on, but we can get a lot better. We can have a good start to the season, and we can be in really good shape to make a run. But we got to get off to a good start.” (via NBC Sports Bay Area & CA with Bonta Hill and Monte Poole)
1:13

Mike Dunleavy Jr. says the Warriors are not in a situation to fix everything in free agency but they’re looking to put together a good team going into training camp: “I think there’s where you want to be in September when training camp starts, and there’s where you want to be in the spring as you try and compete for the postseason and get in the postseason. We have a couple transaction cycles here in the summer and then once again in February where we feel like we can build and maximize our team. And so I think the biggest thing is to put together a good team going into camp. And as I mentioned before, a lot of the improvement for us is going to have to come from within our habits, taking care of the ball, doing the little things. Get that knocked out, you get on your way to having a good season. We need to stay healthy and then get those guys (Jimmy & Moses) back, and then we’ll see at the trade deadline and we’ll evaluate everything. But this is not a fix-everything over the next couple weeks in free agency. We’re not in that situation based on some stuff we have going on, but we can get a lot better. We can have a good start to the season, and we can be in really good shape to make a run. But we got to get off to a good start.” (via NBC Sports Bay Area & CA with Bonta Hill and Monte Poole)

aly ✶

54,924 просмотров • 3 месяцев назад

Hot Chips 3D DRAM session: D-Matrix says its 3D DRAM moves data 6-to-7x more efficiently than HBM4 on measured silicon "On comparing with HBM4, like we heard in the morning, we can get about 2.4 picojoules per bit energy efficiency on HBM4 of moving data into the base die." "There's obviously additional energy burned to move the data sideways into the GPU, which is not counted here." "But using this face-to-face stacking with 3D DRAM, we are able to reduce that energy to 0.37 picojoules per bit. This is a measured number." "And so this gives us, you know, 6 to 7X energy efficiency." "So for the same amount of power you can drive the bandwidth up, and so we were able to drive the bandwidth up here to 100 terabytes per second." "And in this construction it's a single-high stack, and like I showed, even with 32 gigabytes of memory capacity we are able to solve SOTA models in a scale-up network." "So no bits are wasted."
1:03

Hot Chips 3D DRAM session: D-Matrix says its 3D DRAM moves data 6-to-7x more efficiently than HBM4 on measured silicon "On comparing with HBM4, like we heard in the morning, we can get about 2.4 picojoules per bit energy efficiency on HBM4 of moving data into the base die." "There's obviously additional energy burned to move the data sideways into the GPU, which is not counted here." "But using this face-to-face stacking with 3D DRAM, we are able to reduce that energy to 0.37 picojoules per bit. This is a measured number." "And so this gives us, you know, 6 to 7X energy efficiency." "So for the same amount of power you can drive the bandwidth up, and so we were able to drive the bandwidth up here to 100 terabytes per second." "And in this construction it's a single-high stack, and like I showed, even with 32 gigabytes of memory capacity we are able to solve SOTA models in a scale-up network." "So no bits are wasted."

Fireside Alpha

24,389 просмотров • 2 месяцев назад

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.
0:33

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

122,567 просмотров • 3 лет назад

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.
0:35

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 просмотров • 1 год назад

Google announces PALP Prompt Aligned Personalization of Text-to-Image Models paper page: Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want the resulting image to encompass a specific location, style, ambiance, and more. Existing personalization methods may compromise personalization ability or the alignment to complex textual prompts. This trade-off can impede the fulfillment of user prompts and subject fidelity. We propose a new approach focusing on personalization methods for a single prompt to address this issue. We term our approach prompt-aligned personalization. While this may seem restrictive, our method excels in improving text alignment, enabling the creation of images with complex and intricate prompts, which may pose a challenge for current techniques. In particular, our method keeps the personalized model aligned with a target prompt using an additional score distillation sampling term. We demonstrate the versatility of our method in multi- and single-shot settings and further show that it can compose multiple subjects or use inspiration from reference images, such as artworks. We compare our approach quantitatively and qualitatively with existing baselines and state-of-the-art techniques.
1:16

Google announces PALP Prompt Aligned Personalization of Text-to-Image Models paper page: Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want the resulting image to encompass a specific location, style, ambiance, and more. Existing personalization methods may compromise personalization ability or the alignment to complex textual prompts. This trade-off can impede the fulfillment of user prompts and subject fidelity. We propose a new approach focusing on personalization methods for a single prompt to address this issue. We term our approach prompt-aligned personalization. While this may seem restrictive, our method excels in improving text alignment, enabling the creation of images with complex and intricate prompts, which may pose a challenge for current techniques. In particular, our method keeps the personalized model aligned with a target prompt using an additional score distillation sampling term. We demonstrate the versatility of our method in multi- and single-shot settings and further show that it can compose multiple subjects or use inspiration from reference images, such as artworks. We compare our approach quantitatively and qualitatively with existing baselines and state-of-the-art techniques.

AK

90,212 просмотров • 2 лет назад

Muslims are now specifically targeting California to run for office and take over the government “We want to make sure we activate California Muslims in so we can shape the rest of the country, because we can shape California. We need you to run for office” “Muslims. We will build a network of activists at every masjid, every mosque, to ensure that every eligible Muslim, every eligible person is registered to vote, that every elected official at every level engages with the Muslim community, that more American Muslims run for office — And we want to make sure that we activate California Muslims in so we can shape the rest of the country, because we can shape California. We need you to run for office, and I want to salute the dozens and dozens of American Muslims who were, who had the courage and the commitment and the resolve to run for office. Dozens of them won, others did not. They will win next time. What Allah promises is guaranteed 100%.” “How many phone calls have you made? How many protests have you tried to attend? Have you met or called your elected officials, your member of Congress? Have you gone to call the White House? Have you joined the efforts, the political efforts happening in every city to organize the Muslim votes as we deal with elections? Ask yourself, where am I in the equation? Because until there is a critical mass, Allah is withholding what he has promised us, because we haven't fulfilled our part of the deal” “Mahmoud Saifi in Redlands. Dr. Asif Mahmud, running for Congress. Fatima Eqbal Zubair in LA for Assembly, and many others. But as important as running is, it is equally important to build the power of the grassroots at the grassroots level. This year, we've established the Muslim Community Action Network, we know it as MCAN, which aims to train activists as community organizers who can inform and mobilize their local community, set up candidate forums, engage with local politicians, and advocate for local, state, and national issues of importance to our community.“
2:59

Muslims are now specifically targeting California to run for office and take over the government “We want to make sure we activate California Muslims in so we can shape the rest of the country, because we can shape California. We need you to run for office” “Muslims. We will build a network of activists at every masjid, every mosque, to ensure that every eligible Muslim, every eligible person is registered to vote, that every elected official at every level engages with the Muslim community, that more American Muslims run for office — And we want to make sure that we activate California Muslims in so we can shape the rest of the country, because we can shape California. We need you to run for office, and I want to salute the dozens and dozens of American Muslims who were, who had the courage and the commitment and the resolve to run for office. Dozens of them won, others did not. They will win next time. What Allah promises is guaranteed 100%.” “How many phone calls have you made? How many protests have you tried to attend? Have you met or called your elected officials, your member of Congress? Have you gone to call the White House? Have you joined the efforts, the political efforts happening in every city to organize the Muslim votes as we deal with elections? Ask yourself, where am I in the equation? Because until there is a critical mass, Allah is withholding what he has promised us, because we haven't fulfilled our part of the deal” “Mahmoud Saifi in Redlands. Dr. Asif Mahmud, running for Congress. Fatima Eqbal Zubair in LA for Assembly, and many others. But as important as running is, it is equally important to build the power of the grassroots at the grassroots level. This year, we've established the Muslim Community Action Network, we know it as MCAN, which aims to train activists as community organizers who can inform and mobilize their local community, set up candidate forums, engage with local politicians, and advocate for local, state, and national issues of importance to our community.“

Wall Street Apes

339,304 просмотров • 11 месяцев назад