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WalkTheDog Cross-Morphology Motion Alignment via Phase Manifolds We present a new approach for understanding the periodicity structure and semantics of motion datasets, independently of the morphology and skeletal structure of characters. Unlike existing methods using an overly sparse high-dimensional latent, we propose a phase manifold consisting of multiple closed...

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AK

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27,569 次观看 • 2 年前 •via X (Twitter)

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We are pleased to present a new lateral-view animation of the leg in motion. As the limb moves through extension and flexion, the active muscle groups light up. Now the challenge: can you name every muscle active in each phase? Most people miss at least one. #anatomystandard
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We are pleased to present a new lateral-view animation of the leg in motion. As the limb moves through extension and flexion, the active muscle groups light up. Now the challenge: can you name every muscle active in each phase? Most people miss at least one. #anatomystandard

Anatomy Standard

22,759 次观看 • 11 个月前

Meta releases VGGSfM Visual Geometry Grounded Deep Structure From Motion Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incremental manner by detecting and matching keypoints, registering images, triangulating 3D points, and conducting bundle adjustment. Recent research efforts have predominantly revolved around harnessing the power of deep learning techniques to enhance specific elements (e.g., keypoint matching), but are still based on the original, non-differentiable pipeline. Instead, we propose a new deep SfM pipeline VGGSfM, where each component is fully differentiable and thus can be trained in an end-to-end manner. To this end, we introduce new mechanisms and simplifications. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. Furthermore, we recover all cameras simultaneously based on the image and track features instead of gradually registering cameras. Finally, we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.
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Meta releases VGGSfM Visual Geometry Grounded Deep Structure From Motion Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incremental manner by detecting and matching keypoints, registering images, triangulating 3D points, and conducting bundle adjustment. Recent research efforts have predominantly revolved around harnessing the power of deep learning techniques to enhance specific elements (e.g., keypoint matching), but are still based on the original, non-differentiable pipeline. Instead, we propose a new deep SfM pipeline VGGSfM, where each component is fully differentiable and thus can be trained in an end-to-end manner. To this end, we introduce new mechanisms and simplifications. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. Furthermore, we recover all cameras simultaneously based on the image and track features instead of gradually registering cameras. Finally, we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.

AK

96,527 次观看 • 2 年前

The Thomas attractor is a mathematical concept in dynamical systems theory representing a type of chaotic space phase that exhibits cyclic symmetry. It was named after René Thomas, the mathematician who introduced it in 1979. The system is strictly related to the Lorenz attractor another famous chaotic evolutionary space manifold. The 3D Thomas attractor is represented by the combination of three differential equations where the symmetry is invariant under rotations of 120° (or 2π/3 radians) around the x y z axis which are the space coordinates, plus μ (mu) that is a parameter controlling the behavior. The attractor is characterized by a chaotic tendency and it’s sensitive to initial conditions, meaning that as μ increases, the motion undergoes period-doubling bifurcations leading to chaos. A good visualization of this spacetime manifold is a twisted, spiral-shaped structure with 3-fold symmetry where each spiral arm is symmetric with respect to the central axis, like the one displayed below. The chaotic evolution of this attractor demonstrates the geometric beauty and complexity of unpredictable systems.
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The Thomas attractor is a mathematical concept in dynamical systems theory representing a type of chaotic space phase that exhibits cyclic symmetry. It was named after René Thomas, the mathematician who introduced it in 1979. The system is strictly related to the Lorenz attractor another famous chaotic evolutionary space manifold. The 3D Thomas attractor is represented by the combination of three differential equations where the symmetry is invariant under rotations of 120° (or 2π/3 radians) around the x y z axis which are the space coordinates, plus μ (mu) that is a parameter controlling the behavior. The attractor is characterized by a chaotic tendency and it’s sensitive to initial conditions, meaning that as μ increases, the motion undergoes period-doubling bifurcations leading to chaos. A good visualization of this spacetime manifold is a twisted, spiral-shaped structure with 3-fold symmetry where each spiral arm is symmetric with respect to the central axis, like the one displayed below. The chaotic evolution of this attractor demonstrates the geometric beauty and complexity of unpredictable systems.

𝓜𝒂𝒖𝒓𝒊𝔃𝒊𝒐 𝗜𝒃𝛼

31,712 次观看 • 2 年前

A closed outline shaped like the Greek letter pi can be rebuilt as the path of a point carried by a chain of rotating circles, each circle matching one frequency component through its radius and phase, so that successive circles refine the match to the original curve.
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A closed outline shaped like the Greek letter pi can be rebuilt as the path of a point carried by a chain of rotating circles, each circle matching one frequency component through its radius and phase, so that successive circles refine the match to the original curve.

Matheorems

12,280 次观看 • 1 个月前

Abu Obeida in a previous speech: We bless the operations of the sincere brothers, Ansar Allah, in dear Yemen, and their announcement of a new phase of confrontation with the enemy and support for Gaza, despite the blind, unjust threats and alliances.
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Abu Obeida in a previous speech: We bless the operations of the sincere brothers, Ansar Allah, in dear Yemen, and their announcement of a new phase of confrontation with the enemy and support for Gaza, despite the blind, unjust threats and alliances.

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The Hidden Language of Diffusion Models paper page: tackle the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a pseudo-token that is a sparse weighted combination of tokens from the model's vocabulary, with the objective of reconstructing the images generated for the given concept. Applied over the state-of-the-art Stable Diffusion model, this decomposition reveals non-trivial and surprising structures in the representations of concepts. For example, we find that some concepts such as "a president" or "a composer" are dominated by specific instances (e.g., "Obama", "Biden") and their interpolations. Other concepts, such as "happiness" combine associated terms that can be concrete ("family", "laughter") or abstract ("friendship", "emotion"). In addition to peering into the inner workings of Stable Diffusion, our method also enables applications such as single-image decomposition to tokens, bias detection and mitigation, and semantic image manipulation
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The Hidden Language of Diffusion Models paper page: tackle the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a pseudo-token that is a sparse weighted combination of tokens from the model's vocabulary, with the objective of reconstructing the images generated for the given concept. Applied over the state-of-the-art Stable Diffusion model, this decomposition reveals non-trivial and surprising structures in the representations of concepts. For example, we find that some concepts such as "a president" or "a composer" are dominated by specific instances (e.g., "Obama", "Biden") and their interpolations. Other concepts, such as "happiness" combine associated terms that can be concrete ("family", "laughter") or abstract ("friendship", "emotion"). In addition to peering into the inner workings of Stable Diffusion, our method also enables applications such as single-image decomposition to tokens, bias detection and mitigation, and semantic image manipulation

AK

41,830 次观看 • 3 年前

Fighter jet pilot demonstrates centripetal force, a concept that Isaac Newton discovered between 1676 and 1677. However, the concept of centripetal force was not actually discovered in a single event or by a single individual but rather evolved over time through the works of multiple scientists and thinkers. Here is a brief history of the development of our understanding of centripetal force: Ancient Greece: The ancient Greeks, particularly philosophers such as Aristotle and later Archimedes, made observations and formulated theories about the motion of objects. However, they did not have a clear understanding of centripetal force as a distinct concept. Islamic Scholars: During the Islamic Golden Age, scholars such as Ibn Sina (Avicenna) and Ibn Bajjah (Avempace) made significant contributions to the understanding of motion. They discussed the idea of impetus, a precursor to the concept of centripetal force. Johannes Kepler: In the 17th century, Johannes Kepler, a German astronomer and mathematician, formulated his laws of planetary motion. Kepler's laws described the motion of planets around the sun and the concept of elliptical orbits. Although Kepler did not explicitly mention centripetal force, his work laid the foundation for later developments. Isaac Newton: In the late 17th century, Sir Isaac Newton, an English physicist and mathematician, published his groundbreaking work, "Mathematical Principles of Natural Philosophy" (also known as the Principia). In this work, Newton presented his laws of motion and the universal law of gravitation. Newton's laws, particularly the second law of motion, provided a mathematical understanding of centripetal force. He demonstrated that an object moving in a curved path experiences an inward force acting towards the center of the curve, which is now known as centripetal force. Further Developments: Following Newton's work, subsequent scientists, such as Jean le Rond d'Alembert and Daniel Bernoulli, expanded on the understanding of centripetal force in the context of mechanics and fluid dynamics. It's important to note that the discovery and understanding of centripetal force were gradual processes that involved the contributions of numerous individuals over centuries. The concept became firmly established with the development of Newtonian physics and its laws of motion.
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Fighter jet pilot demonstrates centripetal force, a concept that Isaac Newton discovered between 1676 and 1677. However, the concept of centripetal force was not actually discovered in a single event or by a single individual but rather evolved over time through the works of multiple scientists and thinkers. Here is a brief history of the development of our understanding of centripetal force: Ancient Greece: The ancient Greeks, particularly philosophers such as Aristotle and later Archimedes, made observations and formulated theories about the motion of objects. However, they did not have a clear understanding of centripetal force as a distinct concept. Islamic Scholars: During the Islamic Golden Age, scholars such as Ibn Sina (Avicenna) and Ibn Bajjah (Avempace) made significant contributions to the understanding of motion. They discussed the idea of impetus, a precursor to the concept of centripetal force. Johannes Kepler: In the 17th century, Johannes Kepler, a German astronomer and mathematician, formulated his laws of planetary motion. Kepler's laws described the motion of planets around the sun and the concept of elliptical orbits. Although Kepler did not explicitly mention centripetal force, his work laid the foundation for later developments. Isaac Newton: In the late 17th century, Sir Isaac Newton, an English physicist and mathematician, published his groundbreaking work, "Mathematical Principles of Natural Philosophy" (also known as the Principia). In this work, Newton presented his laws of motion and the universal law of gravitation. Newton's laws, particularly the second law of motion, provided a mathematical understanding of centripetal force. He demonstrated that an object moving in a curved path experiences an inward force acting towards the center of the curve, which is now known as centripetal force. Further Developments: Following Newton's work, subsequent scientists, such as Jean le Rond d'Alembert and Daniel Bernoulli, expanded on the understanding of centripetal force in the context of mechanics and fluid dynamics. It's important to note that the discovery and understanding of centripetal force were gradual processes that involved the contributions of numerous individuals over centuries. The concept became firmly established with the development of Newtonian physics and its laws of motion.

Historic Vids

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Our new website is now live! 🚀💻 With a sleek new design, improved user experience, and new and updated information showcasing the Polemos ecosystem, we are ready for the next phase of our journey. 👉 Explore it now:

Polemos

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Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.
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Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

MrNeRF

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Synk x Secret Network: A Game-Changing Partnership We’re proud to announce our official partnership with Secret Network and, more specifically, with the Secret Network Foundation (SNF) as part of their exclusive Builders Program. This collaboration marks a significant milestone for $Synk, as we join a select group of innovators working to shape the future of confidential computing. Through this partnership, we gain unparalleled access to the Decentralized Confidential Computing (DeCC) tools as well as joining the #DeCC Alliance, fostering deeper integrations and advancements in privacy-first technologies. As part of this collaboration, the Synk Firefox App will soon be positioned as the default private browser within their ecosystem, solidifying Synk’s role as a key player in the Web3 space. This is just the beginning. With Secret Network as a major partner, Synk is entering a transformative phase. Together, we will integrate groundbreaking applications into the Synk OS, leveraging the full potential of confidential computing to deliver unmatched tools and experiences to our users. The future of privacy and Web3 innovation is here—and it’s powered by Synk and Secret Network.
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Synk x Secret Network: A Game-Changing Partnership We’re proud to announce our official partnership with Secret Network and, more specifically, with the Secret Network Foundation (SNF) as part of their exclusive Builders Program. This collaboration marks a significant milestone for $Synk, as we join a select group of innovators working to shape the future of confidential computing. Through this partnership, we gain unparalleled access to the Decentralized Confidential Computing (DeCC) tools as well as joining the #DeCC Alliance, fostering deeper integrations and advancements in privacy-first technologies. As part of this collaboration, the Synk Firefox App will soon be positioned as the default private browser within their ecosystem, solidifying Synk’s role as a key player in the Web3 space. This is just the beginning. With Secret Network as a major partner, Synk is entering a transformative phase. Together, we will integrate groundbreaking applications into the Synk OS, leveraging the full potential of confidential computing to deliver unmatched tools and experiences to our users. The future of privacy and Web3 innovation is here—and it’s powered by Synk and Secret Network.

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bitcoin:native Same structure that marked the 2022 macro bottom is forming again. Price rejected from the upper trendline, printed a distribution phase, and is now working lower into the next major demand zone. We repeating similar bottoming process before the next sustained expansion. As long as the long-term ascending structure holds, the larger cycle remains intact.
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bitcoin:native Same structure that marked the 2022 macro bottom is forming again. Price rejected from the upper trendline, printed a distribution phase, and is now working lower into the next major demand zone. We repeating similar bottoming process before the next sustained expansion. As long as the long-term ascending structure holds, the larger cycle remains intact.

vex

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Depth video workflows have been getting a lot of attention lately, so I tested one myself. Combined with Seedance 2.0, it produced more natural motion-transfer results than using Kling Motion Control directly. Why use a depth video? 1. It removes the original character and scene details, reducing copyright and sensitive-content risks. 2. It preserves the original motion, timing, and spatial structure. This separates motion extraction from visual generation, allowing you to recreate the movement with better models and any reference character. We’ve also launched a free online tool that converts regular videos into depth videos—no local setup required: In the example below, we converted a dance video from Douyin into a depth video, then regenerated it with a reference character using Seedance 2.0. The original choreography and timing are preserved, while the lighting adapts naturally to the new character and scene.
0:14

Depth video workflows have been getting a lot of attention lately, so I tested one myself. Combined with Seedance 2.0, it produced more natural motion-transfer results than using Kling Motion Control directly. Why use a depth video? 1. It removes the original character and scene details, reducing copyright and sensitive-content risks. 2. It preserves the original motion, timing, and spatial structure. This separates motion extraction from visual generation, allowing you to recreate the movement with better models and any reference character. We’ve also launched a free online tool that converts regular videos into depth videos—no local setup required: In the example below, we converted a dance video from Douyin into a depth video, then regenerated it with a reference character using Seedance 2.0. The original choreography and timing are preserved, while the lighting adapts naturally to the new character and scene.

underwood

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Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models Contributions: • We introduce Diffuman4D, a novel diffusion model that generates spatio-temporally consistent and high-resolution (1024p) human videos from sparse-view video inputs. • We propose a sliding iterative denoising mechanism that enhances both the spatial and temporal consistency of generated long-term videos while maintaining efficient inference. • We design a human pose conditioning scheme to enhance the appearance quality and motion accuracy of generated human videos. • We plan to release our processed version of the DNA-Rendering dataset, which we believe will benefit future research in this area.
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Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models Contributions: • We introduce Diffuman4D, a novel diffusion model that generates spatio-temporally consistent and high-resolution (1024p) human videos from sparse-view video inputs. • We propose a sliding iterative denoising mechanism that enhances both the spatial and temporal consistency of generated long-term videos while maintaining efficient inference. • We design a human pose conditioning scheme to enhance the appearance quality and motion accuracy of generated human videos. • We plan to release our processed version of the DNA-Rendering dataset, which we believe will benefit future research in this area.

MrNeRF

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🫀 ALTS by adidas is here. ☄️ The new genesis NFT of adidas web3 - a new experience towards your identity and PFP. ❤️‍🔥 Burn your Phase 1 or 2 and mint into ALTS by adidas, the official and final "Phase 3" as we transition away from "Into the Metaverse". Instructions below👇
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🫀 ALTS by adidas is here. ☄️ The new genesis NFT of adidas web3 - a new experience towards your identity and PFP. ❤️‍🔥 Burn your Phase 1 or 2 and mint into ALTS by adidas, the official and final "Phase 3" as we transition away from "Into the Metaverse". Instructions below👇

ALTS by adidas

71,295 次观看 • 3 年前

What if I told you any shape can be broken down into circles? This visualization demonstrates the stunning power of the Fourier Transform. By using a series of rotating vectors—known as epicycles—we can reconstruct complex, continuous paths from simple circular motions. Each circle represents a specific frequency, phase, and amplitude. When these rotating arrows are added together, the resulting "tip" traces out the intricate outline you see here. It’s where pure mathematics meets digital art, proving that even the most complex designs are just a symphony of oscillations. Credit: mathswithmuza
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What if I told you any shape can be broken down into circles? This visualization demonstrates the stunning power of the Fourier Transform. By using a series of rotating vectors—known as epicycles—we can reconstruct complex, continuous paths from simple circular motions. Each circle represents a specific frequency, phase, and amplitude. When these rotating arrows are added together, the resulting "tip" traces out the intricate outline you see here. It’s where pure mathematics meets digital art, proving that even the most complex designs are just a symphony of oscillations. Credit: mathswithmuza

Mathematica

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You are never truly standing still. Even when everything feels motionless, you are carried by Earth’s rotation, swept along as it orbits the Sun, and pulled through the Milky Way along with billions of other stars. From a cosmic perspective, stillness is an illusion created by our limited human scale of perception, too small to notice the vast layers of motion we are part of. Taken together, these motions place us in constant transit through space at speeds far beyond anything we can feel. Yet there is no wind, no vibration, and no sense of rushing. We do not experience this journey because everything around us shares it, locked into the same immense and silent choreography of the universe. Credit: physicsj

Cosmos Archive

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This effect was captured within the time it takes to bake a single cake using edelkrone motion control devices with flawless position accuracy and with just one camera. This is a new generation effect that has never been seen before in the world. In this age where competition in content production is at its fiercest, we are proud to be able to offer such a feature to our customers for free to help them get ahead in the competition. As edelkrone, we are committed to pushing the boundaries of innovation and delivering the never seen before.
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This effect was captured within the time it takes to bake a single cake using edelkrone motion control devices with flawless position accuracy and with just one camera. This is a new generation effect that has never been seen before in the world. In this age where competition in content production is at its fiercest, we are proud to be able to offer such a feature to our customers for free to help them get ahead in the competition. As edelkrone, we are committed to pushing the boundaries of innovation and delivering the never seen before.

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