Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Check out our #PAMI paper with code "Dense Continuous-Time Optical Flow from Event Cameras," where we show how to regress *continuous-time* trajectories of every pixel from event cameras alone or events plus frames! The key idea is to iteratively estimate per-pixel polynomials using a recurrent lookup and update scheme....

davsca1's profile picture

Davide Scaramuzza

17,690 subscribers

12,637 görüntüleme • 2 yıl önce •via X (Twitter)

#PAMI
LIVE

Anya Rossi• Live Now

Private livecam show

1 Yorum

David Jin profil fotoğrafı
David Jin2 yıl önce

Sorry to ask a question before reading the paper. May I ask if I need to train for a different data set or it will work with your pretrained checkpoints?

Benzer Videolar

We are excited to share our #CORL2024 paper on learning quadrotor obstacle avoidance from the visual stream of a single #eventcamera! Trained entirely in simulation! We demonstrate obstacle avoidance both in the dark and in a forest up to 5m/s. PDF: Video: Project page: Event cameras are sensors that output per-pixel-level intensity changes at microsecond latency resolution; they feature nearly zero motion blur and high dynamic range but produce a very large volume of events under significant ego-motion and further lack a high-fidelity continuous-time sensor model in simulation, making direct #sim2real transfer not possible. By leveraging depth prediction as a pretext task, we pre-train a reactive obstacle avoidance policy with “approximated” simulated events and then fine-tune the perception component with limited events-and-depth real-world data. This technique bridges the sim2real gap for #eventcameras! As at the current state, there is no continuous-time sensor model for event cameras, we hope that this work can finally spur future research leveraging simulation for training event-vision-based policies to create faster, agile robots! Kudos to Anish Bhattacharya, @marcocannic, Vijay Kumar Nikolai Matni UZH Science University of Zurich UZH Space Hub UZH IfI European Research Council (ERC) GRASP Laboratory Penn Engineering
1:25

We are excited to share our #CORL2024 paper on learning quadrotor obstacle avoidance from the visual stream of a single #eventcamera! Trained entirely in simulation! We demonstrate obstacle avoidance both in the dark and in a forest up to 5m/s. PDF: Video: Project page: Event cameras are sensors that output per-pixel-level intensity changes at microsecond latency resolution; they feature nearly zero motion blur and high dynamic range but produce a very large volume of events under significant ego-motion and further lack a high-fidelity continuous-time sensor model in simulation, making direct #sim2real transfer not possible. By leveraging depth prediction as a pretext task, we pre-train a reactive obstacle avoidance policy with “approximated” simulated events and then fine-tune the perception component with limited events-and-depth real-world data. This technique bridges the sim2real gap for #eventcameras! As at the current state, there is no continuous-time sensor model for event cameras, we hope that this work can finally spur future research leveraging simulation for training event-vision-based policies to create faster, agile robots! Kudos to Anish Bhattacharya, @marcocannic, Vijay Kumar Nikolai Matni UZH Science University of Zurich UZH Space Hub UZH IfI European Research Council (ERC) GRASP Laboratory Penn Engineering

Davide Scaramuzza

17,163 görüntüleme • 1 yıl önce

🚀 New Paper: Pixel Reasoner 🧠🖼️ How can Vision-Language Models (VLMs) perform chain-of-thought reasoning within the image itself? We introduce Pixel Reasoner, the first open-source framework that enables VLMs to “think in pixel space” through curiosity-driven reinforcement learning. Current VLMs reason only in text — even when grounded in rich images or videos, their logical steps are verbalized in natural language. This restricts their ability to interrogate visual evidence and demonstrate how conclusions are drawn. 🔍 So we ask: What if we could make VLMs "show their work" by reasoning directly in the pixel space? Inspired by GPT-o3’s "think-in-image" ability, we propose a framework where VLMs use interactive visual operations — zoom, select-frame, highlight — to reason through complex visual inputs. To do this, we design a two-stage training process: Instruction tuning with synthesized visual reasoning traces. Reinforcement learning with curiosity-driven reward to balance exploration between pixel and text reasoning ✨ With this, Pixel Reasoner achieves near-SoTA performance on many information-rich multimodal benchmarks: 📊 84% on InfographicsVQA 🧠 84% on V* benchmark 🧩 74% on TallyQA-Complex It also achieves strong accuracy of 68% on MVBench (a video benchmark). Website: Paper: Code: Demo: (coming soon)
0:43

🚀 New Paper: Pixel Reasoner 🧠🖼️ How can Vision-Language Models (VLMs) perform chain-of-thought reasoning within the image itself? We introduce Pixel Reasoner, the first open-source framework that enables VLMs to “think in pixel space” through curiosity-driven reinforcement learning. Current VLMs reason only in text — even when grounded in rich images or videos, their logical steps are verbalized in natural language. This restricts their ability to interrogate visual evidence and demonstrate how conclusions are drawn. 🔍 So we ask: What if we could make VLMs "show their work" by reasoning directly in the pixel space? Inspired by GPT-o3’s "think-in-image" ability, we propose a framework where VLMs use interactive visual operations — zoom, select-frame, highlight — to reason through complex visual inputs. To do this, we design a two-stage training process: Instruction tuning with synthesized visual reasoning traces. Reinforcement learning with curiosity-driven reward to balance exploration between pixel and text reasoning ✨ With this, Pixel Reasoner achieves near-SoTA performance on many information-rich multimodal benchmarks: 📊 84% on InfographicsVQA 🧠 84% on V* benchmark 🧩 74% on TallyQA-Complex It also achieves strong accuracy of 68% on MVBench (a video benchmark). Website: Paper: Code: Demo: (coming soon)

Wenhu Chen

82,829 görüntüleme • 1 yıl önce

Get Ready to Earn $PIXEL in the Forgotten Runiverse! ✨ Use Quanta to claim $PIXEL ✨ Use $PIXEL in-game to purchase Mana and Boosts ✨ Get exclusive Pixels themed rewards Start early and earn Quanta now! 🏃 The Pixels Partner Token Event is coming soon.
0:21

Get Ready to Earn $PIXEL in the Forgotten Runiverse! ✨ Use Quanta to claim $PIXEL ✨ Use $PIXEL in-game to purchase Mana and Boosts ✨ Get exclusive Pixels themed rewards Start early and earn Quanta now! 🏃 The Pixels Partner Token Event is coming soon.

The Forgotten Runiverse

97,862 görüntüleme • 1 yıl önce

4D LangSplat: 4D Language Gaussian Splatting via Multimodal Large Language Models Contributions: • We introduce 4D LangSplat for open-vocabulary 4D spatial-temporal queries. To the best of our knowledge, we are the first to construct 4D language fields with object textual captions generated by MLLMs. • To model smooth transitions across states for objects in 4D scenes, we propose a status deformable network to capture continuous temporal changes. • Experiential results show that our method attains state-of-the-art performance for both time-agnostic and time-sensitive open-vocabulary queries.
0:29

4D LangSplat: 4D Language Gaussian Splatting via Multimodal Large Language Models Contributions: • We introduce 4D LangSplat for open-vocabulary 4D spatial-temporal queries. To the best of our knowledge, we are the first to construct 4D language fields with object textual captions generated by MLLMs. • To model smooth transitions across states for objects in 4D scenes, we propose a status deformable network to capture continuous temporal changes. • Experiential results show that our method attains state-of-the-art performance for both time-agnostic and time-sensitive open-vocabulary queries.

MrNeRF

10,953 görüntüleme • 1 yıl önce

Fast View Synthesis of Casual Videos paper page: Novel view synthesis from an in-the-wild video is difficult due to challenges like scene dynamics and lack of parallax. While existing methods have shown promising results with implicit neural radiance fields, they are slow to train and render. This paper revisits explicit video representations to synthesize high-quality novel views from a monocular video efficiently. We treat static and dynamic video content separately. Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video. Our plane-based scene representation is augmented with spherical harmonics and displacement maps to capture view-dependent effects and model non-planar complex surface geometry. We opt to represent the dynamic content as per-frame point clouds for efficiency. While such representations are inconsistency-prone, minor temporal inconsistencies are perceptually masked due to motion. We develop a method to quickly estimate such a hybrid video representation and render novel views in real time. Our experiments show that our method can render high-quality novel views from an in-the-wild video with comparable quality to state-of-the-art methods while being 100x faster in training and enabling real-time rendering.
0:24

Fast View Synthesis of Casual Videos paper page: Novel view synthesis from an in-the-wild video is difficult due to challenges like scene dynamics and lack of parallax. While existing methods have shown promising results with implicit neural radiance fields, they are slow to train and render. This paper revisits explicit video representations to synthesize high-quality novel views from a monocular video efficiently. We treat static and dynamic video content separately. Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video. Our plane-based scene representation is augmented with spherical harmonics and displacement maps to capture view-dependent effects and model non-planar complex surface geometry. We opt to represent the dynamic content as per-frame point clouds for efficiency. While such representations are inconsistency-prone, minor temporal inconsistencies are perceptually masked due to motion. We develop a method to quickly estimate such a hybrid video representation and render novel views in real time. Our experiments show that our method can render high-quality novel views from an in-the-wild video with comparable quality to state-of-the-art methods while being 100x faster in training and enabling real-time rendering.

AK

20,651 görüntüleme • 2 yıl önce

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

122,525 görüntüleme • 2 yıl önce

Prepare to embark on a cosmic adventure with Pixel Lime, the latest sensation in the universe of our Delta 9 gummies! Influenced by PixelxKitten 🍉 She/ They, Pixel Lime invites you to explore the universe one delicious gummy at a time!
0:20

Prepare to embark on a cosmic adventure with Pixel Lime, the latest sensation in the universe of our Delta 9 gummies! Influenced by PixelxKitten 🍉 She/ They, Pixel Lime invites you to explore the universe one delicious gummy at a time!

Space Panda

14,738 görüntüleme • 1 yıl önce

OPTICAL AIDS CAUSE 28 DAY MONTHS? NCS does not recommend that we use optical aids for a moonsighting system in Britain for many reasons. The primary reason is that we are seeking a unified solution, and unity is in the sunnah - local, naked eye sightings. Not everybody believes that optical aids are permissible, but naked eye sightings are wholly uncontroversial. Another issue is that optical aids would lead to more and more examples of the moon being “seen” a day early, and out of phase, on the 28th of a lunar month. This has ALREADY HAPPENED to those who use optical aids in the UK - yet has never happened to naked eye moonsighters for over 100 months. Whilst there are procedures from the sunnah of what to do if you were to see a moon on the 28th, we cannot really justify asking people to end their fast a day early on the 28th because of a moon which is invisible to the eye and only visible through optical aids. Hence our stance. Remember - we ourselves use optical aids - and had them on our livestream. We are experienced in them, and it is from our experience we share with you that we have found that it would leave to problems. In any case why are you in a rush to end Ramadan, using technology, and away from the sunnah?
1:02

OPTICAL AIDS CAUSE 28 DAY MONTHS? NCS does not recommend that we use optical aids for a moonsighting system in Britain for many reasons. The primary reason is that we are seeking a unified solution, and unity is in the sunnah - local, naked eye sightings. Not everybody believes that optical aids are permissible, but naked eye sightings are wholly uncontroversial. Another issue is that optical aids would lead to more and more examples of the moon being “seen” a day early, and out of phase, on the 28th of a lunar month. This has ALREADY HAPPENED to those who use optical aids in the UK - yet has never happened to naked eye moonsighters for over 100 months. Whilst there are procedures from the sunnah of what to do if you were to see a moon on the 28th, we cannot really justify asking people to end their fast a day early on the 28th because of a moon which is invisible to the eye and only visible through optical aids. Hence our stance. Remember - we ourselves use optical aids - and had them on our livestream. We are experienced in them, and it is from our experience we share with you that we have found that it would leave to problems. In any case why are you in a rush to end Ramadan, using technology, and away from the sunnah?

New Crescent Society

18,343 görüntüleme • 2 ay önce

[SIGGRAPH 2025] Photoreal Scene Reconstruction from an Egocentric Device Contributions: 1. We address the importance of employing visual-inertial bundle adjustment (VIBA) that accounts for the rolling-shutter behavior of the RGB camera. This provides a continuous camera trajectory to model pixel movement in neural reconstruction. Our experiments demonstrate that using VIBA consistently improves the novel view quality in Gaussian Splatting by +1 dB in PSNR. 2. We introduce a rasterization-based image formulation pipeline that addresses common artifacts in physical image formation, including rolling shutter, lens shading, exposure, and gain compensation. Our approach is distinct in that we represent image poses as posed pixel arrays sampled from a continuous trajectory, rather than assigning a single camera pose per image, and preserve the merit of Gaussian rasterization. Unlike existing methods that require ray-tracing Gaussians, e.g., [Moenne-Loccoz et al. 2024], our formulation is applicable to general-purpose rasterization-based Gaussian splatting. When applied to 3D Gaussian Splatting (3DGS) [Kerbl et al. 2023], our approach can further enhance reconstruction quality by +1 dB. We outperform existing baselines and demonstrate a substantial quality improvement in handling complex scenes observed by egocentric devices. 3. To reduce the effect of blur from rapid head motion in darker indoor scenes, we propose a strategy of deliberately underexposing input videos during capture, inspired by HDR+ [Hasinoff et al. 2016]. We demonstrate that we can reconstruct high-quality, noise-free scene radiance from noisy, dim input videos, and further render sharp, blur-free videos at a higher dynamic range.
1:00

[SIGGRAPH 2025] Photoreal Scene Reconstruction from an Egocentric Device Contributions: 1. We address the importance of employing visual-inertial bundle adjustment (VIBA) that accounts for the rolling-shutter behavior of the RGB camera. This provides a continuous camera trajectory to model pixel movement in neural reconstruction. Our experiments demonstrate that using VIBA consistently improves the novel view quality in Gaussian Splatting by +1 dB in PSNR. 2. We introduce a rasterization-based image formulation pipeline that addresses common artifacts in physical image formation, including rolling shutter, lens shading, exposure, and gain compensation. Our approach is distinct in that we represent image poses as posed pixel arrays sampled from a continuous trajectory, rather than assigning a single camera pose per image, and preserve the merit of Gaussian rasterization. Unlike existing methods that require ray-tracing Gaussians, e.g., [Moenne-Loccoz et al. 2024], our formulation is applicable to general-purpose rasterization-based Gaussian splatting. When applied to 3D Gaussian Splatting (3DGS) [Kerbl et al. 2023], our approach can further enhance reconstruction quality by +1 dB. We outperform existing baselines and demonstrate a substantial quality improvement in handling complex scenes observed by egocentric devices. 3. To reduce the effect of blur from rapid head motion in darker indoor scenes, we propose a strategy of deliberately underexposing input videos during capture, inspired by HDR+ [Hasinoff et al. 2016]. We demonstrate that we can reconstruct high-quality, noise-free scene radiance from noisy, dim input videos, and further render sharp, blur-free videos at a higher dynamic range.

MrNeRF

15,244 görüntüleme • 1 yıl önce

Introducing Continuous Thought Machines New Blog: Modern AI is powerful, but it’s still distinct from human-like flexible intelligence. We believe neural timing is key. Our Continuous Thought Machine is built from the ground up to use neural dynamics as a powerful representation for intelligence. Thought takes time, and reasoning is a process. Biological brains inspire us with their complex neural activity, where neural timing is critical to intelligence. We’re exploring how to bring that power to AI. The Continuous Thought Machine (CTM) incorporates neuron-level temporal processing and neural synchronization, moving beyond current AI limitations. Our approach has two core innovations: (1) neuron-level temporal processing, where each neuron uses unique parameters to process a history of incoming signals for fine-grained temporal dynamics, and (2) neural synchronization, used as a direct latent representation to modulate data and produce outputs, encoding information directly in the timing of neural activity. Learn more about our approach: Interactive Report: Full Paper: GitHub :
0:57

Introducing Continuous Thought Machines New Blog: Modern AI is powerful, but it’s still distinct from human-like flexible intelligence. We believe neural timing is key. Our Continuous Thought Machine is built from the ground up to use neural dynamics as a powerful representation for intelligence. Thought takes time, and reasoning is a process. Biological brains inspire us with their complex neural activity, where neural timing is critical to intelligence. We’re exploring how to bring that power to AI. The Continuous Thought Machine (CTM) incorporates neuron-level temporal processing and neural synchronization, moving beyond current AI limitations. Our approach has two core innovations: (1) neuron-level temporal processing, where each neuron uses unique parameters to process a history of incoming signals for fine-grained temporal dynamics, and (2) neural synchronization, used as a direct latent representation to modulate data and produce outputs, encoding information directly in the timing of neural activity. Learn more about our approach: Interactive Report: Full Paper: GitHub :

Sakana AI

289,680 görüntüleme • 1 yıl önce

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

106,519 görüntüleme • 2 yıl önce

SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes abs: paper page: Existing methods for the 4D reconstruction of general, non-rigidly deforming objects focus on novel-view synthesis and neglect correspondences. However, time consistency enables advanced downstream tasks like 3D editing, motion analysis, or virtual-asset creation. We propose SceNeRFlow to reconstruct a general, non-rigid scene in a time-consistent manner. Our dynamic-NeRF method takes multi-view RGB videos and background images from static cameras with known camera parameters as input. It then reconstructs the deformations of an estimated canonical model of the geometry and appearance in an online fashion. Since this canonical model is time-invariant, we obtain correspondences even for long-term, long-range motions. We employ neural scene representations to parametrize the components of our method. Like prior dynamic-NeRF methods, we use a backwards deformation model. We find non-trivial adaptations of this model necessary to handle larger motions: We decompose the deformations into a strongly regularized coarse component and a weakly regularized fine component, where the coarse component also extends the deformation field into the space surrounding the object, which enables tracking over time. We show experimentally that, unlike prior work that only handles small motion, our method enables the reconstruction of studio-scale motions.
0:26

SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes abs: paper page: Existing methods for the 4D reconstruction of general, non-rigidly deforming objects focus on novel-view synthesis and neglect correspondences. However, time consistency enables advanced downstream tasks like 3D editing, motion analysis, or virtual-asset creation. We propose SceNeRFlow to reconstruct a general, non-rigid scene in a time-consistent manner. Our dynamic-NeRF method takes multi-view RGB videos and background images from static cameras with known camera parameters as input. It then reconstructs the deformations of an estimated canonical model of the geometry and appearance in an online fashion. Since this canonical model is time-invariant, we obtain correspondences even for long-term, long-range motions. We employ neural scene representations to parametrize the components of our method. Like prior dynamic-NeRF methods, we use a backwards deformation model. We find non-trivial adaptations of this model necessary to handle larger motions: We decompose the deformations into a strongly regularized coarse component and a weakly regularized fine component, where the coarse component also extends the deformation field into the space surrounding the object, which enables tracking over time. We show experimentally that, unlike prior work that only handles small motion, our method enables the reconstruction of studio-scale motions.

AK

76,380 görüntüleme • 2 yıl önce

It's time to present Pixel Trading Group! The goal is simple: connect traders and win together 🏆 ⭐️ The best tools in the market (bundles, volumes, alerts..) ⭐️ Fast trackers! ⭐️ Beginner-friendly tutorials (for new joiners) Pixel is NOT a call group, but a group that has the cleanest setup (no excessive useless tools) for traders to connect and win together. We foster an environment in which people lock in and win together! Join below and see what some of the members have been cooking:
1:06

It's time to present Pixel Trading Group! The goal is simple: connect traders and win together 🏆 ⭐️ The best tools in the market (bundles, volumes, alerts..) ⭐️ Fast trackers! ⭐️ Beginner-friendly tutorials (for new joiners) Pixel is NOT a call group, but a group that has the cleanest setup (no excessive useless tools) for traders to connect and win together. We foster an environment in which people lock in and win together! Join below and see what some of the members have been cooking:

Pixel Trading Group

155,113 görüntüleme • 1 yıl önce

With every generation of Pixel, Google delivers innovation to consumers, and our latest devices are no different. Hear about how Pixel uses AI to bring helpful experiences to life from VP of Product Management at Pixel, Venkat Rapaka. Learn more →
2:04

With every generation of Pixel, Google delivers innovation to consumers, and our latest devices are no different. Hear about how Pixel uses AI to bring helpful experiences to life from VP of Product Management at Pixel, Venkat Rapaka. Learn more →

Made by Google

22,267 görüntüleme • 1 yıl önce

Our token, $PIXEL, is officially live on Binance, the world’s largest crypto exchange 🔥 We’ve been working towards this moment for over 2 years, and we couldn’t be more excited to share this massive milestone with you all. This is just the beginning and the best is yet to come for the Pixels community. New here? Welcome to Pixels: Pixels is a free-to-play farming MMO with a play-to-earn (P2E) model. Gameplay revolves around resource management, strategic exploration and community building. Over the past year, we’ve grown to one of Web3’s largest games, with daily active players of 180k+. At Pixels, we build with our community. We're far from a traditional game studio, and our development process reflects that. Over the past 2 years, we’ve had 60+ public updates. This approach enables us to iterate and experiment more quickly, giving players a true sense of ownership over the game's direction. When you join our community, you are not just a player of Pixels, but a creator of it. Our Mission: We’re excited by the potential of Web3 technology and its ability to significantly enhance the gaming experience. We aim to experiment, build, and iterate on the features that make Web3 gaming unique. We’re here to set a new standard. Here are some of the interesting areas we've leaned into: ➡️ P2E: earn $PIXEL by participating in gameplay ➡️ Guilds: friendtech + competition ➡️ On-chain Reputation & Player Personas: creating a sustainable ecosystem of real players ➡️ Interoperability: using on-chain data to bring communities together We're eager to explore more possibilities in this space and push Web3 even further. Meet $PIXEL ✨ $PIXEL is an in-game utility token. It allows players to enhance their gameplay experience in various ways: ➡️ Purchase VIP memberships ➡️ Create and join guilds ➡️ Mint a Pixels Pet ➡️ Upgrade lands We believe Web3 games don’t need to look exactly like Web2 games. However, P2E formats need sustainable structures and economics. With that, we’ve focused heavily on persona types: why would each user want to use this token? We want to ensure we’ve created an environment where first: ➡️ Our game is providing real enjoyment and entertainment to our users ➡️ Our currency can help amplify that experience without affecting the experience of other users How do I get $PIXEL? Users can earn $PIXEL in-game right now. Head over to Buck’s Galore and chat with Hazel. She will provide you with your daily list of tasks. Within these tasks, you may be lucky enough to be given one that rewards in $PIXEL. Final Thoughts: In 2024, Pixels is stepping into its next chapter of growth fueled by the listing of $PIXEL on Binance. To the Pixels community, today is a historic moment for all of us. We can’t wait to welcome even more members to our community in the days, weeks, and years to come.
1:27

Our token, $PIXEL, is officially live on Binance, the world’s largest crypto exchange 🔥 We’ve been working towards this moment for over 2 years, and we couldn’t be more excited to share this massive milestone with you all. This is just the beginning and the best is yet to come for the Pixels community. New here? Welcome to Pixels: Pixels is a free-to-play farming MMO with a play-to-earn (P2E) model. Gameplay revolves around resource management, strategic exploration and community building. Over the past year, we’ve grown to one of Web3’s largest games, with daily active players of 180k+. At Pixels, we build with our community. We're far from a traditional game studio, and our development process reflects that. Over the past 2 years, we’ve had 60+ public updates. This approach enables us to iterate and experiment more quickly, giving players a true sense of ownership over the game's direction. When you join our community, you are not just a player of Pixels, but a creator of it. Our Mission: We’re excited by the potential of Web3 technology and its ability to significantly enhance the gaming experience. We aim to experiment, build, and iterate on the features that make Web3 gaming unique. We’re here to set a new standard. Here are some of the interesting areas we've leaned into: ➡️ P2E: earn $PIXEL by participating in gameplay ➡️ Guilds: friendtech + competition ➡️ On-chain Reputation & Player Personas: creating a sustainable ecosystem of real players ➡️ Interoperability: using on-chain data to bring communities together We're eager to explore more possibilities in this space and push Web3 even further. Meet $PIXEL ✨ $PIXEL is an in-game utility token. It allows players to enhance their gameplay experience in various ways: ➡️ Purchase VIP memberships ➡️ Create and join guilds ➡️ Mint a Pixels Pet ➡️ Upgrade lands We believe Web3 games don’t need to look exactly like Web2 games. However, P2E formats need sustainable structures and economics. With that, we’ve focused heavily on persona types: why would each user want to use this token? We want to ensure we’ve created an environment where first: ➡️ Our game is providing real enjoyment and entertainment to our users ➡️ Our currency can help amplify that experience without affecting the experience of other users How do I get $PIXEL? Users can earn $PIXEL in-game right now. Head over to Buck’s Galore and chat with Hazel. She will provide you with your daily list of tasks. Within these tasks, you may be lucky enough to be given one that rewards in $PIXEL. Final Thoughts: In 2024, Pixels is stepping into its next chapter of growth fueled by the listing of $PIXEL on Binance. To the Pixels community, today is a historic moment for all of us. We can’t wait to welcome even more members to our community in the days, weeks, and years to come.

Pixels

1,461,070 görüntüleme • 2 yıl önce

Our new Gaussian point splatting renders huge 3DGS scenes in real time (e.g. 425 M⬇️). It samples pixel-sized points from the Gaussians with massive parallelism and splats them to the screen using 64-bit atomics. Grab ACM SIGGRAPH 2026 paper+code+video now:
0:24

Our new Gaussian point splatting renders huge 3DGS scenes in real time (e.g. 425 M⬇️). It samples pixel-sized points from the Gaussians with massive parallelism and splats them to the screen using 64-bit atomics. Grab ACM SIGGRAPH 2026 paper+code+video now:

Christoph Peters

22,195 görüntüleme • 25 gün önce

My game is using pixel art textures with a pixel density of 64px per meter. Got Dmitry Karpukhin blockout tool. Set the grid size to 15.625 and added a decimal grid size of 1.5625 to the editor for snapping. Ta-da pixel-perfect. And yes, I'm a maniac. 😜
0:37

My game is using pixel art textures with a pixel density of 64px per meter. Got Dmitry Karpukhin blockout tool. Set the grid size to 15.625 and added a decimal grid size of 1.5625 to the editor for snapping. Ta-da pixel-perfect. And yes, I'm a maniac. 😜

Gordon Zuchhold

22,914 görüntüleme • 1 ay önce

Working on drawing my the next town for Lair, pixel by pixel. It might not look like it from the zoomed out pictures but there is a lot of detail and these take a really long time.
0:48

Working on drawing my the next town for Lair, pixel by pixel. It might not look like it from the zoomed out pictures but there is a lot of detail and these take a really long time.

David - Nostalgic Realms - Lair of the Leviathan

11,645 görüntüleme • 10 ay önce

In my experience, robot 'generalists' are often jacks of all trades but masters of none. In training across multiple tasks and environments, robot policies fail to generalize robustly and effectively to each particular test setting. What if at test time, we non-parametrically *retrieved* “relevant” data from the training set and used it to significantly improve the performance of few-shot imitation learning to be robust to various test time scenes. Notably, we are *not* collecting lots of new data, just training more on sub-components of the same training data! Now, we’re certainly not the first to suggest retrieval, but in our new work - STRAP, we show how retrieving relevant *sub-trajectories* from offline datasets can significantly increase data reuse across tasks, when paired with an appropriate metric space. A 🧵 (1/7)
0:43

In my experience, robot 'generalists' are often jacks of all trades but masters of none. In training across multiple tasks and environments, robot policies fail to generalize robustly and effectively to each particular test setting. What if at test time, we non-parametrically *retrieved* “relevant” data from the training set and used it to significantly improve the performance of few-shot imitation learning to be robust to various test time scenes. Notably, we are *not* collecting lots of new data, just training more on sub-components of the same training data! Now, we’re certainly not the first to suggest retrieval, but in our new work - STRAP, we show how retrieving relevant *sub-trajectories* from offline datasets can significantly increase data reuse across tasks, when paired with an appropriate metric space. A 🧵 (1/7)

Abhishek Gupta

12,045 görüntüleme • 1 yıl önce

Artificial lifeforms are super fascinating to watch. These self-organizing, self-replicating, “lifeforms” emerged from a continuous time cellular automata system called Flow-Lenia. Lenia is a family of CAs generalizing Conway’s Game of Life to continuous space, time and states.
0:20

Artificial lifeforms are super fascinating to watch. These self-organizing, self-replicating, “lifeforms” emerged from a continuous time cellular automata system called Flow-Lenia. Lenia is a family of CAs generalizing Conway’s Game of Life to continuous space, time and states.

hardmaru

568,485 görüntüleme • 2 yıl önce