NeuRBF: A Neural Fields Representation with Adaptive Radial Basis... Functions paper page: present a novel type of neural fields that uses general radial bases for signal representation. State-of-the-art neural fields typically rely on grid-based representations for storing local neural features and N-dimensional linear kernels for interpolating features at continuous query points. The spatial positions of their neural features are fixed on grid nodes and cannot well adapt to target signals. Our method instead builds upon general radial bases with flexible kernel position and shape, which have higher spatial adaptivity and can more closely fit target signals. To further improve the channel-wise capacity of radial basis functions, we propose to compose them with multi-frequency sinusoid functions. This technique extends a radial basis to multiple Fourier radial bases of different frequency bands without requiring extra parameters, facilitating the representation of details. Moreover, by marrying adaptive radial bases with grid-based ones, our hybrid combination inherits both adaptivity and interpolation smoothness. We carefully designed weighting schemes to let radial bases adapt to different types of signals effectively. Our experiments on 2D image and 3D signed distance field representation demonstrate the higher accuracy and compactness of our method than prior arts. When applied to neural radiance field reconstruction, our method achieves state-of-the-art rendering quality, with small model size and comparable training speed.show more

Electromagnetic radiation: oscillating electric and magnetic fields propagate outward... from a source at the speed of light. The fields E and B remain mutually perpendicular and orthogonal to the radial unit vector r̂. Intensity I of the radiation decreases with the inverse square of the distance, I ∝ r⁻².show more

Human Hair Reconstruction with Strand-Aligned 3D Gaussians Contributions (cited):... – We propose a new 3D line lifting scheme that uses a modified 3DGS reconstruction technique to lift 2D orientation maps into a 3D field while also providing refinement of the camera parameters; – We introduce a dual representation of hair strand polylines and 3D Gaussians to achieve differentiable rasterization of hair strands and leverage photometric constraints for strand-based hair reconstruction; – Based on these components, we propose a coarse-to-fine optimization method for prior-guided hair reconstruction that leverages both latent and explicit representations of the hairstyle.show more

📢DNF: Generating 4D animations with dictionary-based neural fields! Xinyi... Zhang presents a new dictionary-based neural field for unconditional 4D generation of deforming shapes -- generating motions with high-quality shape and temporal consistency.show more

3D Gaussian Splatting for Real-Time Radiance Field Rendering paper... page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.show more

◉⌖✦⟡ more experiments with radial and pinch ui

Oldies but goldies: J Blinn, A Generalization of Algebraic... Surface Drawing, 1982. "Metaballs" are levelsets of mixtures of radial basis functions, which generate blobby lava-lamp-like behaviors.show more

🚀 Excited to share our #ICLR2025 work on planning... with neural dynamics models! While our lab has developed diverse neural dynamics models for manipulating rigid, deformable, and granular objects, having the model alone doesn’t solve the problem—planning with it remains a challenge. 💡 Enter BaB-ND, led by Keyi and Jiangwei! We propose a scalable, GPU-accelerated branch-and-bound algorithm, inspired by neural network verification, to enable effective planning for diverse objects modeled with neural dynamics. 🔗 Project page (open-source + detailed docs!): 🎥 Watch the video to see T being pushed around obstacles, and check out Keyi’s thread for more details!show more

Bringing new meaning to the word NeRD at #CoRL2025.... 🤓 Neural Robot Dynamics (NeRD) bridges the sim-to-real gap with a hybrid of neural modeling and analytical simulation, unlocking zero-shot transfer and continuous learning loops for optimal robotics training. Read more & try it in Newton 🤖show more

Arthrex fibre tape cerclage, gradual tensioning with tension device... allows you to set height of radial head and DRUJ.show more

Tycoon Signals is now LIVE🎉 We are thrilled to... announce that Tycoon Signals, the innovative social trading platform, is now LIVE and ready to empower you on your trading journey. After months of development and fine-tuning, we're excited to invite you to join our vibrant community of traders and unlock a world of opportunities. At Tycoon Signals, we understand the challenges and complexities of trading in today's fast-paced markets. That's why we've created a platform that combines the power of crowd wisdom, real-time trading signals, and advanced tools to help you make informed trading decisions and maximize your potential. It's time to take your trading experience to the next level. Join Tycoon Signals today by visiting our website at and signing up for a free account. Start exploring the platform and discover the array of features and resources designed to empower you on your trading journey. And guess what? Our premium subscription comes with a FREE 7-day trial, so you can explore all the features with zero risk! No payment required! 💯 Thank you for being a part of our journey. We look forward to seeing you thrive on Tycoon Signals.show more

Triangle Splatting for Real-Time Radiance Field Rendering Contributions: (i)... We propose Triangle Splatting, a novel approach that directly optimizes unstructured triangles, bridging traditional computer graphics and radiance fields. (ii) We introduce a differentiable window function for soft triangle boundaries, enabling effective gradient flow. (iii) We demonstrate qualitatively and quantitatively that Triangle Splatting outperforms concurrent methods in terms of visual quality and rendering speed, and achieves superior perceptual quality compared to the state-of-the-art Zip-NeRF on indoor scenes. (iv) The optimized triangles are directly compatible with standard mesh-based renderers, enabling seamless integration into traditional graphics pipelines.show more

These few weeks I've been working on something cool... 😎 It's a radial 🔘 application switcher/launcher. It is highly customisable 🎨 and packed with lots of features. Here are some of them... 🧵 #indiedev #swiftshow more

Kill several birds with one stone 🎯 Starting with... hand strength, engaging the branchio-radial muscles, then the branchial muscles of the biceps and triceps, the trapezius and the abdominals.show more

A visualization of how a radial engine works

🚨New lecture note 🚨 🧵1) Neural networks tend to... learn low frequencies first, and only later the high-frequency oscillations, if at all. Left panel: training data and the neural-net fit. Right panel: their Fourier transforms. code:show more

Grasps are one of the primary ways in which... we interact with and shape our environments. How can we faithfully capture human grasps with details such as hand/object shape and contact points? At #CVPR2026, we present MANUS, a method to accurately reconstruct grasps and contacts. 🧵show more

Introducing Neural Developmental Programs (NDPs)🧬🧠Instead of neural networks with... fixed architectures, we allow neural networks to grow through a dynamic self-organizing process, inspired by how biological nervous systems develop👇 PDF:show more