🚨🔬💻Happy to introduce the new #eSRRF approach, an analytical... #3D #SuperResolution #Microscopy method with improved image fidelity and resolution for live-cell nanoscopy. #eSRRF enables optimization based on the data itself for high-quality nanoscopyshow more

Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic... Gaussians paper page: Creating high-fidelity 3D head avatars has always been a research hotspot, but there remains a great challenge under lightweight sparse view setups. In this paper, we propose Gaussian Head Avatar represented by controllable 3D Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions. The two parts benefit each other, thereby our method can model fine-grained dynamic details while ensuring expression accuracy. Furthermore, we devise a well-designed geometry-guided initialization strategy based on implicit SDF and Deep Marching Tetrahedra for the stability and convergence of the training procedure. Experiments show our approach outperforms other state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions.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

High-resolution image and video generation is hitting a wall... because attention in DiTs scales quadratically with token count. But does every pixel need to be in full resolution? Introducing Foveated Diffusion: a new approach for efficient diffusion-based generation that allocates compute where it matters most. 1/7🧵show more

🚨 TRELLIS.2 is now live on fal! 🎯 Image-to-3D... model producing up to 1536³ PBR textured assets 🎨 Handles arbitrary topology with rich PBR textures (Base Color, Metallic, Roughness, Alpha) ⚡ 16× spatial compression for efficient, scalable, high-fidelity asset generationshow more

gm Frag 011 (wip) The AI image-to-video generator expressed... itself, unprompted, using Phantom 3D and Optics Logic behaviors. I’ve been meaning to mention that MANY artists work so beautifully with unprompted generation. My Native project differs only in intent, not in method—it treats the machine’s unprompted gestures as data for an ongoing inquiry into perception itself. I’m documenting the phenomenon, not claiming ownership of it—an important distinction for me to make clear.show more

GPT Image 2.0 + Generated Normal Maps = Dynamic... Lighting and Shadows for UI. 🤯 The workflow for building high-fidelity skeuomorphic UI concepts with AI + Three.js just got a lot easier. Interactive experience, sound fx, and normals all generated with Omma AI (tool by Spline). 3D iPhone by Ranguel on Sketchfab. Happy to share the remix link if you would like to poke around, lmk!show more

In partnership with Airtel Africa, Starlink Direct to Cell... will connect more than 170 million people in Africa across 14 countries, powering life-saving connectivity when it’s needed most. This marks the sixth continent for the satellite-to-mobile service and expands our mission to end mobile dead zones. The service will begin by delivering data that enables voice, video and messaging, and will advance to providing high-speed broadband service to smartphones with 20x improved data speeds →show more

GPT Image 2.0 is now available on Akool 🚀... This isn’t just an upgrade—it’s a major leap in AI image generation. With GPT Image 2.0, you get: • Sharper, high-resolution outputs with improved detail and realism • Much stronger text rendering (finally, text in images that actually looks right) • Better prompt understanding for more accurate and controllable results • Improved consistency across variations and multi-image generations • More precise editing & refinement for iterative creative workflows Whether you're creating marketing visuals, product images, or storytelling assets, GPT Image 2.0 delivers cleaner, more reliable, and production-ready results. Now live on Akool, try the latest in AI image generation today.show more

The era of ultra-high-resolution imaging has arrived. Modern image... sensors exceeding 200 MP resolution are common in smartphones, with over 400 MP sensors under development. However, the large number of pixels poses significant challenges for acquisition and processing, especially on edge devices. Which pixels should be acquired, and when, for bandwidth-efficient imaging and perception? We introduce Policy-based Foveated Imaging and Perception, an on-device, real-time, predictive, and task-aware framework that dynamically allocates sensor resolution to prioritize important regions under specific perception objectives. This paper will be presented at #SIGGRAPH2026! [1/6]show more

Let's say goodbye to the 2023 and happy welcome... to the 2024. Nezuko from Demon Slayer. NSFW package on patreon. I also made an image package with dialogues, tinies, clean and gore version. Thanks for everything, Happy New Year for everyone!!! #giantess #sizetwitter #NewYear2024show more

NEW: Limitless, the largest prediction market on Base, adopts... Chainlink Data Streams as its oracle infra to power high-speed resolution for short-term, high-volume crypto markets with instant payouts. Predict outcomes across BTC, ETH, SOL, and more leading assets today.show more

EDGS: Eliminating Densification for Efficient Convergence of 3DGS Contributions:... • We show that initial triangulation based on 2D correspondences can replace the incremental refinement process, fundamentally changing how 3DGS models allocate resources. • Our method reduces the path each Gaussian must travel in parameter space. Careful initialization not only accelerates convergence but also guides optimization toward a convergence point corresponding to lower reconstruction error and thus higher reconstruction quality. • Our approach outperforms both speed-optimized and quality-focused state-of-the-art models while using only half the splats of standard 3DGS. By improving initialization rather than altering the optimization process, this method is compatible with other 3DGS acceleration techniques, making it a flexible enhancement to existing models.show more

DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from... In-the-Wild Drone Imagery Abstract: Drones have become essential tools for reconstructing wild scenes due to their outstanding maneuverability. Recent advances in radiance field methods have achieved remarkable rendering quality, providing a new avenue for 3D reconstruction from drone imagery. However, dynamic distractors in wild environments challenge the static scene assumption in radiance fields, while limited view constraints hinder the accurate capture of underlying scene geometry. To address these challenges, we introduce DroneSplat, a novel framework designed for robust 3D reconstruction from in-the-wild drone imagery. Our method adaptively adjusts masking thresholds by integrating local-global segmentation heuristics with statistical approaches, enabling precise identification and elimination of dynamic distractors in static scenes. We enhance 3D Gaussian Splatting with multi-view stereo predictions and a voxel-guided optimization strategy, supporting high-quality rendering under limited view constraints. For comprehensive evaluation, we provide a drone-captured 3D reconstruction dataset encompassing both dynamic and static scenes. Extensive experiments demonstrate that DroneSplat outperforms both 3DGS and NeRF baselines in handling in-the-wild drone imagery.show more

[SIGGRAPH ASIA '25] Detail-Enhanced Gaussian Splatting for Large-Scale Volumetric... Capture Contributions: - A two-stage approach to performance capture, combining a scene-scale capture rig and a single-actor facial capture rig. - A novel high-quality scene-scale volumetric performance capture rig, incorporating both static and dynamic cameras to track the performance of multiple actors. - A reconstruction pipeline for dynamic performance capture, featuring stable calibration of moving cameras and 4DGS with improved dynamic range and color fidelity. - A detail enhancement Diffusion Model, which supports 4K, RGB, and Alpha, with improved temporal stability.show more

Apple just trained a 3D Gaussian head reconstruction model... on 10,000+ subjects. Feed-forward. No test-time optimization. New identity in, reconstructed Gaussian head out. The UV-parameterized Gaussian representation decouples the number of Gaussians from the number and resolution of input images, making it practical to train with many high resolution views. And the heads are not just static either: text-conditioned identity generation, plus blendshape-driven latent animation across identities. We've been building in the 3D Gaussian Splatting space for a while. The gap between "research demo" and "works on real people at scale" is closing fast.show more

🚀 Free High Quality Satellite Imagery With Playback Today,... we're introducing beautiful high quality satellite imagery from the GOES weather satellites. Free for all users: • Access to all 16 bands (L1b products) • Coverage across CONUS, both Mesoscale sectors and Full Disk • Playback with up to 15 frames • Real-time updates • Inspect the data directly on the map Plus subscribers unlock more: • Imagery displayed on a 3D globe (Globe Projection) • Split View to compare bands side-by-side • Playback with up to 50 frames • Smoothing options: see the raw data or a polished look • RenderStream for faster loading when in low cell service areas Tropical season is now here - stay one step ahead Available on: iOS: Android: Browser: And to Plus subscribers using our Windows/Mac Appshow more

Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction... Note: Check below for full video. Abstract (cited): "In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion, and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. Our technique is particularly effective for high-quality scene reconstruction from large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. We introduce a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortions, and demonstrates state-of-the-art performance on both synthetic and real-world datasets."show more

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.show more

Thrilled to introduce ApexTrader & NeuroQuant, our newest AI... agents now live in the Beta! These cutting-edge algorithms combine advanced ML with real-time market data to deliver sharper insights, predictive analytics, and dynamic strategy optimization. Elevate your trading game—try them now and stay tuned for more AI releases!show more

3. Modify image Use this prompt to modify the... image you have chosen: "Modify image [1] with seed [1470033597]: add a parrot on her shoulder" Dall-E 3 identifies the image and makes the changes for you! Tips and tricks: - You can generate as many variations as you like. - It's also possible to remove an element from the image using the same method. - Sometimes the image isn't 100% identical, but still looks very similar.show more