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WeatherEdit: Controllable Weather Editing with 4D Gaussian Field Contributions: 1. Based on our analysis of weather editing characteristics, we introduce WeatherEdit, a comprehensive and efficient framework for realistic and controllable weather generation. Compared with existing methods that focus on either background editing or static weather effects, a progressive 2D-to-4D... transformation process in WeatherEdit enhances adaptability across a wider range of scenarios. 2. We introduce an all-in-one adapter to enable a diffusion model for multi-weather (snowy, rainy, and fog) synthesis, along with a Temporal-View attention to ensure consistent editing across multi-frame and multi-view. 3. We design a 4D Gaussian field for weather particle modeling, enabling plausible simulation of raindrops, snowflakes, and fog with controllable severity. 4. We demonstrate WeatherEdit’s effectiveness in generating realistic, consistent, and controllable weather effects in 3D driving scenes, showcasing its applicability to real-world scenarios.show more

MrNeRF

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10,691 Aufrufe • vor 1 Jahr •via X (Twitter)

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Weather trading bots making thousands $ on Polymarket by simple arbitrage. > Scanning weather markets every 2 mins and comparing data to NOAA forecast for location/date > If they see undervalued temperature bucket (<0.15¢), they buy it and flip for profit. Start testing with small sum $2-5 to calibrate your bot and give it data for analysis. Scale it when you see $20-50 of consistent revenue for several days. Check out this example of a bot account raking in over $65k profit from automated weather trades on Polymarket

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Aleiah

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Is Google taking initial steps to enhance Street View? For some reason, Street View seems stuck in technology that feels outdated. I wonder if we'll see such improvements on the product side. Also, note how much better it performs in all aspects compared to Zip-NeRF in their presented material. It offers more details and fewer artifacts. Great work! "LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering" Contributions: • We propose a novel LOD representation for 3DGS which, unlike previous methods [27, 28, 17], does not recompute the list of used Gaussians at each frame. This allows for acceleration and compaction, enabling the rendering of large-scale scenes even on mobile devices. • We design a strategy to automatically select optimal hyperparameters for splitting LODs, whereas most other methods require manual tuning of hyperparameters for each 3D scene. • To further accelerate rendering, we split the scene into chunks and pre-compute sets of active Gaussians per chunk. • Finally, we introduce a novel opacity interpolation scheme to produce visually pleasing rendering and eliminate artifacts when transitioning between chunks.

Is Google taking initial steps to enhance Street View? For some reason, Street View seems stuck in technology that feels outdated. I wonder if we'll see such improvements on the product side. Also, note how much better it performs in all aspects compared to Zip-NeRF in their presented material. It offers more details and fewer artifacts. Great work! "LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering" Contributions: • We propose a novel LOD representation for 3DGS which, unlike previous methods [27, 28, 17], does not recompute the list of used Gaussians at each frame. This allows for acceleration and compaction, enabling the rendering of large-scale scenes even on mobile devices. • We design a strategy to automatically select optimal hyperparameters for splitting LODs, whereas most other methods require manual tuning of hyperparameters for each 3D scene. • To further accelerate rendering, we split the scene into chunks and pre-compute sets of active Gaussians per chunk. • Finally, we introduce a novel opacity interpolation scheme to produce visually pleasing rendering and eliminate artifacts when transitioning between chunks.

MrNeRF

62,564 Aufrufe • vor 1 Jahr

How to make money on the weather using Polymarket I've been noticing more and more traders quietly printing on Polymarket's weather markets lately - and the category is exploding for a reason. Weather has always been super predictable for meteorologists (and us normals) right up to the day of. The whole point? You can earn easy, near-certain yield just knowing it'll rain in London tomorrow. I'm sharing a finished tutorial with you. Here is a small list of traders 1. gopfan2 ( - The absolute leader in weather. Earned over $2M in net profit by focusing on temperature and precipitation. Strategy - buy Yes below 15 cents, No above 45 cents, with risks of less than $1 per position. It dominates the NYC and London markets where the weather is predictable 2. enzocostapt81 ( is a weather-exclusive trader whose profile shows a complete wipeout in resolved positions-no active/open trades, current positions value $0.00, and all listed markets (resolved) at -100% P/L. The trader focused solely on daily/precise temperature predictions in major cities like New York City and London. 3. 0x594edb9112f526fa6a80b8f858a6379c8a2c1c11 ( 100% of active positions are weather/temperature markets across cities like Dallas, London, Seattle, Atlanta, NYC, and Toronto-focused on precise daily highs/thresholds/ranges. 4. meropi ( - Earned ~$30k on micro bets ($1-3) with multipliers up to 500x. Automated bets on temperature rise for 0.01 cents. Focus on speed to capture momentum in daily markets. One of the most stable in weather 5. 1pixel ( - $18.5k profit from $2.3k deposit, weather only (NYC and London) 6. erb80 ( Dominant focus-two massive Atlanta temperature range bets for Dec 17, with enormous share volume at ultra-low entries (0.1¢) turning into huge unrealized gains (+49,550% on the main one) 7. Hans323 ( - Earned $1.1M on one temperature trade in London. Started with $741 in January 2025 and increased to $87k net profit for the year 8. securebet ( - Turned $7 into $640 (+9244%) on a series of temperature bets in NYC and Seattle. 3077 predictions, top 0.04% by metrics. Focus on small bets ($3-20) with high growth on low quotes. High win rate thanks to NOAA data 9. automatedAItradingbot ( Micro/low-cost bets (0.4¢–15¢) on specific outcomes, especially weather thresholds in Seoul/London and fighter matchups.Explosive wins (300%+ on select weather 1,000–5,000% average ROI across successful weather specialists based on this traders Tools and Automation > - Built specifically for Polymarket weather traders. Offers real-time multi-model forecasts (GFS, ECMWF, etc.), temperature range dashboards, climate pattern guides per city/station, and settlement station details. Includes educational guides on seasonal biases and forecasting challenges—highly recommended for NYC/London/Atlanta markets. > - Free guide/resource hub for weather betting on Polymarket. Covers market overviews, settlement rules > - US GFS and ECMWF Europe models for temperature, precipitation, hurricane forecasts. Updates every 6 hours. Ideal for comparing models if 3+ agree, the probability is high > - real-time maps of air/ocean temperature, precipitation anomalies. With historical context for calculating probabilities > - interactive maps of wind, temperature, rain, snow. 10+ models, for local events NOAA Climate Data Online - 100+ years of historical location data NOAA Weather Prediction > - short forecasts for precipitation, anomalies. Climate Prediction Center - long-term ENSO, droughts > - Completely free open-source weather API with no key required. Provides GFS, ECMWF-derived, and ensemble forecasts for temperature, precipitation, and more at hourly/sub-hourly resolution globally. Excellent for scripting quick checks on NYC/London highs or comparing multiple models. Direct API calls make it ideal for automation or batch probability calculations. > - Free tier gives current conditions, 5-day/3-hour forecasts, and 16-day daily forecasts. Good for real-time verification and basic historical pulls (limited free). Use for cross-checking Polymarket ranges before resolution. > - Free tier includes historical data (50+ years), current conditions, hourly/sub-hourly forecasts, and alerts. Strong for querying specific cities > - Free plan covers real-time, hourly, daily forecasts (up to 14 days), historical data (from 2010), and bulk requests. Reliable for urban stations and includes marine/pollen extras if needed. Quick Tips for Using These in Trading >>>Cross-verify 3+ models (e.g., GFS + ECMWF via Open-Meteo + Windy) → if 80%+ agree on a range/threshold, probability is often very high for "Yes" bets under 10-15¢. >>>Focus on major stations (e.g., Central Park for NYC, Heathrow for London) - check settlement rules on Polymarket pages. >>>ADD TO BOOKMARKS so you don't lose alpha information

Valentin

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8 free Polymarket Trading Bots on GitHub (from Beginner Friendly to Advanced Level). Each of these repos comes with a detailed step by step setup and usage guide in English. > Beginner Level - 5 min setup 1. This bot includes 120 ready to use strategies and tools for trading on prediction markets (Binance-Polymarket latency, Smart Routing, Penny Clipper, Momentum, DCA bots, Expiry Fade and more). It was built by a Cambridge computer science student who won a hackathon with this bot. GitHub: 2. A trading bot with a Smart Money strategy - it finds top traders in selected markets, filters them by Pnl, win rate, stable performance and then creates a list for automated copy trading. GitHub: 3. This is a bot toolkit that includes Polymarket - Kalshi arbitrage, whale alerts, market making, spread farming, sports trading and more. GitHub: 4. A weather trading bot from Chinese dev that analyzes different sources in real time, like forecasts, airport data and aviation observations (METAR + SPECI) to get the latest temperature data and generate a detailed weather report for a specific city and day. GitHub: 5. A huge collection of 30+ free trading bots and services for prediction markets. GitHub: > Advanced bot setup 1. This bot analyzes the real trading behavior of any Polymarket trader. It finds repeated patterns in his trades, shows which strategies he uses and helps you understand how to adapt them to your own trading. GitHub: 2. A bot that automatically manages all your limit orders on Polymarket to maximize liquidity rewards. GitHub: > A full ML weather model 1. A machine learning weather model that learns from weather forecasting errors. Instead of blindly trusting forecasts, it analyzes how different weather sources have historically overestimated or underestimated temperature values in specific cities and conditions. Then it automatically adjusts new forecasts to produce more accurate predictions. GitHub: All of these bots also support Dry Run mode, so you can test them on real markets without risking any funds.

Recogard

58,826 Aufrufe • vor 2 Monaten

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.

AK

65,861 Aufrufe • vor 2 Jahren

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.

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.

AK

634,252 Aufrufe • vor 3 Jahren

Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.

Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.

AK

19,684 Aufrufe • vor 2 Jahren