SplatVoxel: History-Aware Novel View Streaming without Temporal...

Uploaded: 2025-03-20T07:18:51.000Z
Duration: PT18.747S
Channel: MrNeRF

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Adaptive and Temporally Consistent Gaussian Surfels for Multi-view Dynamic Reconstruction Contributions: • A method for efficiently reconstructing dynamic surfaces from multi-view videos using Gaussian surfels. • A unified and gradient-aware densification strategy for optimizing dynamic 3D Gaussians with fine details. • A temporal consistency approach that ensures stable and coherent surface reconstructions across frames by enforcing consistency on curvature maps. • Extensive experiments that demonstrate our method’s advantages including fast training, high-fidelity novel view synthesis, and accurate surface geometry.

MrNeRF

31,824 views • 1 year ago

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F3D-Gaus: Feed-forward 3D-aware Generation on ImageNet with Cycle-Consistent Gaussian Splatting Contributions: • We pioneer 3D-aware generation using generalizable feed-forward Gaussian Splatting representation, achieving significant efficiency and favorable rendering quality on monocular datasets. • We significantly advance the capability of pixel-aligned Gaussian Splatting representations by designing a self-supervised cycle training strategy specifically tailored for monocular datasets. • We further mitigate the artifacts of 3D-aware representations caused by large viewpoint shifts by introducing geometry-aware video priors.

MrNeRF

14,229 views • 1 year ago

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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."

MrNeRF

17,206 views • 1 year ago

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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

24,729 views • 1 year ago

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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

52,849 views • 1 year ago

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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.

MrNeRF

10,691 views • 1 year ago

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Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation paper page: Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.

AK

375,160 views • 3 years ago

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PackUV: Packed Gaussian UV Maps for 4D Volumetric Video - PackUV — A new volumetric video representation that packs 3D Gaussian attributes into a sequence of UV atlases for efficient streaming and storage, making it readily compatible with existing video coding infrastructure. - PackUV-GS — An efficient method to fit PackUV directly from multiview videos using optical-flow-based keyframing and Gaussian labeling to handle large motions, disocclusions, and temporal consistency. - PackUV-2B — The largest multi-view 4D dataset with 2B frames, large motions, and disocclusions. It provides 360° coverage from 50+ synchronized cameras.

MrNeRF

17,035 views • 5 months ago

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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.

MrNeRF

62,564 views • 1 year ago

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GSTAR: Gaussian Surface Tracking and Reconstruction Contributions: • A new framework for tracking and reconstructing dynamic scenes, combining 3D Gaussians and meshes to effectively manage changes in topology. • A method for Gaussian unbinding and surface re-meshing, allowing for the generation of new surfaces as topologies evolve. • A method for handling large or fast deformations of surfaces between frames using scene flow warping. Abstract (excerpt): However, tracking dynamic surfaces with 3D Gaussians remains challenging due to complex topology changes, such as surfaces appearing, disappearing, or splitting. To address these challenges, we propose GSTAR, a novel method that achieves photo-realistic rendering, accurate surface reconstruction, and reliable 3D tracking for general dynamic scenes with changing topology. Given multi-view captures as input, GSTAR binds Gaussians to mesh faces to represent dynamic objects. For surfaces with consistent topology, GSTAR maintains the mesh topology and tracks the meshes using Gaussians.

MrNeRF

22,698 views • 1 year ago

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ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies Contributions: 1) We propose ImmerseGen, a novel agent-guided 3D environment generation framework. It uses simplified geometric proxies with alpha-textured meshes to produce compact, photorealistic worlds ready for real-time mobile VR rendering. 2) We propose a novel RGBA texturing paradigm. It first synthesizes 8K terrain textures using a geometry-conditioned panorama generator via user-centric mapping, and then directly generates alpha-textured proxy assets, avoiding fidelity loss typically resulting from mesh decimation. 3) To automate scene creation from user prompts, we introduce VLM-based modeling agents equipped with a novel grid-based semantic analysis. This enables 3D spatial reasoning from 2D observations and ensures accurate asset placement. ImmerseGen further enhances immersion with dynamic effects and ambient audio for a multisensory experience. 4) Experiments on multiple scene-generation scenarios and live mobile VR applications show that ImmerseGen outperforms previous methods in visual quality, realism, spatial coherence, and rendering efficiency for immersive real-time VR experiences.

MrNeRF

14,225 views • 1 year ago

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Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Shikun Liu

22,389 views • 10 months ago

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FastMap: Revisiting Dense and Scalable Structure from Motion "FASTMAP, a redesigned SfM framework, achieves fast, high-accuracy dense structure from motion. On large scenes with thousands of images, FASTMAP is up to one to two orders of magnitude faster than GLOMAP and COLMAP. ... Importantly, FASTMAP achieves efficiency improvements while keeping comparable performance. Extensive experiments on eight datasets demonstrate pose estimation accuracy and novel view synthesis quality close to GLOMAP and COLMAP. " Contributions: 1. For all the iterative nonlinear optimization problems involved, we design algorithms such that the computational complexity of each iteration is only linear in the number of image pairs, not keypoint pairs or 3D points. This includes replacing the traditional bundle adjustment [50] present in previous SfM frameworks with a novel re-weighting epipolar adjustment algorithm, which is much more efficient. 2. Throughout the entire framework, we formulate as many steps as possible as GPU-friendly dense tensor operations. This allows us to implement the entire method in PyTorch [39], which provides seamless GPU acceleration.

MrNeRF

15,233 views • 1 year ago

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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.

AK

194,469 views • 2 years ago

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First fully ML-framework-free 3D Gaussian Splatting implementation in LichtFeld Studio. I’ve completed the migration of the full training pipeline to a custom CUDA-based tensor library. No PyTorch, no LibTorch, no autograd. Every gradient is implemented by hand, either through CUDA kernels or minimal abstractions on top. This makes it the first full training setup for 3D Gaussian Splatting with zero dependencies on existing ML frameworks. It’s not just about independence, it's about control! We now manage every byte of GPU memory, which opens the door to tighter optimization and finer performance tuning. The framework footprint is minimal, without pulling in gigabytes of ML runtime code that was never designed for real-time or graphics-driven applications. A few modules, such as the metrics and 3DGUT interfaces, are still being ported, and some operations are temporarily naïve, so performance is not yet on par with master. But this refactor lays the groundwork for: - A fully self-contained binary - Fine-grained memory optimization - Easier experimentation without the weight of an ML stack We’re getting close.

MrNeRF

50,548 views • 9 months ago

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We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: Code: Like AlphaEvolve and its variants, our framework leverages LLMs to find state-of-the-art solutions to complex problems, but using orders of magnitude fewer resources! Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution, and we designed our system to be just as resourceful. On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a big leap in efficiency compared to previous methods that required thousands of evaluations. We applied ShinkaEvolve to a diverse set of hard problems with real-world applications: 1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering an entire Pareto frontier of solutions trading performance for efficiency. 2/ Competitive Programming: On ALE-Bench (a benchmark for NP-Hard optimization problems), ShinkaEvolve took the best existing agent's solutions and improved them, turning a 5th place solution on one task into a 2nd place leaderboard rank in a competitive programming competition. 3/ LLM Training: We even turned ShinkaEvolve inward to improve LLMs themselves. It tackled the open challenge of designing load balancing losses for Mixture-of-Experts (MoE) models. It discovered a novel loss function that leads to better expert specialization and consistently improves model performance and perplexity. ShinkaEvolve achieves its remarkable sample-efficiency through three key innovations that work together: (1) an adaptive parent sampling strategy to balance exploration and exploitation, (2) novelty-based rejection filtering to avoid redundant work, and (3) a bandit-based LLM ensemble that dynamically picks the best model for the job. By making ShinkaEvolve open-source and highly sample-efficient, our goal is to democratize access to advanced, open-ended discovery tools. Our vision for ShinkaEvolve is to be an easy-to-use companion tool to help scientists and engineers with their daily work. We believe that building more efficient, nature-inspired systems is key to unlocking the future of AI-driven scientific research. We are excited to see what the community builds with it! Learn more in our technical report:

Sakana AI

360,198 views • 10 months ago

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Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

MrNeRF

27,428 views • 1 year ago

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Our aero kit for the 2024+ Model 3 Performance is designed using CFD to enhance performance while preserving Tesla’s renowned efficiency. Shown here is the exploded view of the aero kit parts to show how we accomplish our goal: to work with the Model 3 Performance foundation without disturbing it. The kit now delivers 1896N of downforce, shifting the car from net lift to substantial downforce with a minimal drag increase of just 1.93%. This is achieved through advanced aerodynamic techniques, such as splitter tunnels that enhance front ground effect—features rarely seen on street-legal vehicles. The kit enhances downforce in areas that already have high-pressure air, such as above the grille, over the side inlets, and on top of the front splitter, but also introduces entirely new shapes like the front undertray. Airflow management around the wheels and side skirts is critical for reducing drag and offsetting the added drag from downforce-generating surfaces. Additionally, placing a wing that is highly optimized to minimally disturb Tesla’s refined wake is crucial for maintaining efficiency. While we’re working out a few more final touches with our production run, we are just as excited to reach out to those who have patiently waited and look forward to providing order updates soon!

UP

31,938 views • 1 year ago

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Ahead of Hadron Energy beginning trading on Nasdaq under the ticker $HDRN on May 26th, we wanted to share a glimpse of Hadron’s vision building the future of microreactors. Hadron Energy is developing the Halo Micro Modular Reactor (MMR), a compact, factory-built light-water reactor designed to deliver reliable, carbon-free power for AI data centers, industrial sites, remote communities, military applications, and critical infrastructure. Each Hadron MMR is designed to generate 10 MWe of reliable power and is transportable by truck or rail to support flexible deployment where power is needed most. By generating power directly on-site, Hadron’s microreactors may reduce reliance on extensive transmission and distribution infrastructure. Multiple units can be deployed in a modular format to meet growing demand, with a targeted fuel-cycle of 10 years and an intended 50-year useful life. Hadron’s microreactors leverage proven light-water technology which powers ~90% of the global nuclear power fleet today. The Nuclear Regulatory Commission (NRC) is most familiar with light-water designs, they have ~27,000 cumulative reactor years of operational history, and light-water avoids the risks associated with novel fuels or coolants. Hadron’s microreactors are designed with inherent and passive safety features and are installed below grade to maximize protection of the public and the environment. Hadron uses Low Enriched Uranium Plus (LEU+), enriched to approximately 8% U-235. This fuel builds on the same proven uranium fuel used in today’s commercial reactors while enabling longer operating cycles and efficient power output for microreactor applications through slightly higher enrichment. Because LEU+ remains within the existing regulatory framework, may support a more familiar licensing pathway relative to certain advanced reactor technologies. Most importantly, LEU+ leverages existing portions of the domestic light-water reactor fuel supply chain, which we believe may reduce fuel supply constraints relative to certain advanced reactor approaches dependent on HALEU or TRISO fuel. Our approach focuses on regulatory efficiency and standardized deployment. In October 2025, the NRC accepted Hadron’s Quality Assurance Program Description (QAPD) Topical Report for review, an important step in establishing the company’s regulatory foundation. Hadron maintains active engagement with the NRC through a dual-track pathway, pursuing both Manufacturing and Combined Licenses, while monitoring evolving NRC initiatives intended to support standardized deployment and serial manufacturing approaches for advanced reactors. Hadron’s leadership team brings decades of experience across nuclear plant operations, NRC licensing, reactor engineering, fuel design, and quality assurance, supported by engineers focused on moving from design through deployment efficiently. As electricity demand continues to accelerate globally, particularly across AI infrastructure and industrial applications, Hadron is targeting a growing market expected to exceed $7 billion by 2030, according to Grand View Research. We believe light-water reactors continuing to produce reliable, 24/7 power, will play a critical role in the future of energy and national security. Please refer to Hadron Energy’s public SEC filings for additional information.

Samuel Gibson

67,076 views • 2 months ago

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This app has completely transformed over the last three years and it is amazing to see how far it has come. - Launched X Chat and encrypted DMs - Launched Video/Audio calling - Launched Ad revenue share program for the creators - Reached a new alltime high of userseconds - Launched Community Notes - Community Notes passed 1M contributers mark - Launched the AI driven algo - Paid millions to the creators - Integerated Grok within the app - Rebranded as 𝕏 - Open sourced the recommendation algorithm. - Over 500 million posts everyday - Over 100 billion impressions daily - 650+ million users - 400000+ communities - #1 News App in 155+ countries - 2 billion+ app downloads - People are spending more time on X - 6th most visited website - Launched X Pro - Announced X Payments - Reduced child sexual abuse material by around 95 since the acquisition - Released subscriptions for creators - Removed legacy checkmarks - Prioritized the verified accounts to fight spam bots - All time low hate speech levels - Released new features in the shortest time period in Xs history - Exposed truth via Twitter files - Removed censorship - Restored free speech - Launched long form posts / Articles. - Improved the live streaming quality - Started paying the creators twice in a month - Added live chat to live streams - Enhanced the Community Notes algorithm - Added an ability to post timestamps while uploading videos - Added an edit post feature - Added view counts on posts publicly - Enhanced the support for Premium users - Added similar posts button - Added new app icons for premium users - Launched different tiers for Premium - Launched Verified Organizations - Added bookmark button on home timeline for posts - Over Ninety percent of the top 100 advertisers have returned to X - Video views are up as compared to previous years - All time high platform usage - Added more privacy options for calling - Introduced recruitment jobs feature for verified organizations - Expanded the community notes in more countries worldwide - Improved ad experience - Increased group chat size to 200 people - Lowered the eligibility threshold for ads revenue sharing from 15M to 5M - Rolled out vertical video ads for brands - Improved communities experience - Announced the removal of tweepcred system - Added live streams on the top bar of the feed - Improved live streaming tested game streaming - Launched the immersive video player - Made the process of reporting a post easier - Improved the UI - Announced the ability of group calling - Announced the removal of block feature except for DMs - Major upgrades to videos and media - Secured money transmission license in multiple states for payments - Introduced video downloads for verified users - Added an ability to limit replies to verified users only - Launched expanded bios - Made communities accessible in more countries - Added ID verification - Added an ability to hide likes - Added a tab for verified followers on user profiles - Added an ability to accept dms from verified users only to prevent spam - Introduced dm support for the ad customers - Announced that X will fund legal representation for users fired from their jobs for their posts - Included posts from communities in for you feed - Renamed notes feature to articles that will have no character limits - Added highlights tab to the profiles - Improved account analytics - Added an ability for community admins to spotlight their community in the profile - Introduced voice dms - Deleted millions of bot accounts - Introduced an ability to upload long videos - Improved advertisement brand safety - Introduced formatting for long form posts - Extended the community notes availability to more countries - Introduced individual reply to dms custom emoji reactions - Launched faster playback mini player for videos - Added an ability to allow dms from subs only - Continued UX/Ui improvements - Added air play button for videos on iOS - Added an ability to fast forwardrewind videos on - Became the most downloaded news app in multiple countries - Improved ad relevance - Removed child po*n - Introduced rate limits to prevent data scraping - Added fast forward backward feature to videos - Launched subscriber only replies - Launched spaces on web - Added an ability to hide the blue checkmark - Reduced scam accounts - Improved engagements for advertisers - Reinstated a multitude of suspended accounts - Introduced Multiple organization affiliate badges - Improved post analytics - Community notes debunking propaganda misstatements - Introduced verified badges for organizations - Launched X TV and many more features.

DogeDesigner

3,602,880 views • 9 months ago

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