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35,708 results · Page 915 of 992

Vireto PDF to Video

AI video

Vireto PDF to Video is a free AI PDF-to-video tool that quickly converts PDF documents into MP4 videos with voiceover, animation, and visual elements. No registration or login is required; after uploading a PDF, the AI automatically generates organized scenes, clear narration, and clean visuals. It supports uploading PDFs of up to 250 pages, making it suitable for turning study notes, courseware, presentations, or guides into videos that are easy to share for learning or teaching.

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Viroo AI

AI video

Viroo AI is an AI video generation tool that focuses on turning ideas into short-form video content suitable for TikTok and YouTube within minutes, with no editing experience required. It offers capabilities such as text-to-video, image-to-video, AI UGC video generation, and AI TikTok video generation, and can automatically generate scripts, covers, and music, while also supporting animations, transitions, and effects. It is officially said to be powered by models such as Sora 2, Veo3, and Wan AI, with generation speeds of about 60 seconds. It uses a flexible token-based billing method, offers subscription plans for individual creators, professional creators, and agencies, and includes a 7-day refund guarantee.

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Virtual Hair Try On

AI lifestyle

Virtual Hair Try On is a browser-based virtual hair-changing tool. After users upload a photo, they can preview different hairstyles and hair colors online. Users can try styles such as bangs, short hair, bob, lob, highlights, balayage, and more. The preview preserves the same face and only changes the hair color, bangs, length, and style. The process is: upload a clear photo, choose a hairstyle direction, generate a preview, compare the before and after effects, and save liked styles as salon references. The official description says no app installation is required, and it can be used in a desktop or mobile browser. After logging in, users can receive 10 Hair Credits, used for one watermarked preview; a standard preview costs 10 credits, and a hairstyle reference preview costs 20 paid credits. The paid credit packages are 400 credits (40 standard or 20 reference previews, watermark-free downloads, credits never expire) and 1,800 credits (180 standard or 90 reference previews). The official description says the tool is intended to help users preview results before cutting or coloring their hair, avoiding mistakes that cost $150–$400 or more or haircut regret that takes months to grow back. The target users include those considering cutting their hair short, adding bangs, changing hair color, doing balayage, or getting highlights; both men and women can use it. Specific generation results vary depending on photo quality and hairstyle direction. The official recommendation is to use a clear photo in which the face is recognizable and the current hair is visible.

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Vision-R1

AI developer platforms

Vision-R1 is a project for enhancing the reasoning capabilities of multimodal large language models (MLLMs), proposed and open-sourced by the relevant research team. It is the first work to explore how to effectively apply R1-like reinforcement learning to multimodal large language models, incentivizing the model to produce reasoning capabilities through a combination of cold-start initialization and reinforcement learning training. The project provides the cold-start dataset Vision-R1-cold, the reinforcement learning dataset Vision-R1-rl, and model weights of multiple scales (Vision-R1-CI-7B, Vision-R1-7B, Vision-R1-32B, Vision-R1-72B), and has open-sourced the inference code, evaluation code, and training scripts. This work has been accepted by ICLR 2026.

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Visual-RFT

AI developer platforms

Visual-RFT is the official code repository for Visual Reinforcement Fine-Tuning, and the first work to fully adapt DeepSeek-R1's reinforcement learning strategy to the multimodal domain. Using Qwen2-VL-2/7B as the base model, it designs rule-based verifiable rewards and integrates them into a GRPO-based reinforcement fine-tuning framework to improve the performance of large vision-language models on a variety of visual perception tasks. This work has been accepted by ICCV 2025, and the training code, training data, and evaluation scripts have been open-sourced. The follow-up work Visual-ARFT further endows open-source LVLMs with agentic capabilities such as browsing websites and writing code to process images, and proposes the multimodal agent tool benchmark MAT.

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Visualizing Data using t-SNE

Other AI tools

t-SNE is a technique for visualizing high-dimensional data, proposed by Laurens van der Maaten and Geoffrey Hinton in 2008 in the paper "Visualizing Data using t-SNE" published in the Journal of Machine Learning Research. This method assigns a position to each data point in a two-dimensional or three-dimensional map, thereby presenting the structural relationships of high-dimensional data in a visual way. It is a variant of Stochastic Neighbor Embedding; compared with the original method, it is easier to optimize and significantly reduces the tendency of data points to cluster in the center of the visualization result, thus producing images of better quality. t-SNE is superior to existing non-parametric visualization techniques at the time, such as Sammon mapping, Isomap, and Locally Linear Embedding, in constructing a single visualization map to reveal data structure at multiple different scales. The paper points out that this property is especially important for high-dimensional data distributed on multiple different but related low-dimensional manifolds, such as images of multi-class objects observed from multiple viewpoints. For very large datasets, the paper shows how t-SNE can use random walks on a neighborhood graph so that the implicit structure of all the data influences the way a subset is displayed. The paper demonstrates the performance of t-SNE on a variety of datasets and compares it with a variety of non-parametric visualization techniques; on almost all datasets, the visualizations generated by t-SNE are clearly better.

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