الصفحة 1
الصفحة 1
Introduction to blender 3.0 : Learn organic and architectural modeling, lighting, materials, painting, rendering, and compositing with blender
Explains modeling, materials, lighting, painting, and more with Blender and other external tools. You will configure a 3D architectural environment and set up the workflow of an art and design project within Blender. You will use Blender's main tools—mesh modeling and sculpting—to create virtual objects and environments. And, you will explore building materials and light scenes, followed by drawing and virtual painting. Chapters cover rendering scenes and transforming them into 2D images or videos. You will learn to use Blender 3.0 for video editing as a compositor and video sequence editor (VSE or sequencer) with a wide range of effects available through the nodal system. You Will Learn : Create objects and architectural buildings with different techniques of 3D modeling / Master creating an environment for your objects and how to light them / Determine how to create node materials and assign them to your Blender objects / Pick up UV unwrapping and texture painting / Get closer to painting and drawing in Blender / Render your scenes and create stunning videos
Fadfada
Sharing personal problems and true feeling on social media has a lot of fears and is not likable nowadays, because of bullying, shaming, and making fun of the user by other users, Fadfada is a multi-platform social networking service, that aims to give the users the ability to share their problems or feeling via written newsfeed (posts) or videos (stories) that had an impact on their psychological health without revealing their true identity, by putting a mask on their faces and changing their voices after recording the story. In addition to extracting features from posts and stories to match users based on it.
Elevating video content creation with ai assistance = ارتقاء إنشاء محتوى الفيديو بمساعدة الذكاء الاصطناعي
We developed an AI Assistant equipped with features such as description crafting, title generation, keyword extraction, image captioning, clickbait detection, and sentiment analysis.To achieve these functionalities, we proposed a model for generating video descriptions using ResNet50 as a feature extractor and a LSTM network with an attention mechanism as a sequence generator, achieving a BLEU-1 score of 0.907 and a ROUGE-L score of 0.645. For keyword extraction, we utilized Sentence Transformer to identify strategically relevant keywords from the generated descriptions. For title generation, we fine-tuned the BART model, achieving a ROUGE-L score of 0.45. For clickbait detection, we used SVC classifier with linear kernel and TF-IDF vectorization for feature extraction, resulting in 96% accuracy. Our sentiment analysis model using a CNN-LSTM architecture achieved 80% accuracy in analyzing comments on videos. For image captioning, we employed a feature extractor with a CNN layer followed by an LSTM model, achieving a BLEU-1 score of 0.53. Our platform empowers creators by simplifying complex tasks and offering deeper audience engagement insights, making it a powerful tool in the evolving digital content creation.
Egocentric video summarization
Video summarization is defined as the generation of a summary of extensive video content that comes from all kinds of videos including egocentric videos by detecting and presenting the material to potential users which is most informative and contains interesting information. Video summarization has many practical applications and Egocentric video summarization approaches have been proposed to solve various problems in the healthcare industry. This work focuses on Alzheimer. Patients suffering from Alzheimer’s face difficulties in remembering what happened during their day, the identity of persons and medicine they took.
Dream catcher
Dream Catcher is a video generation application that helps in many fields as science fiction, imagine event’s scenarios, education, animation and montage. By applying artificial algorithms implemented and trained on a dataset containing video samples and there descriptions to generate videos from any given text. The idea of generating videos from text is a new idea that was first presented at 2017, even that international companies like Google and OpenAi In the last year, was working on developing models to generate images from text. To make it easier to use the application, there are many ways to enter the text either by an image, voice or Typing from the keyboard.
Deepfake detection
Recently, various techniques of manipulating the video content have become available to everyone – online, one can find free applications e.g., for face swapping in videos. Such universal accessibility carries a notable risk of flooding online content with false information, affecting not only the greats of this world, but also the whole societies, also the rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. It is therefore necessary to develop a verification tool that will help assess the authenticity of the videos posted on the internet. This project describes the approach of using artificial intelligence solutions to detect doctored videos.
Deepfake
The technology used to create such digital content has quickly become accessible to the masses, such as “DEEPFAKE.” Deep fakes refer to manipulated videos, or other digital representations produced by sophisticated artificial intelligence, that yields to synthesize a sequence of face images and voices of characters corresponding to their identities, such as voice tone, facial expression, with a good lip synchronization. Therefore, this study is about developing real-time video generation software, which generates a target video from a single input image. Several methods and algorithms have been applied to detect, analyze personalize facial expression, voice and natural head poses to present a life-like image instead of a low quality one.
Deep fake detection
Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is “deepfake”. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable.
Creating Cool MINDSTORMS® NXT Robots
Build and program MINDSTORM NXT robots with Daniele Benedettelli, one of the world's most respected NXT robot builders. He shows you how to build and program them from scratch, starting with the simplest robots and progressing in difficulty to a total of seven award–winning robots! You can download all the code, along with low–resolution videos that show how your robot works when it's finished. You don't need to be a programmer to develop these cool robots, because all the code is provided, but advanced developers will enjoy seeing the secrets of Benedettelli's code and techniques revealed.
Computer vision : Algorithms and applications
Explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.
Computer vision : Algorithms and applications
Explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.
Automatic video editor
Searching in a large database of videos is one of the challenges faced by the user today as most of the results are inaccurate or correct. In our project, we worked on developing a system that receives the search word from the user and searches for it among a large number of videos using MSR-VTT dataset and COCO data set based on the elements that we see inside the video. Entered by the user. We have also worked on adding other options that the user can benefit from in modifying the videos, such as entering a black and white video clip and returning the result in color. The user can also enter a low-resolution video clip, and the system improves the accuracy of the video and sends it.
