# extract one frame at the end of a video ffmpeg -sseof -0.1 -i intro_1.mp4 -frames:v 1 -q:v 1 intro_end.jpg
-sseof -0.1: This option tells FFmpeg to seek to 0.1 seconds before the end of the file. This approach is often more reliable for extracting the last frame, especially if the video’s duration isn’t an exact multiple of the frame interval. Super User -frames:v 1: Extracts a single frame. -q:v 1: Sets the quality of the output image; 1 is the highest quality.
# extract one frame at the beginning of a video ffmpeg -i speaking_4.mp4 -frames:v 1 speaking_beginning.jpg
# check video length ffmpeg -i C:\myvideo.mp4 -f null –
# Convert mov/mp4 to animated gifEdit ffmpeg -i input.mp4 -pix_fmt rgb24 output.gif Other useful ffmpeg commandsEdit
There’s been no statements as to when Midjourney’s technology will start showing up in Meta’s products, or to what degree it will be baked into the company’s AI strategy.
Tired of having iTunes messing up your mp3 library? … Time to try MiniTunes!
– Arrange your library by Genre, Artists or Albums. – Change UI colors at will. – Edit tags and create playlists. – Consolidate your library once for all. – Windows 64 only
Qwen-Image-Edit is the image editing version of Qwen-Image. It is further trained based on the 20B Qwen-Image model, successfully extending Qwen-Image’s unique text rendering capabilities to editing tasks, enabling precise text editing. In addition, Qwen-Image-Edit feeds the input image into both Qwen2.5-VL (for visual semantic control) and the VAE Encoder (for visual appearance control), thus achieving dual semantic and appearance editing capabilities.
PixiEditor is a universal 2D editor that was made to provide you with tools and features for all your 2D needs. Create beautiful sprites for your games, animations, edit images, create logos. All packed up in an intuitive and familiar interface.
Deepfake technology is a type of artificial intelligence used to create convincing fake images, videos and audio recordings. The term describes both the technology and the resulting bogus content and is a portmanteau of deep learning and fake.
Deepfakes often transform existing source content where one person is swapped for another. They also create entirely original content where someone is represented doing or saying something they didn’t do or say.
Deepfakes aren’t edited or photoshopped videos or images. In fact, they’re created using specialized algorithms that blend existing and new footage. For example, subtle facial features of people in images are analyzed through machine learning (ML) to manipulate them within the context of other videos.
Deepfakes uses two algorithms — a generator and a discriminator — to create and refine fake content. The generator builds a training data set based on the desired output, creating the initial fake digital content, while the discriminator analyzes how realistic or fake the initial version of the content is. This process is repeated, enabling the generator to improve at creating realistic content and the discriminator to become more skilled at spotting flaws for the generator to correct.
The combination of the generator and discriminator algorithms creates a generative adversarial network.
A GANuses deep learning to recognize patterns in real images and then uses those patterns to create the fakes.
When creating a deepfake photograph, a GAN system views photographs of the target from an array of angles to capture all the details and perspectives. When creating a deepfake video, the GAN views the video from various angles and analyzes behavior, movement and speech patterns. This information is then run through the discriminator multiple times to fine-tune the realism of the final image or video.
🔸 Gaussian Splats: imagine throwing thousands of tiny ellipsoidal paint drops. They overlap, blend, and create a smooth, photorealistic look. Fast, great for visualization, but less structured for measurements.
🔸 Point Clouds: every dot is a measured hit. LiDAR or photogrammetry gives us millions of them forming a constellation of reality. Amazing for accuracy, but they don’t connect the dots out of the box.
🔸 Meshes: take those points, connect them into triangles, and you get very realistic surfaces. Strong for 3D analysis, simulation as continues watertight models.
To measure the contrast ratio you will need a light meter. The process starts with you measuring the main source of light, or the key light.
Get a reading from the brightest area on the face of your subject. Then, measure the area lit by the secondary light, or fill light. To make sense of what you have just measured you have to understand that the information you have just gathered is in F-stops, a measure of light. With each additional F-stop, for example going one stop from f/1.4 to f/2.0, you create a doubling of light. The reverse is also true; moving one stop from f/8.0 to f/5.6 results in a halving of the light.
