Truly Infinite Videos This isn’t a gimmick. You can generate incredibly long videos without frying your VRAM. Perfect for podcasts, presentations, or full-on virtual influencers.
More Than Just Lips This is the best part. It doesn’t just sync the mouth; it generates realistic head movements, body posture, and facial expressions that match the audio’s emotion. It makes characters feel alive.
Keeps Everything Consistent It preserves the character’s identity, the background, and even camera movements from your original video, so everything looks seamless.
Completely Open Source & Ready for Business The code, the weights, and the paper are all out there for you to use. Best of all, it’s released under an Apache 2.0 license, which means you are free to use what you create for commercial projects!
# 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
MiniMax-Remover is a fast and effective video object remover based on minimax optimization. It operates in two stages: the first stage trains a remover using a simplified DiT architecture, while the second stage distills a robust remover with CFG removal and fewer inference steps.
Basically, gamma is the relationship between the brightness of a pixel as it appears on the screen, and the numerical value of that pixel. Generally Gamma is just about defining relationships.
Three main types: – Image Gamma encoded in images – Display Gammas encoded in hardware and/or viewing time – System or Viewing Gamma which is the net effect of all gammas when you look back at a final image. In theory this should flatten back to 1.0 gamma.
