Posts tagged video in 2024
Filters: Year: 2024 × video × Sorted by date
Sora (via) OpenAI's released their long-threatened Sora text-to-video model this morning, available in most non-European countries to subscribers to ChatGPT Plus ($20/month) or Pro ($200/month).
Here's what I got for the very first test prompt I ran through it:
A pelican riding a bicycle along a coastal path overlooking a harbor
The Pelican inexplicably morphs to cycle in the opposite direction half way through, but I don't see that as a particularly significant issue: Sora is built entirely around the idea of directly manipulating and editing and remixing the clips it generates, so the goal isn't to have it produce usable videos from a single prompt.
How developers are using Gemini 1.5 Pro’s 1 million token context window. I got to be a talking head for a few seconds in an intro video for today's Google I/O keynote, talking about how I used Gemini Pro 1.5 to index my bookshelf (and with a cameo from my squirrel nutcracker). I'm at 1m25s.
(Or at 10m6s in the full video of the keynote)
When I first published the micrograd repo, it got some traction on GitHub but then somewhat stagnated and it didn't seem that people cared much. [...] When I made the video that built it and walked through it, it suddenly almost 100X'd the overall interest and engagement with that exact same piece of code.
[...] you might be leaving somewhere 10-100X of the potential of that exact same piece of work on the table just because you haven't made it sufficiently accessible.
Val Town Newsletter 15 (via) I really like how Val Town founder Steve Krouse now accompanies their “what’s new” newsletter with a video tour of the new features. I’m seriously considering imitating this for my own projects.
Google Research: Lumiere. The latest in text-to-video from Google Research, described as “a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion”.
Most existing text-to-video models generate keyframes and then use other models to fill in the gaps, which frequently leads to a lack of coherency. Lumiere “generates the full temporal duration of the video at once”, which avoids this problem.
Disappointingly but unsurprisingly the paper doesn’t go into much detail on the training data, beyond stating “We train our T2V model on a dataset containing 30M videos along with their text caption. The videos are 80 frames long at 16 fps (5 seconds)”.
The examples of “stylized generation” which combine a text prompt with a single reference image for style are particularly impressive.