Loading video...

Video Failed to Load

Go Home

Introducing our newest tool from SideFX Labs, Regions from Image! Section out an input file into major shapes based off of dominant image colors, and use these shapes for masks, polygon extraction, and more! #Houdini SideFX #techart

14,599 views • 2 years ago •via X (Twitter)

3 Comments

Kitae Kim's profile picture
Kitae Kim2 years ago

@sidefxlabs @sidefx I love @sidefxlabs so much

Paul Esteves's profile picture
Paul Esteves2 years ago

@sidefxlabs @sidefx Very cool video! Lovely to see the tool and also a usecase!

vincent thomas's profile picture
vincent thomas2 years ago

@sidefxlabs @sidefx Not sure why you are going by Cop context which is terribly slow and not stable. You could basicely do the same thing in less than 10 nodes and 5 min setup but well make lifer easier i guess for many!

Related Videos

InstantDrag Improving Interactivity in Drag-based Image Editing discuss: Drag-based image editing has recently gained popularity for its interactivity and precision. However, despite the ability of text-to-image models to generate samples within a second, drag editing still lags behind due to the challenge of accurately reflecting user interaction while maintaining image content. Some existing approaches rely on computationally intensive per-image optimization or intricate guidance-based methods, requiring additional inputs such as masks for movable regions and text prompts, thereby compromising the interactivity of the editing process. We introduce InstantDrag, an optimization-free pipeline that enhances interactivity and speed, requiring only an image and a drag instruction as input. InstantDrag consists of two carefully designed networks: a drag-conditioned optical flow generator (FlowGen) and an optical flow-conditioned diffusion model (FlowDiffusion). InstantDrag learns motion dynamics for drag-based image editing in real-world video datasets by decomposing the task into motion generation and motion-conditioned image generation. We demonstrate InstantDrag's capability to perform fast, photo-realistic edits without masks or text prompts through experiments on facial video datasets and general scenes. These results highlight the efficiency of our approach in handling drag-based image editing, making it a promising solution for interactive, real-time applications.

AK

71,228 views • 1 year ago