Discover and read the best of Twitter Threads about #depth2img
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I wanted to imagine how we’d better use #stablediffusion for video content / AR.
A major obstacle, why most videos are so flickery, is lack of temporal & viewing angle consistency, so I experimented with an approach to fix this
See 🧵 for process & examples
A major obstacle, why most videos are so flickery, is lack of temporal & viewing angle consistency, so I experimented with an approach to fix this
See 🧵 for process & examples
Ideally you want to learn a single representation of an object across time or different viewing directions to perform a *single* #img2img generation on.
For this I used layered-neural-atlases.github.io (2021)
For this I used layered-neural-atlases.github.io (2021)
This learns an "atlas" to represent an object and its background across the video.
Regularization losses during training help preserve the original shape, with a result that resembles a usable slightly "unwrapped" version of the object
Regularization losses during training help preserve the original shape, with a result that resembles a usable slightly "unwrapped" version of the object
Used #stablediffusion2 #depth2img model to render a more photoreal layer ontop of a walking animation I made in #UnrealEngine5 with #realtime clothing and hair on a #daz model. Breakdown thread 1/6 @UnrealEngine @daz3d #aiart #MachineLearning #aiartcommunity #aiartprocess #aiia
Reused this animation I made from a post from a few months ago. Rendered in #UE5 @UnrealEngine using a @daz3d model with #realtime clothing in the @triMirror plugin. Walk was from the #daz store. Hair was from the Epic Marketplace. 2/6 #aiartprocess
Used SD2’s #depth2img model running locally in Automatic1111. Thanks to @TomLikesRobots for the help getting it working! And showing how the model retains more consistency than normal img2img. I basically did an img2img batch process on the image sequence. 3/6 #aiartprocess
