HelloMeme for Video Memes
HelloMeme makes video memes
Paper: HelloMeme: Integrating Spatial Knitting Attentions to Embed High-Level and Fidelity-Rich Conditions in Diffusion Models (11 Pages)
Github: https://github.com/HelloVision/HelloMeme
Researchers from HelloGroup Inc. are interested in development of better models end to end generation for memes.
Hmm..What’s the background?
The goal was to develop a system for creating meme videos, similar to video-driven portrait animation but with unique challenges.
Highly exaggerated facial expressions and head poses in meme images or videos
The need to scale the technology to accommodate half-body or full-body compositions
Preservation of the text-to-image foundation model's generalization ability to utilize Stable Diffusion's customization options for diverse content generation
So what is proposed in the research paper?
The authors combined the strengths of 3D face models and aligned facial feature bitmaps as conditions for strong performance in meme video generation
They built their solution on the Stable Diffusion 1.5 model due to its early adoption, moderate computational demands, strong performance, and an extensive open-source ecosystem
HelloMeme maintains full compatibility with SD1.5-derived models because, similar to Animatediff, it only optimizes the inserted adapter's parameters, leaving the SD1.5 UNet weights untouched
The Spatial Knitting Attentions (SK Attentions) mechanism effectively preserves the structural information within the 2D feature map, which prevents the neural network from having to relearn this concept
Here are some results
What’s next?
The authors acknowledge that while they employed a two-stage method, the generated videos' frame continuity still falls short of GAN-based solutions.
HelloMeme makes video memes
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