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Found 5 papers, 1 papers with code
Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models
With the emergence of text-to-video (T2V) diffusion models, its temporal counterpart, motion customization, has not yet been well investigated.
Towards Scalable Neural Representation for Diverse Videos
Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e. g., NeRV, E-NeRV).
NeRV: Neural Representations for Videos
In contrast, with NeRV, we can use any neural network compression method as a proxy for video compression, and achieve comparable performance to traditional frame-based video compression approaches (H. 264, HEVC \etc).
Ranked #6 on
Video Reconstruction
on UVG
StEP: Style-based Encoder Pre-training for Multi-modal Image Synthesis
Specifically, we pre-train a generic style encoder using a novel proxy task to learn an embedding of images, from arbitrary domains, into a low-dimensional style latent space.
Style-based Encoder Pre-training for Multi-modal Image Synthesis
Then we train a generator to transform an input image along with a style-code to the output domain.
