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Found 10 papers, 9 papers with code
XCube ($\mathcal{X}^3$): Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
In addition to unconditional generation, we show that our model can be used to solve a variety of tasks such as user-guided editing, scene completion from a single scan, and text-to-3D.
Blind Video Deflickering by Neural Filtering with a Flawed Atlas
Prior work usually requires specific guidance such as the flickering frequency, manual annotations, or extra consistent videos to remove the flicker.
Look Outside the Room: Synthesizing A Consistent Long-Term 3D Scene Video from A Single Image
Novel view synthesis from a single image has recently attracted a lot of attention, and it has been primarily advanced by 3D deep learning and rendering techniques.
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
Last, an innovative link attention module serves as the decoder to reconstruct data from the decomposed content and style, with the help of the linking keys.
Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving
The ability to predict unseen vehicles is critical for safety in autonomous driving.
Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement
From the unsupervised disentanglement perspective, we rethink content and style and propose a formulation for unsupervised C-S disentanglement based on our assumption that different factors are of different importance and popularity for image reconstruction, which serves as a data bias.
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View
Based on this observation, we argue that it is possible to mitigate the trade-off by $(i)$ leveraging the pretrained generative models with high generation quality, $(ii)$ focusing on discovering the traversal directions as factors for disentangled representation learning.
Towards Building A Group-based Unsupervised Representation Disentanglement Framework
We then propose a model, based on existing VAE-based methods, to tackle the unsupervised learning problem of the framework.
Video Deblurring by Fitting to Test Data
Our key observation is that some frames in a video with motion blur are much sharper than others, and thus we can transfer the texture information in those sharp frames to blurry frames.
Music-oriented Dance Video Synthesis with Pose Perceptual Loss
We present a learning-based approach with pose perceptual loss for automatic music video generation.
