Search Results for author:
Found 9 papers, 7 papers with code
MemFlow: Optical Flow Estimation and Prediction with Memory
To this end, we present MemFlow, a real-time method for optical flow estimation and prediction with memory.
Repositioning the Subject within Image
Our research reveals that the fundamental sub-tasks of subject repositioning, which include filling the void left by the repositioned subject, reconstructing obscured portions of the subject and blending the subject to be consistent with surrounding areas, can be effectively reformulated as a unified, prompt-guided inpainting task.
Open-DDVM: A Reproduction and Extension of Diffusion Model for Optical Flow Estimation
Recently, Google proposes DDVM which for the first time demonstrates that a general diffusion model for image-to-image translation task works impressively well on optical flow estimation task without any specific designs like RAFT.
LeftRefill: Filling Right Canvas based on Left Reference through Generalized Text-to-Image Diffusion Model
As an exemplar, we leverage LeftRefill to address two different challenges: reference-guided inpainting and novel view synthesis, based on the pre-trained StableDiffusion.
Rethinking Optical Flow from Geometric Matching Consistent Perspective
In this paper, we propose a rethinking to previous optical flow estimation.
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction
Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction.
ZITS++: Image Inpainting by Improving the Incremental Transformer on Structural Priors
Specifically, given one corrupt image, we present the Transformer Structure Restorer (TSR) module to restore holistic structural priors at low image resolution, which are further upsampled by Simple Structure Upsampler (SSU) module to higher image resolution.
Learning Prior Feature and Attention Enhanced Image Inpainting
To this end, this paper incorporates the pre-training based Masked AutoEncoder (MAE) into the inpainting model, which enjoys richer informative priors to enhance the inpainting process.
Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding
The proposed model restores holistic image structures with a powerful attention-based transformer model in a fixed low-resolution sketch space.
