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Found 13 papers, 5 papers with code
GenN2N: Generative NeRF2NeRF Translation
We present GenN2N, a unified NeRF-to-NeRF translation framework for various NeRF translation tasks such as text-driven NeRF editing, colorization, super-resolution, inpainting, etc.
Diffusion Posterior Proximal Sampling for Image Restoration
Specifically, we opt for a sample consistent with the measurement identity at each generative step, exploiting the sampling selection as an avenue for output stability and enhancement.
Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraints
To address these problems, we present Ctrl-Room, which is able to generate convincing 3D rooms with designer-style layouts and high-fidelity textures from just a text prompt.
AccFlow: Backward Accumulation for Long-Range Optical Flow
To train and evaluate the proposed AccFlow, we have constructed a large-scale high-quality dataset named CVO, which provides ground-truth optical flow labels between adjacent and distant frames.
Fast and Stable Diffusion Inverse Solver with History Gradient Update
To address this issue, we propose to utilize the history information of the diffusion-based inverse solvers.
Learning Optical Flow from Event Camera with Rendered Dataset
Previous datasets are created by either capturing real scenes by event cameras or synthesizing from images with pasted foreground objects.
GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learning
Second, we design a self-guided fusion module (SGF) to fuse the background motion extracted from the gyro field with the optical flow and guide the network to focus on motion details.
RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos
Specifically, we first estimate optical flow between a pair of video frames, and then synthesize a new image from this pair based on the predicted flow.
Learning Optical Flow with Adaptive Graph Reasoning
Our key idea is to decouple the context reasoning from the matching procedure, and exploit scene information to effectively assist motion estimation by learning to reason over the adaptive graph.
ASFlow: Unsupervised Optical Flow Learning with Adaptive Pyramid Sampling
Equipped with these two modules, our method achieves the best performance for unsupervised optical flow estimation on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning
Experiments show that our method outperforms the state-of-art methods in both regular and challenging scenes.
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning
By integrating these two components together, our method achieves the best performance for unsupervised optical flow learning on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
Ranked #1 on
Optical Flow Estimation
on Sintel Final unsupervised
OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning
Occlusion is an inevitable and critical problem in unsupervised optical flow learning.
