Search Results for author:
Found 14 papers, 9 papers with code
Towards Physical Understanding in Video Generation: A 3D Point Regularization Approach
Furthermore, our model improves the overall quality of video generation by promoting the 3D consistency of moving objects and reducing abrupt changes in shape and motion.
Wonderland: Navigating 3D Scenes from a Single Image
Specifically, we introduce a large-scale reconstruction model that uses latents from a video diffusion model to predict 3D Gaussian Splattings for the scenes in a feed-forward manner.
Lightweight Predictive 3D Gaussian Splats
Our key observation is that nearby points in the scene can share similar representations.
OpenBias: Open-set Bias Detection in Text-to-Image Generative Models
In this paper, we tackle the challenge of open-set bias detection in text-to-image generative models presenting OpenBias, a new pipeline that identifies and quantifies the severity of biases agnostically, without access to any precompiled set.
VASE: Object-Centric Appearance and Shape Manipulation of Real Videos
Recently, several works tackled the video editing task fostered by the success of large-scale text-to-image generative models.
PAIR Diffusion: A Comprehensive Multimodal Object-Level Image Editor
We propose PAIR Diffusion a generic framework that enables a diffusion model to control the structure and appearance properties of each object in the image.
Video Instance Matting
To remedy this deficiency, we propose Video Instance Matting~(VIM), that is, estimating alpha mattes of each instance at each frame of a video sequence.
Interactive Neural Painting
This paper advances the state of the art in this emerging research domain by proposing the first approach for Interactive NP.
PAIR-Diffusion: A Comprehensive Multimodal Object-Level Image Editor
We propose PAIR Diffusion, a generic framework that can enable a diffusion model to control the structure and appearance properties of each object in the image.
VMFormer: End-to-End Video Matting with Transformer
In this paper, we propose VMFormer: a transformer-based end-to-end method for video matting.
VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution
The learned implicit neural representation can be decoded to videos of arbitrary spatial resolution and frame rate.
Space-time Video Super-resolution
Video Frame Interpolation
+1
MSN: Efficient Online Mask Selection Network for Video Instance Segmentation
Our method improves the masks from segmentation and propagation branches in an online manner using the Mask Selection Network (MSN) hence limiting the noise accumulation during mask tracking.
Ranked #17 on
Video Instance Segmentation
on YouTube-VIS validation
Mask Selection and Propagation for Unsupervised Video Object Segmentation
We efficiently handle problems present in existing methods such as drift while temporal propagation, tracking and addition of new objects.
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams).
