Search Results for author:
Found 27 papers, 6 papers with code
CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians
The field of 3D reconstruction from images has rapidly evolved in the past few years, first with the introduction of Neural Radiance Field (NeRF) and more recently with 3D Gaussian Splatting (3DGS).
Garment3DGen: 3D Garment Stylization and Texture Generation
We present a plethora of quantitative and qualitative comparisons on various assets both real and generated and provide use-cases of how one can generate simulation-ready 3D garments.
Towards Image Ambient Lighting Normalization
However, existing works often simplify this task within the context of shadow removal, limiting the light sources to one and oversimplifying the scene, thus excluding complex self-shadows and restricting surface classes to smooth ones.
MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction
MVDiffusion++ achieves superior flexibility and scalability with two surprisingly simple ideas: 1) A ``pose-free architecture'' where standard self-attention among 2D latent features learns 3D consistency across an arbitrary number of conditional and generation views without explicitly using camera pose information; and 2) A ``view dropout strategy'' that discards a substantial number of output views during training, which reduces the training-time memory footprint and enables dense and high-resolution view synthesis at test time.
Key-Graph Transformer for Image Restoration
While it is crucial to capture global information for effective image restoration (IR), integrating such cues into transformer-based methods becomes computationally expensive, especially with high input resolution.
Geometry Transfer for Stylizing Radiance Fields
Shape and geometric patterns are essential in defining stylistic identity.
SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity
In this paper, we reveal that the gradient estimation in score distillation is inherent to high variance.
Taming Mode Collapse in Score Distillation for Text-to-3D Generation
In this paper, we reveal that the existing score distillation-based text-to-3D generation frameworks degenerate to maximal likelihood seeking on each view independently and thus suffer from the mode collapse problem, manifesting as the Janus artifact in practice.
PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar
3D reconstruction from a single-view is challenging because of the ambiguity from monocular cues and lack of information about occluded regions.
Training Neural Networks on RAW and HDR Images for Restoration Tasks
Our results indicate that neural networks train significantly better on HDR and RAW images represented in display-encoded color spaces, which offer better perceptual uniformity than linear spaces.
MoVideo: Motion-Aware Video Generation with Diffusion Models
While recent years have witnessed great progress on using diffusion models for video generation, most of them are simple extensions of image generation frameworks, which fail to explicitly consider one of the key differences between videos and images, i. e., motion.
NOVIS: A Case for End-to-End Near-Online Video Instance Segmentation
Until recently, the Video Instance Segmentation (VIS) community operated under the common belief that offline methods are generally superior to a frame by frame online processing.
Ranked #5 on
Video Instance Segmentation
on YouTube-VIS validation
(using extra training data)
MMG-Ego4D: Multi-Modal Generalization in Egocentric Action Recognition
In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG).
Learning Neural Duplex Radiance Fields for Real-Time View Synthesis
Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual quality.
AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation
To apply optical flow in practice, it is often necessary to resize the input to smaller dimensions in order to reduce computational costs.
Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration.
Ranked #1 on
Image Defocus Deblurring
on DPD (Dual-view)
MMG-Ego4D: Multimodal Generalization in Egocentric Action Recognition
In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG).
FSID: Fully Synthetic Image Denoising via Procedural Scene Generation
To address this, we have developed a procedural synthetic data generation pipeline and dataset tailored to low-level vision tasks.
Fast Point Cloud Generation with Straight Flows
We perform evaluations on multiple 3D tasks and find that our PSF performs comparably to the standard diffusion model, outperforming other efficient 3D point cloud generation methods.
Consistent Direct Time-of-Flight Video Depth Super-Resolution
We believe the methods and dataset are beneficial to a broad community as dToF depth sensing is becoming mainstream on mobile devices.
Recurrent Video Restoration Transformer with Guided Deformable Attention
Specifically, RVRT divides the video into multiple clips and uses the previously inferred clip feature to estimate the subsequent clip feature.
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars
Compared with previous methods, our network can effectively handle the misalignment between the input and the reference without requiring facial priors and learn the aggregated reference set representation in an end-to-end manner.
Learning Spatio-Temporal Downsampling for Effective Video Upscaling
Consequently, the inverse task of upscaling a low-resolution, low frame-rate video in space and time becomes a challenging ill-posed problem due to information loss and aliasing artifacts.
VRT: A Video Restoration Transformer
Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping.
Ranked #1 on
Deblurring
on BASED
EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images
To overcome this, we propose Eye Authentication with PAD (EyePAD), a distillation-based method that trains a single network for EA and PAD while reducing the effect of forgetting.
Feature-Align Network with Knowledge Distillation for Efficient Denoising
(2) A novel Feature Matching Loss that allows knowledge distillation from large denoising networks in the form of a perceptual content loss.
