Search Results for author:
Found 16 papers, 8 papers with code
Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance
In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in curernt human generative techniques.
EffiVED:Efficient Video Editing via Text-instruction Diffusion Models
Large-scale text-to-video models have shown remarkable abilities, but their direct application in video editing remains challenging due to limited available datasets.
Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle
Moreover, the explicit deformation modeling for discretized Gaussian points ensures ultra-fast training and rendering of a 4D scene, which is comparable to the original 3DGS designed for static 3D reconstruction.
AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidance
Image animation is a key task in computer vision which aims to generate dynamic visual content from static image.
Fine-grained Text-Video Retrieval with Frozen Image Encoders
In the second stage, we propose a novel decoupled video text cross attention module to capture fine-grained multimodal information in spatial and temporal dimensions.
UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model
Unsupervised video object segmentation has made significant progress in recent years, but the manual annotation of video mask datasets is expensive and limits the diversity of available datasets.
Towards Robust Video Instance Segmentation with Temporal-Aware Transformer
In this paper, we observe the temporal information is important as well and we propose TAFormer to aggregate spatio-temporal features both in transformer encoder and decoder.
RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds
In the experiments, we evaluate the proposed method on both the 3D scene flow estimation and the point cloud registration task.
NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation
While recent works design increasingly complicated and powerful networks to directly regress the depth map, we take the path of CRFs optimization.
Ranked #1 on
Depth Prediction
on Matterport3D
RCP: Recurrent Closest Point for Point Cloud
In the experiments, we evaluate the proposed method on both the 3D scene flow estimation and the point cloud registration task.
Neural Window Fully-Connected CRFs for Monocular Depth Estimation
Estimating the accurate depth from a single image is challenging since it is inherently ambiguous and ill-posed.
DRO: Deep Recurrent Optimizer for Video to Depth
There are increasing interests of studying the video-to-depth (V2D) problem with machine learning techniques.
Cluster Contrast for Unsupervised Person Re-Identification
Thus, our method can solve the problem of cluster inconsistency and be applicable to larger data sets.
Ranked #1 on
Unsupervised Person Re-Identification
on PersonX
MeshMVS: Multi-View Stereo Guided Mesh Reconstruction
Deep learning based 3D shape generation methods generally utilize latent features extracted from color images to encode the semantics of objects and guide the shape generation process.
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity.
Ranked #12 on
Point Clouds
on Tanks and Temples
Batch DropBlock Network for Person Re-identification and Beyond
In this paper, we propose the Batch DropBlock (BDB) Network which is a two branch network composed of a conventional ResNet-50 as the global branch and a feature dropping branch.
Ranked #8 on
Person Re-Identification
on Market-1501-C
