Search Results for author:
Found 19 papers, 16 papers with code
AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars
Our key insight is to take advantage of the powerful vision-language model CLIP for supervising neural human generation, in terms of 3D geometry, texture and animation.
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling
4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications.
Neural MoCon: Neural Motion Control for Physically Plausible Human Motion Capture
Our key-idea is to use real physical supervisions to train a target pose distribution prior for sampling-based motion control to capture physically plausible human motion.
Versatile Multi-Modal Pre-Training for Human-Centric Perception
To tackle the challenges, we design the novel Dense Intra-sample Contrastive Learning and Sparse Structure-aware Contrastive Learning targets by hierarchically learning a modal-invariant latent space featured with continuous and ordinal feature distribution and structure-aware semantic consistency.
TCTrack: Temporal Contexts for Aerial Tracking
Temporal contexts among consecutive frames are far from being fully utilized in existing visual trackers.
Benchmarking and Analyzing Point Cloud Classification under Corruptions
3D perception, especially point cloud classification, has achieved substantial progress.
Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.
Garment4D: Garment Reconstruction from Point Cloud Sequences
The main challenges are two-fold: 1) effective 3D feature learning for fine details, and 2) capture of garment dynamics caused by the interaction between garments and the human body, especially for loose garments like skirts.
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
The RFNet refines the coarse output of the CGNet and further improves quality of the completed point cloud.
PTTR: Relational 3D Point Cloud Object Tracking with Transformer
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud.
Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
We adopt DCD to evaluate the point cloud completion task, where experimental results show that DCD pays attention to both the overall structure and local geometric details and provides a more reliable evaluation even when CD and EMD contradict each other.
Robust Partial-to-Partial Point Cloud Registration in a Full Range
\textbf{3)} Based on a synergy of hierarchical graph networks and graphical modeling, we propose the {H}ierarchical {G}raphical {M}odeling (\textbf{HGM}) architecture to encode robust descriptors consisting of i) a unary term learned from {\textit{RI}} features; and ii) multiple smoothness terms encoded from neighboring point relations at different scales through our TPT modules.
Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
We adopt DCD to evaluate the point cloud completion task, where experimental results show that DCD pays attention to both the overall structure and local geometric details and provides a more reliable evaluation even when CD and EMD contradict each other.
TAda! Temporally-Adaptive Convolutions for Video Understanding
This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modelling complex temporal dynamics in videos.
Ranked #31 on
Action Recognition
on Something-Something V2
(using extra training data)
CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing
Generating an interpretable and compact representation of 3D shapes from point clouds is an important and challenging problem.
Unsupervised 3D Shape Completion through GAN Inversion
In contrast to previous fully supervised approaches, in this paper we present ShapeInversion, which introduces Generative Adversarial Network (GAN) inversion to shape completion for the first time.
Variational Relational Point Completion Network
In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds.
Robust 6D Object Pose Estimation by Learning RGB-D Features
Accurate 6D object pose estimation is fundamental to robotic manipulation and grasping.
PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
Motivated by the success of encoding multi-scale contextual information for image analysis, we propose our PointAtrousGraph (PAG) - a deep permutation-invariant hierarchical encoder-decoder for efficiently exploiting multi-scale edge features in point clouds.
