Search Results for author:
Found 19 papers, 9 papers with code
TexRO: Generating Delicate Textures of 3D Models by Recursive Optimization
We propose an optimal viewpoint selection strategy, that finds the most miniature set of viewpoints covering all the faces of a mesh.
GGRt: Towards Pose-free Generalizable 3D Gaussian Splatting in Real-time
Specifically, we design a novel joint learning framework that consists of an Iterative Pose Optimization Network (IPO-Net) and a Generalizable 3D-Gaussians (G-3DG) model.
GVA: Reconstructing Vivid 3D Gaussian Avatars from Monocular Videos
In this paper, we present a novel method that facilitates the creation of vivid 3D Gaussian avatars from monocular video inputs (GVA).
GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization
This paper presents GIR, a 3D Gaussian Inverse Rendering method for relightable scene factorization.
Accelerating Vision Transformers Based on Heterogeneous Attention Patterns
On one hand, different images share more similar attention patterns in early layers than later layers, indicating that the dynamic query-by-key self-attention matrix may be replaced with a static self-attention matrix in early layers.
VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation
In this paper, we present VideoGen, a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion.
HD-Fusion: Detailed Text-to-3D Generation Leveraging Multiple Noise Estimation
In this paper, we study Text-to-3D content generation leveraging 2D diffusion priors to enhance the quality and detail of the generated 3D models.
StyleSync: High-Fidelity Generalized and Personalized Lip Sync in Style-based Generator
Despite recent advances in syncing lip movements with any audio waves, current methods still struggle to balance generation quality and the model's generalization ability.
PSVT: End-to-End Multi-person 3D Pose and Shape Estimation with Progressive Video Transformers
To handle the variances of objects as time proceeds, a novel scheme of progressive decoding is used to update pose and shape queries at each frame.
Ranked #23 on
3D Human Pose Estimation
on 3DPW
Graph Contrastive Learning for Skeleton-based Action Recognition
In this paper, we propose a graph contrastive learning framework for skeleton-based action recognition (\textit{SkeletonGCL}) to explore the \textit{global} context across all sequences.
Ranked #9 on
Skeleton Based Action Recognition
on NTU RGB+D
Cyclically Disentangled Feature Translation for Face Anti-spoofing
We further extend CDFTN for multi-target domain adaptation by leveraging data from more unlabeled target domains.
Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sampling
In this paper, we focus on the compression of DETR with knowledge distillation.
Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining
The training process consists of self-supervised pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the detector on Object365, and finally finetuning it on COCO.
Ranked #8 on
Object Detection
on COCO test-dev
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
Recently, transformer-based networks have shown impressive results in semantic segmentation.
Ranked #2 on
Real-Time Semantic Segmentation
on CamVid
Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment
Detection transformer (DETR) relies on one-to-one assignment, assigning one ground-truth object to one prediction, for end-to-end detection without NMS post-processing.
UFO: Unified Feature Optimization
UFO aims to benefit each single task with a large-scale pretraining on all tasks.
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
In this paper, we rethink the paradigm and explore a new regime: {\em fine-tuning a small part of parameters in the backbone}.
Ranked #8 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Dynamic Class Queue for Large Scale Face Recognition In the Wild
Since only a subset of classes is selected for each iteration, the computing requirement is reduced.
Ranked #3 on
Face Recognition
on AgeDB-30
Learning Generalized Spoof Cues for Face Anti-spoofing
In this paper, we reformulate FAS in an anomaly detection perspective and propose a residual-learning framework to learn the discriminative live-spoof differences which are defined as the spoof cues.
