118 citations · 193 across the 18 of their papers we have counts for
19 papers
ATGS: Anchored Temporal Gaussian Splatting for Long Volumetric Video Representation
Jiahao Wu, Jie Liang, Die Hu +6
Volumetric video enables immersive free viewpoint rendering of dynamic real world scenes, yet existing methods struggle with long sequences and complex motions, often leading to te…
ClipGStream: Clip-Stream Gaussian Splatting for Any Length and Any Motion Multi-View Dynamic Scene Reconstruction
Jie Liang, Jiahao Wu, Chao Wang +7
Dynamic 3D scene reconstruction is essential for immersive media such as VR, MR, and XR, yet remains challenging for long multi-view sequences with large-scale motion. Existing dyn…
LocalDyGS: Multi-view Global Dynamic Scene Modeling via Adaptive Local Implicit Feature Decoupling
Jiahao Wu, Rui Peng, Jianbo Jiao +7
Due to the complex and highly dynamic motions in the real world, synthesizing dynamic videos from multi-view inputs for arbitrary viewpoints is challenging. Previous works based on…
L-LBVC: Long-Term Motion Estimation and Prediction for Learned Bi-Directional Video Compression
Yongqi Zhai, Luyang Tang, Wei Jiang +2
Recently, learned video compression (LVC) has shown superior performance under low-delay configuration. However, the performance of learned bi-directional video compression (LBVC)…
MLICv2: Enhanced Multi-Reference Entropy Modeling for Learned Image Compression
Wei Jiang, Yongqi Zhai, Jiayu Yang +2
Recent advances in learned image compression (LIC) have achieved remarkable performance improvements over traditional codecs. Notably, the MLIC series-LICs equipped with multi-refe…
Enhancing 3D Gaussian Splatting Compression via Spatial Condition-based Prediction
Jingui Ma, Yang Hu, Luyang Tang +3
Recently, 3D Gaussian Spatting (3DGS) has gained widespread attention in Novel View Synthesis (NVS) due to the remarkable real-time rendering performance. However, the substantial…
Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting
Jinbo Yan, Rui Peng, Zhiyan Wang +5
Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to offline training methods, greatly enhancing user experience. However,…
Hybrid Local-Global Context Learning for Neural Video Compression
Yongqi Zhai, Jiayu Yang, Wei Jiang +3
In neural video codecs, current state-of-the-art methods typically adopt multi-scale motion compensation to handle diverse motions. These methods estimate and compress either optic…
UCVC: A Unified Contextual Video Compression Framework with Joint P-frame and B-frame Coding
Jiayu Yang, Wei Jiang, Yongqi Zhai +2
This paper presents a learned video compression method in response to video compression track of the 6th Challenge on Learned Image Compression (CLIC), at DCC 2024.Specifically, we…
Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's Eye View
Jiayu Yang, Enze Xie, Miaomiao Liu +1
Recent vision-only perception models for autonomous driving achieved promising results by encoding multi-view image features into Bird's-Eye-View (BEV) space. A critical step and t…
MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression
Wei Jiang, Jiayu Yang, Yongqi Zhai +2
The latent representation in learned image compression encompasses channel-wise, local spatial, and global spatial correlations, which are essential for the entropy model to captur…
LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression
Wei Jiang, Peirong Ning, Jiayu Yang +3
The effective receptive field (ERF) plays an important role in transform coding, which determines how much redundancy can be removed during transform and how many spatial priors ca…
Butterfly: Multiple Reference Frames Feature Propagation Mechanism for Neural Video Compression
Feng Wang, Haihang Ruan, Fei Xiong +3
Using more reference frames can significantly improve the compression efficiency in neural video compression. However, in low-latency scenarios, most existing neural video compress…
MLIC: Multi-Reference Entropy Model for Learned Image Compression
Wei Jiang, Jiayu Yang, Yongqi Zhai +3
Recently, learned image compression has achieved remarkable performance. The entropy model, which estimates the distribution of the latent representation, plays a crucial role in b…
Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo
Jiayu Yang, Jose M. Alvarez, Miaomiao Liu
Recent cost volume pyramid based deep neural networks have unlocked the potential of efficiently leveraging high-resolution images for depth inference from multi-view stereo. In ge…
Self-supervised Learning of Depth Inference for Multi-view Stereo
Jiayu Yang, Jose M. Alvarez, Miaomiao Liu
Recent supervised multi-view depth estimation networks have achieved promising results. Similar to all supervised approaches, these networks require ground-truth data during traini…
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
Ren Yang, Radu Timofte, Jing Liu +69
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results. In this challenge, the new Large-scale Di…
Super-Resolving Compressed Video in Coding Chain
Dewang Hou, Yang Zhao, Yuyao Ye +3
Scaling and lossy coding are widely used in video transmission and storage. Previous methods for enhancing the resolution of such videos often ignore the inherent interference betw…
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo
Jiayu Yang, Wei Mao, Jose M. Alvarez +1
We propose a cost volume-based neural network for depth inference from multi-view images. We demonstrate that building a cost volume pyramid in a coarse-to-fine manner instead of c…