1.2k citations · 3k across the 74 of their papers we have counts for
15 papers · 1 filter
Speech2Lip: High-fidelity Speech to Lip Generation by Learning from a Short Video
Xiuzhe Wu, Pengfei Hu, Yang Wu +6
Synthesizing realistic videos according to a given speech is still an open challenge. Previous works have been plagued by issues such as inaccurate lip shape generation and poor im…
Texture Generation on 3D Meshes with Point-UV Diffusion
Xin Yu, Peng Dai, Wenbo Li +3
In this work, we focus on synthesizing high-quality textures on 3D meshes. We present Point-UV diffusion, a coarse-to-fine pipeline that marries the denoising diffusion model with…
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding
Runyu Ding, Jihan Yang, Chuhui Xue +3
Open-world instance-level scene understanding aims to locate and recognize unseen object categories that are not present in the annotated dataset. This task is challenging because…
MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds
Jiahui Liu, Chirui Chang, Jianhui Liu +3
3D semantic segmentation on multi-scan large-scale point clouds plays an important role in autonomous systems. Unlike the single-scan-based semantic segmentation task, this task re…
Decoupled Kullback-Leibler Divergence Loss
Jiequan Cui, Zhuotao Tian, Zhisheng Zhong +3
In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss…
Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur
Peng Dai, Yinda Zhang, Xin Yu +2
Rendering novel view images is highly desirable for many applications. Despite recent progress, it remains challenging to render high-fidelity and view-consistent novel views of la…
RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding
Jihan Yang, Runyu Ding, Weipeng Deng +2
We propose a lightweight and scalable Regional Point-Language Contrastive learning framework, namely \textbf{RegionPLC}, for open-world 3D scene understanding, aiming to identify a…
Context-Aware Transformer for 3D Point Cloud Automatic Annotation
Xiaoyan Qian, Chang Liu, Xiaojuan Qi +3
3D automatic annotation has received increased attention since manually annotating 3D point clouds is laborious. However, existing methods are usually complicated, e.g., pipelined…
Learning Context-aware Classifier for Semantic Segmentation
Zhuotao Tian, Jiequan Cui, Li Jiang +5
Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature dist…
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking
Yukang Chen, Jianhui Liu, Xiangyu Zhang +2
3D object detectors usually rely on hand-crafted proxies, e.g., anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be densified…
Learning a Room with the Occ-SDF Hybrid: Signed Distance Function Mingled with Occupancy Aids Scene Representation
Xiaoyang Lyu, Peng Dai, Zizhang Li +4
Implicit neural rendering, which uses signed distance function (SDF) representation with geometric priors (such as depth or surface normal), has led to impressive progress in the s…
DreamStone: Image as Stepping Stone for Text-Guided 3D Shape Generation
Zhengzhe Liu, Peng Dai, Ruihui Li +2
In this paper, we present a new text-guided 3D shape generation approach DreamStone that uses images as a stepping stone to bridge the gap between text and shape modalities for gen…
You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding
Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu
3D scene understanding, e.g., point cloud semantic and instance segmentation, often requires large-scale annotated training data, but clearly, point-wise labels are too tedious to…
IST-Net: Prior-free Category-level Pose Estimation with Implicit Space Transformation
Jianhui Liu, Yukang Chen, Xiaoqing Ye +1
Category-level 6D pose estimation aims to predict the poses and sizes of unseen objects from a specific category. Thanks to prior deformation, which explicitly adapts a category-sp…
Understanding Imbalanced Semantic Segmentation Through Neural Collapse
Zhisheng Zhong, Jiequan Cui, Yibo Yang +4
A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equi…