203 citations · 1.5k across the 53 of their papers we have counts for
55 papers
Example-based Motion Synthesis via Generative Motion Matching
Weiyu Li, Xuelin Chen, Peizhuo Li +2
We present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, whi…
Patch-based 3D Natural Scene Generation from a Single Example
Weiyu Li, Xuelin Chen, Jue Wang +1
We target a 3D generative model for general natural scenes that are typically unique and intricate. Lacking the necessary volumes of training data, along with the difficulties of h…
Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling
Yulin Liu, Haoran Liu, Yingda Yin +3
Normalizing flows (NFs) provide a powerful tool to construct an expressive distribution by a sequence of trackable transformations of a base distribution and form a probabilistic m…
Control3Diff: Learning Controllable 3D Diffusion Models from Single-view Images
Jiatao Gu, Qingzhe Gao, Shuangfei Zhai +3
Diffusion models have recently become the de-facto approach for generative modeling in the 2D domain. However, extending diffusion models to 3D is challenging due to the difficulti…
SinGRAV: Learning a Generative Radiance Volume from a Single Natural Scene
Yujie Wang, Xuelin Chen, Baoquan Chen
We present a 3D generative model for general natural scenes. Lacking necessary volumes of 3D data characterizing the target scene, we propose to learn from a single scene. Our key…
Rhythmic Gesticulator: Rhythm-Aware Co-Speech Gesture Synthesis with Hierarchical Neural Embeddings
Tenglong Ao, Qingzhe Gao, Yuke Lou +2
Automatic synthesis of realistic co-speech gestures is an increasingly important yet challenging task in artificial embodied agent creation. Previous systems mainly focus on genera…
Multi-Robot Active Mapping via Neural Bipartite Graph Matching
Kai Ye, Siyan Dong, Qingnan Fan +5
We study the problem of multi-robot active mapping, which aims for complete scene map construction in minimum time steps. The key to this problem lies in the goal position estimati…
FisherMatch: Semi-Supervised Rotation Regression via Entropy-based Filtering
Yingda Yin, Yingcheng Cai, He Wang +1
Estimating the 3DoF rotation from a single RGB image is an important yet challenging problem. Recent works achieve good performance relying on a large amount of expensive-to-obtain…
Self-Conditioned Generative Adversarial Networks for Image Editing
Yunzhe Liu, Rinon Gal, Amit H. Bermano +2
Generative Adversarial Networks (GANs) are susceptible to bias, learned from either the unbalanced data, or through mode collapse. The networks focus on the core of the data distri…
Projective Manifold Gradient Layer for Deep Rotation Regression
Jiayi Chen, Yingda Yin, Tolga Birdal +3
Regressing rotations on SO(3) manifold using deep neural networks is an important yet unsolved problem. The gap between the Euclidean network output space and the non-Euclidean SO(…
Unsupervised Co-part Segmentation through Assembly
Qingzhe Gao, Bin Wang, Libin Liu +1
Co-part segmentation is an important problem in computer vision for its rich applications. We propose an unsupervised learning approach for co-part segmentation from images. For th…
MoCo-Flow: Neural Motion Consensus Flow for Dynamic Humans in Stationary Monocular Cameras
Xuelin Chen, Weiyu Li, Daniel Cohen-Or +2
Synthesizing novel views of dynamic humans from stationary monocular cameras is a specialized but desirable setup. This is particularly attractive as it does not require static sce…
Neural Implicit 3D Shapes from Single Images with Spatial Patterns
Yixin Zhuang, Yunzhe Liu, Yujie Wang +1
Neural implicit functions have achieved impressive results for reconstructing 3D shapes from single images. However, the image features for describing 3D point samplings of implici…
Solid-Fluid Interaction with Surface-Tension-Dominant Contact
Liangwang Ruan, Jinyuan Liu, Bo Zhu +3
We propose a novel three-way coupling method to model the contact interaction between solid and fluid driven by strong surface tension. At the heart of our physical model is a thin…
Learning Skeletal Articulations with Neural Blend Shapes
Peizhuo Li, Kfir Aberman, Rana Hanocka +3
Animating a newly designed character using motion capture (mocap) data is a long standing problem in computer animation. A key consideration is the skeletal structure that should c…
CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
Yijia Weng, He Wang, Qiang Zhou +6
In this work, we tackle the problem of category-level online pose tracking of objects from point cloud sequences. For the first time, we propose a unified framework that can handle…
Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments
Siyan Dong, Qingnan Fan, He Wang +5
Localizing the camera in a known indoor environment is a key building block for scene mapping, robot navigation, AR, etc. Recent advances estimate the camera pose via optimization…
Implicit Multidimensional Projection of Local Subspaces
Rongzheng Bian, Yumeng Xue, Liang Zhou +4
We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation. Here, we understand the local…
MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
Mingyi Shi, Kfir Aberman, Andreas Aristidou +4
We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video.While previous methods rely on either rigging or inve…
DO-Conv: Depthwise Over-parameterized Convolutional Layer
Jinming Cao, Yangyan Li, Mingchao Sun +5
Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs). In this paper, we propose to augment a convolutional layer with an additional depthwise c…
Towards a Neural Graphics Pipeline for Controllable Image Generation
Xuelin Chen, Daniel Cohen-Or, Baoquan Chen +1
In this paper, we leverage advances in neural networks towards forming a neural rendering for controllable image generation, and thereby bypassing the need for detailed modeling in…
Generative 3D Part Assembly via Dynamic Graph Learning
Jialei Huang, Guanqi Zhan, Qingnan Fan +5
Autonomous part assembly is a challenging yet crucial task in 3D computer vision and robotics. Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a si…
Unpaired Motion Style Transfer from Video to Animation
Kfir Aberman, Yijia Weng, Dani Lischinski +2
Transferring the motion style from one animation clip to another, while preserving the motion content of the latter, has been a long-standing problem in character animation. Most e…
Skeleton-Aware Networks for Deep Motion Retargeting
Kfir Aberman, Peizhuo Li, Dani Lischinski +3
We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Imp…