74 citations · 634 across the 41 of their papers we have counts for
34 papers · 1 filter
Inverse Global Illumination using a Neural Radiometric Prior
Saeed Hadadan, Geng Lin, Jan Novák +2
Inverse rendering methods that account for global illumination are becoming more popular, but current methods require evaluating and automatically differentiating millions of path…
PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters
Shuhong Chen, Kevin Zhang, Yichun Shi +7
We propose PAniC-3D, a system to reconstruct stylized 3D character heads directly from illustrated (p)ortraits of (ani)me (c)haracters. Our anime-style domain poses unique challeng…
Surface Reconstruction from Point Clouds by Learning Predictive Context Priors
Baorui Ma, Yu-Shen Liu, Matthias Zwicker +1
Surface reconstruction from point clouds is vital for 3D computer vision. State-of-the-art methods leverage large datasets to first learn local context priors that are represented…
HVTR: Hybrid Volumetric-Textural Rendering for Human Avatars
Tao Hu, Tao Yu, Zerong Zheng +3
We propose a novel neural rendering pipeline, Hybrid Volumetric-Textural Rendering (HVTR), which synthesizes virtual human avatars from arbitrary poses efficiently and at high qual…
EgoRenderer: Rendering Human Avatars from Egocentric Camera Images
Tao Hu, Kripasindhu Sarkar, Lingjie Liu +2
We present EgoRenderer, a system for rendering full-body neural avatars of a person captured by a wearable, egocentric fisheye camera that is mounted on a cap or a VR headset. Our…
PatchGame: Learning to Signal Mid-level Patches in Referential Games
Kamal Gupta, Gowthami Somepalli, Anubhav Gupta +3
We study a referential game (a type of signaling game) where two agents communicate with each other via a discrete bottleneck to achieve a common goal. In our referential game, the…
Improving the Perceptual Quality of 2D Animation Interpolation
Shuhong Chen, Matthias Zwicker
Traditional 2D animation is labor-intensive, often requiring animators to manually draw twelve illustrations per second of movement. While automatic frame interpolation may ease th…
Hierarchical View Predictor: Unsupervised 3D Global Feature Learning through Hierarchical Prediction among Unordered Views
Zhizhong Han, Xiyang Wang, Yu-Shen Liu +1
Unsupervised learning of global features for 3D shape analysis is an important research challenge because it avoids manual effort for supervised information collection. In this pap…
Transfer Learning for Pose Estimation of Illustrated Characters
Shuhong Chen, Matthias Zwicker
Human pose information is a critical component in many downstream image processing tasks, such as activity recognition and motion tracking. Likewise, a pose estimator for the illus…
Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces
Baorui Ma, Zhizhong Han, Yu-Shen Liu +1
Reconstructing continuous surfaces from 3D point clouds is a fundamental operation in 3D geometry processing. Several recent state-of-the-art methods address this problem using neu…
Improved Modeling of 3D Shapes with Multi-view Depth Maps
Kamal Gupta, Susmija Jabbireddy, Ketul Shah +2
We present a simple yet effective general-purpose framework for modeling 3D shapes by leveraging recent advances in 2D image generation using CNNs. Using just a single depth image…
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images
Zhizhong Han, Chao Chen, Yu-Shen Liu +1
Differentiable renderers have been used successfully for unsupervised 3D structure learning from 2D images because they can bridge the gap between 3D and 2D. To optimize 3D shape p…
Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions
Xinhai Liu, Zhizhong Han, Yu-Shen Liu +1
Fine-grained 3D shape classification is important for shape understanding and analysis, which poses a challenging research problem. However, the studies on the fine-grained 3D shap…
LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local Region Contexts
Xinhai Liu, Zhizhong Han, Fangzhou Hong +2
Learning discriminative feature directly on point clouds is still challenging in the understanding of 3D shapes. Recent methods usually partition point clouds into local region set…
SeqXY2SeqZ: Structure Learning for 3D Shapes by Sequentially Predicting 1D Occupancy Segments From 2D Coordinates
Zhizhong Han, Guanhui Qiao, Yu-Shen Liu +1
Structure learning for 3D shapes is vital for 3D computer vision. State-of-the-art methods show promising results by representing shapes using implicit functions in 3D that are lea…
Learning to Generate Dense Point Clouds with Textures on Multiple Categories
Tao Hu, Geng Lin, Zhizhong Han +1
3D reconstruction from images is a core problem in computer vision. With recent advances in deep learning, it has become possible to recover plausible 3D shapes even from single RG…
SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization
Yue Jiang, Dantong Ji, Zhizhong Han +1
We propose SDFDiff, a novel approach for image-based shape optimization using differentiable rendering of 3D shapes represented by signed distance functions (SDFs). Compared to oth…
3D Shape Completion with Multi-view Consistent Inference
Tao Hu, Zhizhong Han, Matthias Zwicker
3D shape completion is important to enable machines to perceive the complete geometry of objects from partial observations. To address this problem, view-based methods have been pr…
L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention
Xinhai Liu, Zhizhong Han, Xin Wen +2
Auto-encoder is an important architecture to understand point clouds in an encoding and decoding procedure of self reconstruction. Current auto-encoder mainly focuses on the learni…
ShapeCaptioner: Generative Caption Network for 3D Shapes by Learning a Mapping from Parts Detected in Multiple Views to Sentences
Zhizhong Han, Chao Chen, Yu-Shen Liu +1
3D shape captioning is a challenging application in 3D shape understanding. Captions from recent multi-view based methods reveal that they cannot capture part-level characteristics…
Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction
Zhizhong Han, Xiyang Wang, Yu-Shen Liu +1
Unsupervised feature learning for point clouds has been vital for large-scale point cloud understanding. Recent deep learning based methods depend on learning global geometry from…
Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views
Zhizhong Han, Xinhai Liu, Yu-Shen Liu +1
Deep learning has achieved remarkable results in 3D shape analysis by learning global shape features from the pixel-level over multiple views. Previous methods, however, compute lo…
3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention
Zhizhong Han, Xiyang Wang, Chi-Man Vong +3
Learning global features by aggregating information over multiple views has been shown to be effective for 3D shape analysis. For view aggregation in deep learning models, pooling…
Render4Completion: Synthesizing Multi-View Depth Maps for 3D Shape Completion
Tao Hu, Zhizhong Han, Abhinav Shrivastava +1
We propose a novel approach for 3D shape completion by synthesizing multi-view depth maps. While previous work for shape completion relies on volumetric representations, meshes, or…