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Yang, Jing; Xiao, Hanyuan; Teng, Wenbin; Cai, Yunxuan; Zhao, Yajie
Light Sampling Field and BRDF Representation for Physically-based Neural Rendering Journal Article
In: 2023, (Publisher: arXiv Version Number: 1).
@article{yang_light_2023,
title = {Light Sampling Field and BRDF Representation for Physically-based Neural Rendering},
author = {Jing Yang and Hanyuan Xiao and Wenbin Teng and Yunxuan Cai and Yajie Zhao},
url = {https://arxiv.org/abs/2304.05472},
doi = {10.48550/ARXIV.2304.05472},
year = {2023},
date = {2023-01-01},
urldate = {2023-08-22},
abstract = {Physically-based rendering (PBR) is key for immersive rendering effects used widely in the industry to showcase detailed realistic scenes from computer graphics assets. A well-known caveat is that producing the same is computationally heavy and relies on complex capture devices. Inspired by the success in quality and efficiency of recent volumetric neural rendering, we want to develop a physically-based neural shader to eliminate device dependency and significantly boost performance. However, no existing lighting and material models in the current neural rendering approaches can accurately represent the comprehensive lighting models and BRDFs properties required by the PBR process. Thus, this paper proposes a novel lighting representation that models direct and indirect light locally through a light sampling strategy in a learned light sampling field. We also propose BRDF models to separately represent surface/subsurface scattering details to enable complex objects such as translucent material (i.e., skin, jade). We then implement our proposed representations with an end-to-end physically-based neural face skin shader, which takes a standard face asset (i.e., geometry, albedo map, and normal map) and an HDRI for illumination as inputs and generates a photo-realistic rendering as output. Extensive experiments showcase the quality and efficiency of our PBR face skin shader, indicating the effectiveness of our proposed lighting and material representations.},
note = {Publisher: arXiv
Version Number: 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Liu, Shichen; Cai, Yunxuan; Chen, Haiwei; Zhou, Yichao; Zhao, Yajie
Rapid Face Asset Acquisition with Recurrent Feature Alignment Journal Article
In: ACM Trans. Graph., vol. 41, no. 6, pp. 214:1–214:17, 2022, ISSN: 0730-0301.
@article{liu_rapid_2022,
title = {Rapid Face Asset Acquisition with Recurrent Feature Alignment},
author = {Shichen Liu and Yunxuan Cai and Haiwei Chen and Yichao Zhou and Yajie Zhao},
url = {https://dl.acm.org/doi/10.1145/3550454.3555509},
doi = {10.1145/3550454.3555509},
issn = {0730-0301},
year = {2022},
date = {2022-11-01},
urldate = {2023-03-31},
journal = {ACM Trans. Graph.},
volume = {41},
number = {6},
pages = {214:1–214:17},
abstract = {We present Recurrent Feature Alignment (ReFA), an end-to-end neural network for the very rapid creation of production-grade face assets from multi-view images. ReFA is on par with the industrial pipelines in quality for producing accurate, complete, registered, and textured assets directly applicable to physically-based rendering, but produces the asset end-to-end, fully automatically at a significantly faster speed at 4.5 FPS, which is unprecedented among neural-based techniques. Our method represents face geometry as a position map in the UV space. The network first extracts per-pixel features in both the multi-view image space and the UV space. A recurrent module then iteratively optimizes the geometry by projecting the image-space features to the UV space and comparing them with a reference UV-space feature. The optimized geometry then provides pixel-aligned signals for the inference of high-resolution textures. Experiments have validated that ReFA achieves a median error of 0.603mm in geometry reconstruction, is robust to extreme pose and expression, and excels in sparse-view settings. We believe that the progress achieved by our network enables lightweight, fast face assets acquisition that significantly boosts the downstream applications, such as avatar creation and facial performance capture. It will also enable massive database capturing for deep learning purposes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kuang, Zhengfei; Li, Jiaman; He, Mingming; Wang, Tong; Zhao, Yajie
DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points Proceedings Article
In: pp. 542–549, IEEE Computer Society, 2022, ISBN: 978-1-66549-062-7.
@inproceedings{kuang_densegap_2022,
title = {DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points},
author = {Zhengfei Kuang and Jiaman Li and Mingming He and Tong Wang and Yajie Zhao},
url = {https://www.computer.org/csdl/proceedings-article/icpr/2022/09956472/1IHpppIuqOc},
doi = {10.1109/ICPR56361.2022.9956472},
isbn = {978-1-66549-062-7},
year = {2022},
date = {2022-08-01},
urldate = {2023-03-31},
pages = {542–549},
publisher = {IEEE Computer Society},
abstract = {Establishing dense correspondence between two images is a fundamental computer vision problem, which is typically tackled by matching local feature descriptors. However, without global awareness, such local features are often insufficient for disambiguating similar regions. And computing the pairwise feature correlation across images is both computation-expensive and memory-intensive. To make the local features aware of the global context and improve their matching accuracy, we introduce DenseGAP, a new solution for efficient Dense correspondence learning with a Graph-structured neural network conditioned on Anchor Points. Specifically, we first propose a graph structure that utilizes anchor points to provide sparse but reliable prior on inter- and intra-image context and propagates them to all image points via directed edges. We also design a graph-structured network to broadcast multi-level contexts via light-weighted message-passing layers and generate high-resolution feature maps at low memory cost. Finally, based on the predicted feature maps, we introduce a coarse-to-fine framework for accurate correspondence prediction using cycle consistency. Our feature descriptors capture both local and global information, thus enabling a continuous feature field for querying arbitrary points at high resolution. Through comprehensive ablative experiments and evaluations on large-scale indoor and outdoor datasets, we demonstrate that our method advances the state-of-the-art of correspondence learning on most benchmarks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Chen, Haiwei; Liu, Jiayi; Chen, Weikai; Liu, Shichen; Zhao, Yajie
Exemplar-based Pattern Synthesis with Implicit Periodic Field Network Proceedings Article
In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3698–3707, IEEE, New Orleans, LA, USA, 2022, ISBN: 978-1-66546-946-3.
@inproceedings{chen_exemplar-based_2022,
title = {Exemplar-based Pattern Synthesis with Implicit Periodic Field Network},
author = {Haiwei Chen and Jiayi Liu and Weikai Chen and Shichen Liu and Yajie Zhao},
url = {https://ieeexplore.ieee.org/document/9879904/},
doi = {10.1109/CVPR52688.2022.00369},
isbn = {978-1-66546-946-3},
year = {2022},
date = {2022-06-01},
urldate = {2023-02-10},
booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {3698–3707},
publisher = {IEEE},
address = {New Orleans, LA, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Liu, Shichen; Li, Tianye; Chen, Weikai; Li, Hao
A General Differentiable Mesh Renderer for Image-Based 3D Reasoning Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 1, pp. 50–62, 2022, ISSN: 1939-3539, (Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence).
@article{liu_general_2022,
title = {A General Differentiable Mesh Renderer for Image-Based 3D Reasoning},
author = {Shichen Liu and Tianye Li and Weikai Chen and Hao Li},
doi = {10.1109/TPAMI.2020.3007759},
issn = {1939-3539},
year = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {44},
number = {1},
pages = {50–62},
abstract = {Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation. By inverting such renderer, one can think of a learning approach to infer 3D information from 2D images. However, standard graphics renderers involve a fundamental step called rasterization, which prevents rendering to be differentiable. Unlike the state-of-the-art differentiable renderers (Kato et al. 2018 and Loper 2018), which only approximate the rendering gradient in the backpropagation, we propose a natually differentiable rendering framework that is able to (1) directly render colorized mesh using differentiable functions and (2) back-propagate efficient supervisions to mesh vertices and their attributes from various forms of image representations. The key to our framework is a novel formulation that views rendering as an aggregation function that fuses the probabilistic contributions of all mesh triangles with respect to the rendered pixels. Such formulation enables our framework to flow gradients to the occluded and distant vertices, which cannot be achieved by the previous state-of-the-arts. We show that by using the proposed renderer, one can achieve significant improvement in 3D unsupervised single-view reconstruction both qualitatively and quantitatively. Experiments also demonstrate that our approach can handle the challenging tasks in image-based shape fitting, which remain nontrivial to existing differentiable renders.},
note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Li, Jiaman; Villegas, Ruben; Ceylan, Duygu; Yang, Jimei; Kuang, Zhengfei; Li, Hao; Zhao, Yajie
Task-Generic Hierarchical Human Motion Prior using VAEs Proceedings Article
In: 2021 International Conference on 3D Vision (3DV), pp. 771–781, IEEE, London, United Kingdom, 2021, ISBN: 978-1-66542-688-6.
@inproceedings{li_task-generic_2021,
title = {Task-Generic Hierarchical Human Motion Prior using VAEs},
author = {Jiaman Li and Ruben Villegas and Duygu Ceylan and Jimei Yang and Zhengfei Kuang and Hao Li and Yajie Zhao},
url = {https://ieeexplore.ieee.org/document/9665881/},
doi = {10.1109/3DV53792.2021.00086},
isbn = {978-1-66542-688-6},
year = {2021},
date = {2021-12-01},
urldate = {2022-09-22},
booktitle = {2021 International Conference on 3D Vision (3DV)},
pages = {771–781},
publisher = {IEEE},
address = {London, United Kingdom},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Li, Tianye; Liu, Shichen; Bolkart, Timo; Liu, Jiayi; Li, Hao; Zhao, Yajie
Topologically Consistent Multi-View Face Inference Using Volumetric Sampling Proceedings Article
In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3804–3814, IEEE, Montreal, QC, Canada, 2021, ISBN: 978-1-66542-812-5.
@inproceedings{li_topologically_2021,
title = {Topologically Consistent Multi-View Face Inference Using Volumetric Sampling},
author = {Tianye Li and Shichen Liu and Timo Bolkart and Jiayi Liu and Hao Li and Yajie Zhao},
url = {https://ieeexplore.ieee.org/document/9711264/},
doi = {10.1109/ICCV48922.2021.00380},
isbn = {978-1-66542-812-5},
year = {2021},
date = {2021-10-01},
urldate = {2022-09-22},
booktitle = {2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
pages = {3804–3814},
publisher = {IEEE},
address = {Montreal, QC, Canada},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Liu, Shichen; Zhou, Yichao; Zhao, Yajie
VaPiD: A Rapid Vanishing Point Detector via Learned Optimizers Proceedings Article
In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 12839–12848, IEEE, Montreal, QC, Canada, 2021, ISBN: 978-1-66542-812-5.
@inproceedings{liu_vapid_2021,
title = {VaPiD: A Rapid Vanishing Point Detector via Learned Optimizers},
author = {Shichen Liu and Yichao Zhou and Yajie Zhao},
url = {https://ieeexplore.ieee.org/document/9711313/},
doi = {10.1109/ICCV48922.2021.01262},
isbn = {978-1-66542-812-5},
year = {2021},
date = {2021-10-01},
urldate = {2022-09-22},
booktitle = {2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
pages = {12839–12848},
publisher = {IEEE},
address = {Montreal, QC, Canada},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Xiang, Sitao; Gu, Yuming; Xiang, Pengda; Chai, Menglei; Li, Hao; Zhao, Yajie; He, Mingming
DisUnknown: Distilling Unknown Factors for Disentanglement Learning Proceedings Article
In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 14790–14799, IEEE, Montreal, QC, Canada, 2021, ISBN: 978-1-66542-812-5.
@inproceedings{xiang_disunknown_2021,
title = {DisUnknown: Distilling Unknown Factors for Disentanglement Learning},
author = {Sitao Xiang and Yuming Gu and Pengda Xiang and Menglei Chai and Hao Li and Yajie Zhao and Mingming He},
url = {https://ieeexplore.ieee.org/document/9709965/},
doi = {10.1109/ICCV48922.2021.01454},
isbn = {978-1-66542-812-5},
year = {2021},
date = {2021-10-01},
urldate = {2022-09-23},
booktitle = {2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
pages = {14790–14799},
publisher = {IEEE},
address = {Montreal, QC, Canada},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Xiang, Sitao
Eliminating topological errors in neural network rotation estimation using self-selecting ensembles Journal Article
In: ACM Trans. Graph., vol. 40, no. 4, pp. 167:1–167:21, 2021, ISSN: 0730-0301.
@article{xiang_eliminating_2021,
title = {Eliminating topological errors in neural network rotation estimation using self-selecting ensembles},
author = {Sitao Xiang},
url = {https://dl.acm.org/doi/10.1145/3450626.3459882},
doi = {10.1145/3450626.3459882},
issn = {0730-0301},
year = {2021},
date = {2021-07-01},
urldate = {2023-03-31},
journal = {ACM Trans. Graph.},
volume = {40},
number = {4},
pages = {167:1–167:21},
abstract = {Many problems in computer graphics and computer vision applications involves inferring a rotation from a variety of different forms of inputs. With the increasing use of deep learning, neural networks have been employed to solve such problems. However, the traditional representations for 3D rotations, the quaternions and Euler angles, are found to be problematic for neural networks in practice, producing seemingly unavoidable large estimation errors. Previous researches has identified the discontinuity of the mapping from SO(3) to the quaternions or Euler angles as the source of such errors, and to solve it, embeddings of SO(3) have been proposed as the output representation of rotation estimation networks instead. In this paper, we argue that the argument against quaternions and Euler angles from local discontinuities of the mappings from SO(3) is flawed, and instead provide a different argument from the global topological properties of SO(3) that also establishes the lower bound of maximum error when using quaternions and Euler angles for rotation estimation networks. Extending from this view, we discover that rotation symmetries in the input object causes additional topological problems that even using embeddings of SO(3) as the output representation would not correctly handle. We propose the self-selecting ensemble, a topologically motivated approach, where the network makes multiple predictions and assigns weights to them. We show theoretically and with experiments that our methods can be combined with a wide range of different rotation representations and can handle all kinds of finite symmetries in 3D rotation estimation problems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Chen, Haiwei; Liu, Shichen; Chen, Weikai; Li, Hao; Hill, Randall
Equivariant Point Network for 3D Point Cloud Analysis Proceedings Article
In: pp. 14514–14523, 2021.
@inproceedings{chen_equivariant_2021,
title = {Equivariant Point Network for 3D Point Cloud Analysis},
author = {Haiwei Chen and Shichen Liu and Weikai Chen and Hao Li and Randall Hill},
url = {https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Equivariant_Point_Network_for_3D_Point_Cloud_Analysis_CVPR_2021_paper.html},
year = {2021},
date = {2021-01-01},
urldate = {2023-03-31},
pages = {14514–14523},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Zhou, Yi; Wu, Chenglei; Li, Zimo; Cao, Chen; Ye, Yuting; Saragih, Jason; Li, Hao; Sheikh, Yaser
Fully convolutional mesh autoencoder using efficient spatially varying kernels Proceedings Article
In: Proceedings of the 34th International Conference on Neural Information Processing Systems, pp. 9251–9262, Curran Associates Inc., Red Hook, NY, USA, 2020, ISBN: 978-1-71382-954-6.
@inproceedings{zhou_fully_2020,
title = {Fully convolutional mesh autoencoder using efficient spatially varying kernels},
author = {Yi Zhou and Chenglei Wu and Zimo Li and Chen Cao and Yuting Ye and Jason Saragih and Hao Li and Yaser Sheikh},
isbn = {978-1-71382-954-6},
year = {2020},
date = {2020-12-01},
urldate = {2023-03-31},
booktitle = {Proceedings of the 34th International Conference on Neural Information Processing Systems},
pages = {9251–9262},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
series = {NIPS'20},
abstract = {Learning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Filter
2023
Yang, Jing; Xiao, Hanyuan; Teng, Wenbin; Cai, Yunxuan; Zhao, Yajie
Light Sampling Field and BRDF Representation for Physically-based Neural Rendering Journal Article
In: 2023, (Publisher: arXiv Version Number: 1).
Abstract | Links | BibTeX | Tags: UARC, VGL
@article{yang_light_2023,
title = {Light Sampling Field and BRDF Representation for Physically-based Neural Rendering},
author = {Jing Yang and Hanyuan Xiao and Wenbin Teng and Yunxuan Cai and Yajie Zhao},
url = {https://arxiv.org/abs/2304.05472},
doi = {10.48550/ARXIV.2304.05472},
year = {2023},
date = {2023-01-01},
urldate = {2023-08-22},
abstract = {Physically-based rendering (PBR) is key for immersive rendering effects used widely in the industry to showcase detailed realistic scenes from computer graphics assets. A well-known caveat is that producing the same is computationally heavy and relies on complex capture devices. Inspired by the success in quality and efficiency of recent volumetric neural rendering, we want to develop a physically-based neural shader to eliminate device dependency and significantly boost performance. However, no existing lighting and material models in the current neural rendering approaches can accurately represent the comprehensive lighting models and BRDFs properties required by the PBR process. Thus, this paper proposes a novel lighting representation that models direct and indirect light locally through a light sampling strategy in a learned light sampling field. We also propose BRDF models to separately represent surface/subsurface scattering details to enable complex objects such as translucent material (i.e., skin, jade). We then implement our proposed representations with an end-to-end physically-based neural face skin shader, which takes a standard face asset (i.e., geometry, albedo map, and normal map) and an HDRI for illumination as inputs and generates a photo-realistic rendering as output. Extensive experiments showcase the quality and efficiency of our PBR face skin shader, indicating the effectiveness of our proposed lighting and material representations.},
note = {Publisher: arXiv
Version Number: 1},
keywords = {UARC, VGL},
pubstate = {published},
tppubtype = {article}
}
2022
Liu, Shichen; Cai, Yunxuan; Chen, Haiwei; Zhou, Yichao; Zhao, Yajie
Rapid Face Asset Acquisition with Recurrent Feature Alignment Journal Article
In: ACM Trans. Graph., vol. 41, no. 6, pp. 214:1–214:17, 2022, ISSN: 0730-0301.
Abstract | Links | BibTeX | Tags: VGL
@article{liu_rapid_2022,
title = {Rapid Face Asset Acquisition with Recurrent Feature Alignment},
author = {Shichen Liu and Yunxuan Cai and Haiwei Chen and Yichao Zhou and Yajie Zhao},
url = {https://dl.acm.org/doi/10.1145/3550454.3555509},
doi = {10.1145/3550454.3555509},
issn = {0730-0301},
year = {2022},
date = {2022-11-01},
urldate = {2023-03-31},
journal = {ACM Trans. Graph.},
volume = {41},
number = {6},
pages = {214:1–214:17},
abstract = {We present Recurrent Feature Alignment (ReFA), an end-to-end neural network for the very rapid creation of production-grade face assets from multi-view images. ReFA is on par with the industrial pipelines in quality for producing accurate, complete, registered, and textured assets directly applicable to physically-based rendering, but produces the asset end-to-end, fully automatically at a significantly faster speed at 4.5 FPS, which is unprecedented among neural-based techniques. Our method represents face geometry as a position map in the UV space. The network first extracts per-pixel features in both the multi-view image space and the UV space. A recurrent module then iteratively optimizes the geometry by projecting the image-space features to the UV space and comparing them with a reference UV-space feature. The optimized geometry then provides pixel-aligned signals for the inference of high-resolution textures. Experiments have validated that ReFA achieves a median error of 0.603mm in geometry reconstruction, is robust to extreme pose and expression, and excels in sparse-view settings. We believe that the progress achieved by our network enables lightweight, fast face assets acquisition that significantly boosts the downstream applications, such as avatar creation and facial performance capture. It will also enable massive database capturing for deep learning purposes.},
keywords = {VGL},
pubstate = {published},
tppubtype = {article}
}
Kuang, Zhengfei; Li, Jiaman; He, Mingming; Wang, Tong; Zhao, Yajie
DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points Proceedings Article
In: pp. 542–549, IEEE Computer Society, 2022, ISBN: 978-1-66549-062-7.
Abstract | Links | BibTeX | Tags: VGL
@inproceedings{kuang_densegap_2022,
title = {DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points},
author = {Zhengfei Kuang and Jiaman Li and Mingming He and Tong Wang and Yajie Zhao},
url = {https://www.computer.org/csdl/proceedings-article/icpr/2022/09956472/1IHpppIuqOc},
doi = {10.1109/ICPR56361.2022.9956472},
isbn = {978-1-66549-062-7},
year = {2022},
date = {2022-08-01},
urldate = {2023-03-31},
pages = {542–549},
publisher = {IEEE Computer Society},
abstract = {Establishing dense correspondence between two images is a fundamental computer vision problem, which is typically tackled by matching local feature descriptors. However, without global awareness, such local features are often insufficient for disambiguating similar regions. And computing the pairwise feature correlation across images is both computation-expensive and memory-intensive. To make the local features aware of the global context and improve their matching accuracy, we introduce DenseGAP, a new solution for efficient Dense correspondence learning with a Graph-structured neural network conditioned on Anchor Points. Specifically, we first propose a graph structure that utilizes anchor points to provide sparse but reliable prior on inter- and intra-image context and propagates them to all image points via directed edges. We also design a graph-structured network to broadcast multi-level contexts via light-weighted message-passing layers and generate high-resolution feature maps at low memory cost. Finally, based on the predicted feature maps, we introduce a coarse-to-fine framework for accurate correspondence prediction using cycle consistency. Our feature descriptors capture both local and global information, thus enabling a continuous feature field for querying arbitrary points at high resolution. Through comprehensive ablative experiments and evaluations on large-scale indoor and outdoor datasets, we demonstrate that our method advances the state-of-the-art of correspondence learning on most benchmarks.},
keywords = {VGL},
pubstate = {published},
tppubtype = {inproceedings}
}
Chen, Haiwei; Liu, Jiayi; Chen, Weikai; Liu, Shichen; Zhao, Yajie
Exemplar-based Pattern Synthesis with Implicit Periodic Field Network Proceedings Article
In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3698–3707, IEEE, New Orleans, LA, USA, 2022, ISBN: 978-1-66546-946-3.
Links | BibTeX | Tags: UARC, VGL
@inproceedings{chen_exemplar-based_2022,
title = {Exemplar-based Pattern Synthesis with Implicit Periodic Field Network},
author = {Haiwei Chen and Jiayi Liu and Weikai Chen and Shichen Liu and Yajie Zhao},
url = {https://ieeexplore.ieee.org/document/9879904/},
doi = {10.1109/CVPR52688.2022.00369},
isbn = {978-1-66546-946-3},
year = {2022},
date = {2022-06-01},
urldate = {2023-02-10},
booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {3698–3707},
publisher = {IEEE},
address = {New Orleans, LA, USA},
keywords = {UARC, VGL},
pubstate = {published},
tppubtype = {inproceedings}
}
Liu, Shichen; Li, Tianye; Chen, Weikai; Li, Hao
A General Differentiable Mesh Renderer for Image-Based 3D Reasoning Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 1, pp. 50–62, 2022, ISSN: 1939-3539, (Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence).
Abstract | Links | BibTeX | Tags: VGL
@article{liu_general_2022,
title = {A General Differentiable Mesh Renderer for Image-Based 3D Reasoning},
author = {Shichen Liu and Tianye Li and Weikai Chen and Hao Li},
doi = {10.1109/TPAMI.2020.3007759},
issn = {1939-3539},
year = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {44},
number = {1},
pages = {50–62},
abstract = {Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation. By inverting such renderer, one can think of a learning approach to infer 3D information from 2D images. However, standard graphics renderers involve a fundamental step called rasterization, which prevents rendering to be differentiable. Unlike the state-of-the-art differentiable renderers (Kato et al. 2018 and Loper 2018), which only approximate the rendering gradient in the backpropagation, we propose a natually differentiable rendering framework that is able to (1) directly render colorized mesh using differentiable functions and (2) back-propagate efficient supervisions to mesh vertices and their attributes from various forms of image representations. The key to our framework is a novel formulation that views rendering as an aggregation function that fuses the probabilistic contributions of all mesh triangles with respect to the rendered pixels. Such formulation enables our framework to flow gradients to the occluded and distant vertices, which cannot be achieved by the previous state-of-the-arts. We show that by using the proposed renderer, one can achieve significant improvement in 3D unsupervised single-view reconstruction both qualitatively and quantitatively. Experiments also demonstrate that our approach can handle the challenging tasks in image-based shape fitting, which remain nontrivial to existing differentiable renders.},
note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {VGL},
pubstate = {published},
tppubtype = {article}
}
2021
Li, Jiaman; Villegas, Ruben; Ceylan, Duygu; Yang, Jimei; Kuang, Zhengfei; Li, Hao; Zhao, Yajie
Task-Generic Hierarchical Human Motion Prior using VAEs Proceedings Article
In: 2021 International Conference on 3D Vision (3DV), pp. 771–781, IEEE, London, United Kingdom, 2021, ISBN: 978-1-66542-688-6.
Links | BibTeX | Tags: DTIC, UARC, VGL
@inproceedings{li_task-generic_2021,
title = {Task-Generic Hierarchical Human Motion Prior using VAEs},
author = {Jiaman Li and Ruben Villegas and Duygu Ceylan and Jimei Yang and Zhengfei Kuang and Hao Li and Yajie Zhao},
url = {https://ieeexplore.ieee.org/document/9665881/},
doi = {10.1109/3DV53792.2021.00086},
isbn = {978-1-66542-688-6},
year = {2021},
date = {2021-12-01},
urldate = {2022-09-22},
booktitle = {2021 International Conference on 3D Vision (3DV)},
pages = {771–781},
publisher = {IEEE},
address = {London, United Kingdom},
keywords = {DTIC, UARC, VGL},
pubstate = {published},
tppubtype = {inproceedings}
}
Li, Tianye; Liu, Shichen; Bolkart, Timo; Liu, Jiayi; Li, Hao; Zhao, Yajie
Topologically Consistent Multi-View Face Inference Using Volumetric Sampling Proceedings Article
In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3804–3814, IEEE, Montreal, QC, Canada, 2021, ISBN: 978-1-66542-812-5.
Links | BibTeX | Tags: DTIC, UARC, VGL
@inproceedings{li_topologically_2021,
title = {Topologically Consistent Multi-View Face Inference Using Volumetric Sampling},
author = {Tianye Li and Shichen Liu and Timo Bolkart and Jiayi Liu and Hao Li and Yajie Zhao},
url = {https://ieeexplore.ieee.org/document/9711264/},
doi = {10.1109/ICCV48922.2021.00380},
isbn = {978-1-66542-812-5},
year = {2021},
date = {2021-10-01},
urldate = {2022-09-22},
booktitle = {2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
pages = {3804–3814},
publisher = {IEEE},
address = {Montreal, QC, Canada},
keywords = {DTIC, UARC, VGL},
pubstate = {published},
tppubtype = {inproceedings}
}
Liu, Shichen; Zhou, Yichao; Zhao, Yajie
VaPiD: A Rapid Vanishing Point Detector via Learned Optimizers Proceedings Article
In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 12839–12848, IEEE, Montreal, QC, Canada, 2021, ISBN: 978-1-66542-812-5.
Links | BibTeX | Tags: DTIC, UARC, VGL
@inproceedings{liu_vapid_2021,
title = {VaPiD: A Rapid Vanishing Point Detector via Learned Optimizers},
author = {Shichen Liu and Yichao Zhou and Yajie Zhao},
url = {https://ieeexplore.ieee.org/document/9711313/},
doi = {10.1109/ICCV48922.2021.01262},
isbn = {978-1-66542-812-5},
year = {2021},
date = {2021-10-01},
urldate = {2022-09-22},
booktitle = {2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
pages = {12839–12848},
publisher = {IEEE},
address = {Montreal, QC, Canada},
keywords = {DTIC, UARC, VGL},
pubstate = {published},
tppubtype = {inproceedings}
}
Xiang, Sitao; Gu, Yuming; Xiang, Pengda; Chai, Menglei; Li, Hao; Zhao, Yajie; He, Mingming
DisUnknown: Distilling Unknown Factors for Disentanglement Learning Proceedings Article
In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 14790–14799, IEEE, Montreal, QC, Canada, 2021, ISBN: 978-1-66542-812-5.
Links | BibTeX | Tags: DTIC, UARC, VGL
@inproceedings{xiang_disunknown_2021,
title = {DisUnknown: Distilling Unknown Factors for Disentanglement Learning},
author = {Sitao Xiang and Yuming Gu and Pengda Xiang and Menglei Chai and Hao Li and Yajie Zhao and Mingming He},
url = {https://ieeexplore.ieee.org/document/9709965/},
doi = {10.1109/ICCV48922.2021.01454},
isbn = {978-1-66542-812-5},
year = {2021},
date = {2021-10-01},
urldate = {2022-09-23},
booktitle = {2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
pages = {14790–14799},
publisher = {IEEE},
address = {Montreal, QC, Canada},
keywords = {DTIC, UARC, VGL},
pubstate = {published},
tppubtype = {inproceedings}
}
Xiang, Sitao
Eliminating topological errors in neural network rotation estimation using self-selecting ensembles Journal Article
In: ACM Trans. Graph., vol. 40, no. 4, pp. 167:1–167:21, 2021, ISSN: 0730-0301.
Abstract | Links | BibTeX | Tags: VGL
@article{xiang_eliminating_2021,
title = {Eliminating topological errors in neural network rotation estimation using self-selecting ensembles},
author = {Sitao Xiang},
url = {https://dl.acm.org/doi/10.1145/3450626.3459882},
doi = {10.1145/3450626.3459882},
issn = {0730-0301},
year = {2021},
date = {2021-07-01},
urldate = {2023-03-31},
journal = {ACM Trans. Graph.},
volume = {40},
number = {4},
pages = {167:1–167:21},
abstract = {Many problems in computer graphics and computer vision applications involves inferring a rotation from a variety of different forms of inputs. With the increasing use of deep learning, neural networks have been employed to solve such problems. However, the traditional representations for 3D rotations, the quaternions and Euler angles, are found to be problematic for neural networks in practice, producing seemingly unavoidable large estimation errors. Previous researches has identified the discontinuity of the mapping from SO(3) to the quaternions or Euler angles as the source of such errors, and to solve it, embeddings of SO(3) have been proposed as the output representation of rotation estimation networks instead. In this paper, we argue that the argument against quaternions and Euler angles from local discontinuities of the mappings from SO(3) is flawed, and instead provide a different argument from the global topological properties of SO(3) that also establishes the lower bound of maximum error when using quaternions and Euler angles for rotation estimation networks. Extending from this view, we discover that rotation symmetries in the input object causes additional topological problems that even using embeddings of SO(3) as the output representation would not correctly handle. We propose the self-selecting ensemble, a topologically motivated approach, where the network makes multiple predictions and assigns weights to them. We show theoretically and with experiments that our methods can be combined with a wide range of different rotation representations and can handle all kinds of finite symmetries in 3D rotation estimation problems.},
keywords = {VGL},
pubstate = {published},
tppubtype = {article}
}
Chen, Haiwei; Liu, Shichen; Chen, Weikai; Li, Hao; Hill, Randall
Equivariant Point Network for 3D Point Cloud Analysis Proceedings Article
In: pp. 14514–14523, 2021.
Links | BibTeX | Tags: UARC, VGL
@inproceedings{chen_equivariant_2021,
title = {Equivariant Point Network for 3D Point Cloud Analysis},
author = {Haiwei Chen and Shichen Liu and Weikai Chen and Hao Li and Randall Hill},
url = {https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Equivariant_Point_Network_for_3D_Point_Cloud_Analysis_CVPR_2021_paper.html},
year = {2021},
date = {2021-01-01},
urldate = {2023-03-31},
pages = {14514–14523},
keywords = {UARC, VGL},
pubstate = {published},
tppubtype = {inproceedings}
}
2020
Zhou, Yi; Wu, Chenglei; Li, Zimo; Cao, Chen; Ye, Yuting; Saragih, Jason; Li, Hao; Sheikh, Yaser
Fully convolutional mesh autoencoder using efficient spatially varying kernels Proceedings Article
In: Proceedings of the 34th International Conference on Neural Information Processing Systems, pp. 9251–9262, Curran Associates Inc., Red Hook, NY, USA, 2020, ISBN: 978-1-71382-954-6.
@inproceedings{zhou_fully_2020,
title = {Fully convolutional mesh autoencoder using efficient spatially varying kernels},
author = {Yi Zhou and Chenglei Wu and Zimo Li and Chen Cao and Yuting Ye and Jason Saragih and Hao Li and Yaser Sheikh},
isbn = {978-1-71382-954-6},
year = {2020},
date = {2020-12-01},
urldate = {2023-03-31},
booktitle = {Proceedings of the 34th International Conference on Neural Information Processing Systems},
pages = {9251–9262},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
series = {NIPS'20},
abstract = {Learning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models.},
keywords = {VGL},
pubstate = {published},
tppubtype = {inproceedings}
}