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Email / CV / Linkedin / Github / Scholar I am a 1st year PhD student from CVMI Lab supervised by Xiaojuan Qi, focusing on 3D/4D reconstruction, interaction, simulation and editing. Previously, I completed my master degree at the Institute of Computing Technology, Chinese Academy of Sciences, under the supervision of Professor Lin Gao. I am grateful for the valuable guidance and assistance provided by Prof. Gao. Additionally, I have also worked closely with Doctor Yan-Pei Cao and Professor Yu-Kun Lai, both of whom have greatly contributed to my learning experience. Prior to that, I obtained my bachelor degree at the University of Chinese Academy of Sciences, where I was supervised by the respectful Prof. Xilin Chen, who introduced me to my research field and taught the foundational class on conducting research.
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[7] Yihua Huang *, Yang-Tian Sun*, Ziyi Yang*, Xiaoyang Lyu, Yan-Pei Cao#, Xiaojuan Qi# IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024 paper / project page / code We introduce sparse-controlled gaussian splatting to synthesize dynamic novel views. With the learned node graph of sparse control points, real-time editing can be achieved with ARAP deformation by interactive dragging of users. |
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[6] Yihua Huang, Yan-Pei Cao, Yu-Kun Lai, Ying Shan, Lin Gao IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2024 paper / project page / code We propose an algorithm to synthesize NeRF textures on arbitrary manifolds. By using a patch-matching method on curved surfaces, we can smoothly quilt texture patches on mesh surfaces. We create a multi-resolution pyramid for a fast patch-matching process. By incorporating a reflection network, we preserve high-frequency view-dependent features such as highlights and mirror reflections in the final synthesized results. |
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[5] Yihua Huang, Yan-Pei Cao, Yu-Kun Lai, Ying Shan, Lin Gao ACM SIGGRAPH 2023 Conference Proceedings (SIGGRAPH), 2023 paper / project page / code We introduce a NeRF-based system to acquire, synthesize, map, and relight textures from real-world textures. A novel coarse-fine disentangling representation is proposed to model meso-structures of textures. Acquired textures are synthesized by an implicit patch-matching algorithm. |
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[4] Yihua Huang, Yue He, Yu-Jie Yuan, Yu-Kun Lai, Lin Gao IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 arxiv / project page / code We propose a novel mutual learning framework for 3D scene stylization that combines a 2D image stylization network and NeRF to fuse the stylization ability of 2D stylization network with the 3D consistency of NeRF. |
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[3] Yuting He, Yiqiang Chen, XiaoDong Yang, Hanchao Yu, Yihua Huang, Yang Gu IEEE Transactions on Big Data (TBD), 2022 paper We propose a Selective Self-Distillation method for Federated learning (FedSSD), which imposes adaptive constraints on the local updates by self-distilling the global model's knowledge and selectively weighting it by evaluating the credibility at both the class and sample level. |
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[2] Yu-Jie Yuan, Yu-Kun Lai, Yihua Huang, Leif Kobbelt, Lin Gao, IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2022 paper / project page We introduce a novel NeRF reconstruction method using RGB-D inputs from a consumer-level device (iPad), which enables high-quality reconstruction from sparse inputs. Experiments show that the proposed method achieves state-of-the-art novel view synthesis quality in this case of sparse RGB-D inputs. |
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[1] Jie Yang, Lin Gao, Qingyang Tan, Yihua Huang, Shihong Xia Yu-Kun Lai IEEE Transactions on Visualization and Computer Graphics (TVCG), 2021 arxiv We propose a novel method to exact multiscale deformation components automatically with a stacked attention-based autoencoder. |
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Internships: Tencent (2023 Summer) |
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Kudos to Dr. Jon Barron for sharing his website template. |