no code implementations • 15 Jun 2023 • Shizhan Zhu, Shunsuke Saito, Aljaz Bozic, Carlos Aliaga, Trevor Darrell, Christop Lassner
Reconstructing and relighting objects and scenes under varying lighting conditions is challenging: existing neural rendering methods often cannot handle the complex interactions between materials and light.
2 code implementations • ACL 2022 • Rodolfo Corona, Shizhan Zhu, Dan Klein, Trevor Darrell
Natural language applied to natural 2D images describes a fundamentally 3D world.
no code implementations • ICLR 2022 • Shizhan Zhu, Sayna Ebrahimi, Angjoo Kanazawa, Trevor Darrell
Existing approaches for single object reconstruction impose supervision signals based on the loss of the signed distance value from all locations in a scene, posing difficulties when extending to real-world scenarios.
no code implementations • 18 Dec 2020 • Sayna Ebrahimi, William Gan, Dian Chen, Giscard Biamby, Kamyar Salahi, Michael Laielli, Shizhan Zhu, Trevor Darrell
Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator.
no code implementations • ICCV 2017 • Shizhan Zhu, Sanja Fidler, Raquel Urtasun, Dahua Lin, Chen Change Loy
In the second stage, a generative model with a newly proposed compositional mapping layer is used to render the final image with precise regions and textures conditioned on this map.
no code implementations • 18 Jul 2016 • Shizhan Zhu, Sifei Liu, Chen Change Loy, Xiaoou Tang
We present a novel framework for hallucinating faces of unconstrained poses and with very low resolution (face size as small as 5pxIOD).
Ranked #5 on Image Super-Resolution on VggFace2 - 8x upscaling
no code implementations • CVPR 2016 • Shizhan Zhu, Cheng Li, Chen-Change Loy, Xiaoou Tang
We present a practical approach to address the problem of unconstrained face alignment for a single image.
Ranked #19 on Face Alignment on AFLW-19
no code implementations • 20 Nov 2015 • Shizhan Zhu, Cheng Li, Chen Change Loy, Xiaoou Tang
The unified framework seamlessly handles different viewpoints and landmark protocols, and it is trained by optimising directly on landmark locations, thus yielding superior results on arbitrary-view face alignment.
1 code implementation • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 • Shizhan Zhu, Cheng Li, Chen Change Loy, Xiaoou Tang
We present a novel face alignment framework based on coarse-to-fine shape searching.
Ranked #20 on Face Alignment on AFLW-19
no code implementations • 2 Sep 2014 • Shizhan Zhu, Cheng Li, Chen Change Loy, Xiaoou Tang
We show extensive results on combining various popular databases (LFW, AFLW, LFPW, HELEN) for improved cross-dataset and unseen data alignment.