Velox: Learning Representations of 4D Geometry and Appearance.
Anagh Malik, Dorian Chan, Xiaoming Zhao, David B. Lindell, Oncel Tuzel, and Jen-Hao Rick Chang
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026   
[bibtex]    We introduce a framework for learning latent representations of 4D objects which are descriptive, compressive, and accessible (requiring minimal input). LiTo: Surface Light Field Tokenization.
Jen-Hao Rick Chang*, Xiaoming Zhao*, Dorian Chan, and Oncel Tuzel
(* denotes equal contribution)
International Conference on Learning Representations (ICLR), 2026   
[bibtex]    We propose a 3D latent representation that jointly models object geometry and view-dependent appearance, enabling high-quality image-to-3D generation. Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective.
Xiaoming Zhao and Alexander G. Schwing
AAAI Conference on Artificial Intelligence (AAAI), 2026   
[Paper] [bibtex]    We find both classifier guidance and classifier-free guidance achieve conditional generation by pushing the denoising diffusion trajectories away from data distribution's decision boundaries. 3D Shape Tokenization via Latent Flow Matching.
Jen-Hao Rick Chang, Yuyang Wang, Miguel Angel Bautista, Jiatao Gu, Xiaoming Zhao, Joshua M. Susskind, and Oncel Tuzel
arXiv, 2025   
[Paper] [Website] [bibtex]    We show, for the first time, that latent 3D representations learned from modeling 3D surface probability densities can scale and perform competitively. IllumiNeRF: 3D Relighting Without Inverse Rendering.
Xiaoming Zhao, Pratul P. Srinivasan, Dor Verbin, Keunhong Park, Ricardo Martin Brualla, and Philipp Henzler
Neural Information Processing Systems (NeurIPS), 2024   
[Paper] [Results] [Website] [Leaderboard] [bibtex]   
  
IllumiNeRF provides a simpler approach than traditional inverse rendering for 3D relighting: distilling samples from a single-image relighting diffusion model into a latent-variable NeRF. GoMAvatar: Efficient Animatable Human Modeling From Monocular Video Using Gaussians-on-Mesh.
Jing Wen, Xiaoming Zhao, Zhongzheng Ren, Alexander G. Schwing, and Shenlong Wang
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024   
[Paper] [Code] [Website] [bibtex]    GoMAvatar introduces Gaussians-on-Mesh (GoM) representation for real-time, memory-efficient, and high-quality animatable human modeling. NeRFDeformer: NeRF Transformation From a Single View via 3D Scene Flows.
Zhenggang Tang, Zhongzheng Ren, Xiaoming Zhao, Bowen Wen, Jonathan Tremblay, Stan Birchfield, and Alexander G. Schwing
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024   
[Paper] [Code] [Website] [bibtex]    NeRFDeformer automatically modifies a NeRF representation based on a single RGB-D observation of a non-rigid transformed version of the original scene. Pseudo-Generalized Dynamic View Synthesis From a Video.
Xiaoming Zhao, Alex Colburn, Fangchang Ma, Miguel Angel Bautista, Joshua M. Susskind, and Alexander G. Schwing
International Conference on Learning Representations (ICLR), 2024   
[Paper] [Code] [Website] [bibtex]    PGDVS provides an analysis framework for generalized dynamic view synthesis and finds with consistent depth estimations, scene-specific appearance optimization is NOT required. Occupancy Planes for Single-View RGB-D Human Reconstruction.
Xiaoming Zhao, Yuan-Ting Hu, Zhongzheng Ren, and Alexander G. Schwing
AAAI Conference on Artificial Intelligence (AAAI), 2023   
[Paper] [Code] [bibtex]    OPlanes provides more flexibility than voxel grids and enables to better leverage correlations than per-point classification. Generative Multiplane Images: Making a 2D GAN 3D-Aware.
Xiaoming Zhao, Fangchang Ma, David Güera, Zhile Ren, Alexander G. Schwing, and Alex Colburn
European Conference on Computer Vision (ECCV), 2022 (Oral Presentation)   
[Paper] [Code] [Website] [bibtex]   
  
GMPI guarantees to be view-consistent and enables fast training (in less than half a day at a resolution of 1024²) and high FPS during inference. Initialization and Alignment for Adversarial Texture Optimization.
Xiaoming Zhao, Zhizhen Zhao, and Alexander G. Schwing
European Conference on Computer Vision (ECCV), 2022   
[Paper] [Code] [Website] [bibtex]    Carefully designed initialization and alignment procedures enable benefiting from both classical and recent learning-based texture optimization techniques. Class-agnostic Reconstruction of Dynamic Objects From Videos.
Zhongzheng Ren*, Xiaoming Zhao*, and Alexander G. Schwing
(* denotes equal contribution)
Neural Information Processing Systems (NeurIPS), 2021   
[Paper] [Website] [bibtex]    REDO enables class-agnostic geometry reconstruction for dynamic objects from RGB-D videos. The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation.
Xiaoming Zhao, Harsh Agrawal, Dhruv Batra, and Alexander G. Schwing
International Conference on Computer Vision (ICCV), 2021   
[Paper] [Code] [Website] [bibtex]    A well-trained visual odometry module can be a drop-in replacement for GPS and Compass sensor in PointGoal navigation. Mitigating Data Scarcity in Protein Binding Prediction Using Meta-Learning.
Yunan Luo*, Jianzhu Ma*, Xiaoming Zhao, Yufeng Su, Yang Liu, Trey Ideker, and Jian Peng
(* denotes equal contribution)
Research in Computational Molecular Biology (RECOMB), 2019   
[Paper] [bibtex]    Meta-learning and few-shot learning strategy can be utilized to mitigate the data scarcity issue in characterizing the specificity of less-studied kinases for protein-peptide binding prediction. Integrating Thermodynamic and Sequence Contexts Improves Protein-RNA Binding Prediction.
Yufeng Su, Yunan Luo, Xiaoming Zhao, Yang Liu, and Jian Peng
PLOS Computational Biology, 2019   
[Paper] [Code] [bibtex]    A deep learning-based thermodynamic model is introduced for protein-RNA binding prediction. Stochastic Variance Reduction for Deep Q-Learning.
Wei-Ye Zhao, Xi-Ya Guan, Yang Liu, Xiaoming Zhao, and Jian Peng
arXiv, 2019   
[Paper] [bibtex]