Publication List
(A superscript * denotes equal contribution)-
Taming Mode Collapse in Score Distillation for Text-to-3D GenerationPeihao Wang, Dejia Xu, Zhiwen Fan, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Atlas Wang, Vikas ChandraIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024We derive an entropy-maximizing score distillation rule that fosters view diversity and addresses the multi-face problem for text-to-3D generation.[Project Page] [Paper] [Code]
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Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3DIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024We propose Lift3D that lifts arbitrary 2D backbone to generate view-consistent predictions without any retraining.[Project Page] [Paper] [Code]
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Polynomial Width is Sufficient for Set Representation with High-dimensional FeaturesInternational Conference on Learning Representations (ICLR), 2024We theoretically show that polynomial many neurons are sufficient for set representation with DeepSets architecture.[Paper]
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SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein IdentityPeihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Atlas Wang, Vikas ChandraarXiv preprint, 2023We adopt control variate method constructed by Stein identity to reduce variance in Monte Carlo estimation for text-to-3D score distillation.[MarkTechPost] [Project Page] [Paper] [Code]
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Patch Diffusion: Faster and More Data-Efficient Training of Diffusion ModelsZhendong Wang, Yifan Jiang, Huangjie Zheng, Peihao Wang, Pengcheng He, Atlas Wang, Weizhu Chen, Mingyuan ZhouAdvances in Neural Information Processing Systems (NeurIPS), 2023We propose Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training time costs while improving data efficiency.[Paper] [Code]
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Vision HGNN: An Image is More than a Graph of NodesYan Han, Peihao Wang, Souvik Kundu, Ying Ding, Atlas WangIEEE International Conference on Computer Vision (ICCV), 2023, OralIn this paper, we model an image with a hypergraph to capture high-order interactions, and further employ Hypergraph Neural Networks (HGNN) for learning representations.[Paper] [Code]Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-ExpertsWenyan Cong*, Hanxue Liang*, Peihao Wang, Zhiwen Fan, Tianlong Chen, Mukund Varma T, Yi Wang, Atlas WangIEEE International Conference on Computer Vision (ICCV), 2023We scale up and improve the generalization ability of Generalizable NeRF Transformer (GNT) via plugging in the Mixture-of-Expert (MoE) layers.[Paper] [Code]Data Efficient Neural Scaling Law via Model ReusingPeihao Wang, Rameswar Panda, Atlas WangInternational Conference on Machine Learning (ICML), 2023We empirically reveal that the existing power laws under-estimate the data inefficiency of large transformers, and leverage model reusing to reproduce the power law under the data scarcity regime.[Paper] [Code]NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360 ViewsIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023, HighlightWe study how to lift a single image to a 3D object and generate its 360° views that correspond well with the given reference image.[Project Page] [Paper] [Code]Equivariant Hypergraph Diffusion Neural OperatorsInternational Conference on Learning Representations (ICLR), 2023This work proposes a new hypergraph neural network architecture, which provably represents any continuous equivariant hypergraph diffusion operators.[Paper] [Code]Is Attention All That NeRF Needs?International Conference on Learning Representations (ICLR), 2023We present Generalizable NeRF Transformer (GNT), a pure, unified transformer-based architecture that efficiently reconstructs Neural Radiance Fields (NeRFs) on the fly.[Project Page] [Paper] [Code]NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex ScenesInternational Conference on Learning Representations (ICLR), 2023We propose a novel collaborative contrastive loss for NeRF to segment objects in complex real-world scenes, without any annotation.[Project Page] [Paper] [Code]Learning to Grow Pretrained Models for Efficient Transformer TrainingPeihao Wang, Rameswar Panda, Lucas Torroba Hennigen, Philip Greengard, Leonid Karlinsky, Rogerio Feris, David Cox, Atlas Wang, Yoon KimInternational Conference on Learning Representations (ICLR), 2023, SpotlightThis paper proposes to accelerate transformer training by re-using pretrained models via a learnable, linear and sparse model growth operator.[MIT News] [Paper] [Code]Signal Processing for Implicit Neural RepresentationsAdvances in Neural Information Processing Systems (NeurIPS), 2022We propose a theoretically grounded signal processing framework for Implicit Neural Representations (INR), which analytically manipulates INRs on the weight space through differential operators.[Project Page] [Paper] [Code]Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great AgainAdvances in Neural Information Processing Systems (NeurIPS), 2022We derive a topology-aware isometric initialization and a Dirichlet Energy guided achitectural rewiring technique that boost vanilla-GCNs to be the state-of-the-art.[Paper] [Code]Unified Implicit Neural StylizationEuropean Conference on Computer Vision (ECCV), 2022This work explores stylizing an implicit neural representation, using a generalized approach that can apply to various 2D and 3D representations.[Project Page] [Paper] [Code]SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single ImageEuropean Conference on Computer Vision (ECCV), 2022We present Single View NeRF (SinNeRF) consisting of thoughtfully designed semantic and geometry regularizations to train neural radiance field using only a single view.[Project Page] [Paper] [Code]Neural Implicit Dictionary Learning via Mixture-of-Expert TrainingInternational Conference on Machine Learning (ICML), 2022We present Neural Implicit Dictionary (NID) that learns and represents implicit neural representation as a sparse mixture of expert networks.[Paper] [Code]Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark StudyIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022We present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs.[Paper] [Code]Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level AugmentationsIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022We propose Aug-NeRF which augments NeRF with worst-case perturbations in three distinct levels with physical grounds.[Paper] [Code]CADTransformer: Panoptic Symbol Spotting Transformer for CAD DrawingsIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022, OralWe present CADTransformer, a transformer based framework, to tackle the panoptic symbol spotting task for computer-aided design drawings.[Paper] [Code]Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to PracticeInternational Conference on Learning Representations (ICLR), 2022We prove that self-attention is no more than low-pass filter, and propose two simple yet effective methods to counteract excessive smoothening.[Paper] [Code]Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical SystemsAdvances in Neural Information Processing Systems (NeurIPS), 2021We presents the Delayed Propagation Transformer (DePT) that specializes in the global modeling of CPS while taking into account the immutable constraints from the physical world.[Paper] [Code]SoGCN: Second-Order Graph Convolutional NetworksarXiv preprint, 2021We prove that second-order graph convolution is the maximally localized kernel with full representation power.[Paper] [Code]TightCap: 3D Human Shape Capture with Clothing Tightness FieldACM Transactions on Graphics (TOG), 2021We propose a data-driven approach to capture both the human shape and dressed garments with only a single 3D human scan, by predicting clothing tightness.[Project Page] [Paper] [Code]
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