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Tzofi Klinghoffer, Xiaoyu Xiang, Siddharth Somasundaram, Yuchen Fan, Christian Richardt, Ramesh Raskar, Rakesh Ranjan

3D reconstruction from a single-view is challenging because of the ambiguity from monocular cues and lack of information about occluded regions. Neural radiance fields (NeRF) while popular for view synthesis and 3D reconstruction are typically reliant on multi-view images. Existing methods for single-view 3D reconstruction with NeRF rely on either data priors to hallucinate views of occluded regions which may not be physically accurate or shadows observed by RGB cameras which are difficult to detect in ambient light and low albedo backgrounds. We propose using time-of-flight data captured by a single-photon avalanche diode to overcome these limitations. Our method models two-bounce optical paths with NeRF using lidar transient data for supervision. By leveraging the advantages of both NeRF and two-bounce light measured by lidar we demonstrate that we can reconstruct visible and occluded geometry without data priors or reliance on controlled ambient lighting or scene albedo. In addition we demonstrate improved generalization under practical constraints on sensor spatial- and temporal-resolution. We believe our method is a promising direction as single-photon lidars become ubiquitous on consumer devices such as phones tablets and headsets.

Yiqun Mei, Yu Zeng, He Zhang, Zhixin Shu, Xuaner Zhang, Sai Bi, Jianming Zhang, HyunJoon Jung, Vishal M. Patel

At the core of portrait photography is the search for ideal lighting and viewpoint. The process often requires advanced knowledge in photography and an elaborate studio setup. In this work we propose Holo-Relighting a volumetric relighting method that is capable of synthesizing novel viewpoints and novel lighting from a single image. Holo-Relighting leverages the pretrained 3D GAN (EG3D) to reconstruct geometry and appearance from an input portrait as a set of 3D-aware features. We design a relighting module conditioned on a given lighting to process these features and predict a relit 3D representation in the form of a tri-plane which can render to an arbitrary viewpoint through volume rendering. Besides viewpoint and lighting control Holo-Relighting also takes the head pose as a condition to enable head-pose-dependent lighting effects. With these novel designs Holo-Relighting can generate complex non-Lambertian lighting effects (e.g. specular highlights and cast shadows) without using any explicit physical lighting priors. We train Holo-Relighting with data captured with a light stage and propose two data-rendering techniques to improve the data quality for training the volumetric relighting system. Through quantitative and qualitative experiments we demonstrate Holo-Relighting can achieve state-of-the-arts relighting quality with better photorealism 3D consistency and controllability.

Eunsu Baek, Keondo Park, Jiyoon Kim, Hyung-Sin Kim

Computer vision applications predict on digital images acquired by a camera from physical scenes through light. However conventional robustness benchmarks rely on perturbations in digitized images diverging from distribution shifts occurring in the image acquisition process. To bridge this gap we introduce a new distribution shift dataset ImageNet-ES comprising variations in environmental and camera sensor factors by directly capturing 202k images with a real camera in a controllable testbed. With the new dataset we evaluate out-of-distribution (OOD) detection and model robustness. We find that existing OOD detection methods do not cope with the covariate shifts in ImageNet-ES implying that the definition and detection of OOD should be revisited to embrace real-world distribution shifts. We also observe that the model becomes more robust in both ImageNet-C and -ES by learning environment and sensor variations in addition to existing digital augmentations. Lastly our results suggest that effective shift mitigation via camera sensor control can significantly improve performance without increasing model size. With these findings our benchmark may aid future research on robustness OOD and camera sensor control for computer vision. Our code and dataset are available at https://github.com/Edw2n/ImageNet-ES.

Yuxuan Zhou, Xudong Yan, Zhi-Qi Cheng, Yan Yan, Qi Dai, Xian-Sheng Hua

Graph Convolutional Networks (GCNs) have long set the state-of-the-art in skeleton-based action recognition leveraging their ability to unravel the complex dynamics of human joint topology through the graph's adjacency matrix. However an inherent flaw has come to light in these cutting-edge models: they tend to optimize the adjacency matrix jointly with the model weights. This process while seemingly efficient causes a gradual decay of bone connectivity data resulting in a model indifferent to the very topology it sought to represent. To remedy this we propose a two-fold strategy: (1) We introduce an innovative approach that encodes bone connectivity by harnessing the power of graph distances to describe the physical topology; we further incorporate action-specific topological representation via persistent homology analysis to depict systemic dynamics. This preserves the vital topological nuances often lost in conventional GCNs. (2) Our investigation also reveals the redundancy in existing GCNs for multi-relational modeling which we address by proposing an efficient refinement to Graph Convolutions (GC) - the BlockGC. This significantly reduces parameters while improving performance beyond original GCNs. Our full model BlockGCN establishes new benchmarks in skeleton-based action recognition across all model categories. Its high accuracy and lightweight design most notably on the large-scale NTU RGB+D 120 dataset stand as strong validation of the efficacy of BlockGCN.

Pehuen Moure, Longbiao Cheng, Joachim Ott, Zuowen Wang, Shih-Chii Liu

In order for reinforcement learning (RL) agents to be deployed in real-world environments they must be able to generalize to unseen environments. However RL struggles with out-of-distribution generalization often due to over-fitting the particulars of the training environment. Although regularization techniques from supervised learning can be applied to avoid over-fitting the differences between supervised learning and RL limit their application. To address this we propose the Signal-to-Noise Ratio regulated Parameter Uncertainty Network (SNR PUN) for RL. We introduce SNR as a new measure of regularizing the parameter uncertainty of a network and provide a formal analysis explaining why SNR regularization works well for RL. We demonstrate the effectiveness of our proposed method to generalize in several simulated environments; and in a physical system showing the possibility of using SNR PUN for applying RL to real-world applications.

Giuseppe Vecchio, Valentin Deschaintre

We introduce MatSynth a dataset of 4000+ CC0 ultra-high resolution PBR materials. Materials are crucial components of virtual relightable assets defining the interaction of light at the surface of geometries. Given their importance significant research effort was dedicated to their representation creation and acquisition. However in the past 6 years most research in material acquisition or generation relied either on the same unique dataset or on company-owned huge library of procedural materials. With this dataset we propose a significantly larger more diverse and higher resolution set of materials than previously publicly available. We carefully discuss the data collection process and demonstrate the benefits of this dataset for material acquisition and generation applications. The complete data further contains metadata with each material's origin license category tags creation method and when available descriptions and physical size as well as 3M+ renderings of the augmented materials in 1K under various environment lightings. The MatSynth dataset is released through the project page at: https://www.gvecchio.com/matsynth.

Hongchi Xia, Zhi-Hao Lin, Wei-Chiu Ma, Shenlong Wang

Creating high-quality and interactive virtual environments such as games and simulators often involves complex and costly manual modeling processes. In this paper we present Video2Game a novel approach that automatically converts videos of real-world scenes into realistic and interactive game environments. At the heart of our system are three core components: (i) a neural radiance fields (NeRF) module that effectively captures the geometry and visual appearance of the scene; (ii) a mesh module that distills the knowledge from NeRF for faster rendering; and (iii) a physics module that models the interactions and physical dynamics among the objects. By following the carefully designed pipeline one can construct an interactable and actionable digital replica of the real world. We benchmark our system on both indoor and large-scale outdoor scenes. We show that we can not only produce highly-realistic renderings in real-time but also build interactive games on top.

Haimei Zhao, Jing Zhang, Zhuo Chen, Shanshan Zhao, Dacheng Tao

LiDAR semantic segmentation (LSS) is a critical task in autonomous driving and has achieved promising progress. However prior LSS methods are conventionally investigated and evaluated on datasets within the same domain in clear weather. The robustness of LSS models in unseen scenes and all weather conditions is crucial for ensuring safety and reliability in real applications. To this end we propose UniMix a universal method that enhances the adaptability and generalizability of LSS models. UniMix first leverages physically valid adverse weather simulation to construct a Bridge Domain which serves to bridge the domain gap between the clear weather scenes and the adverse weather scenes. Then a Universal Mixing operator is defined regarding spatial intensity and semantic distributions to create the intermediate domain with mixed samples from given domains. Integrating the proposed two techniques into a teacher-student framework UniMix efficiently mitigates the domain gap and enables LSS models to learn weather-robust and domain-invariant representations. We devote UniMix to two main setups: 1) unsupervised domain adaption adapting the model from the clear weather source domain to the adverse weather target domain; 2) domain generalization learning a model that generalizes well to unseen scenes in adverse weather. Extensive experiments validate the effectiveness of UniMix across different tasks and datasets all achieving superior performance over state-of-the-art methods. The code will be released.

Jiahong Wang, Yinwei Du, Stelian Coros, Bernhard Thomaszewski

We propose a self-supervised approach for learning physics-based subspaces for real-time simulation. Existing learning-based methods construct subspaces by approximating pre-defined simulation data in a purely geometric way. However this approach tends to produce high-energy configurations leads to entangled latent space dimensions and generalizes poorly beyond the training set. To overcome these limitations we propose a self-supervised approach that directly minimizes the system's mechanical energy during training. We show that our method leads to learned subspaces that reflect physical equilibrium constraints resolve overfitting issues of previous methods and offer interpretable latent space parameters.

Agastya Kalra, Guy Stoppi, Dmitrii Marin, Vage Taamazyan, Aarrushi Shandilya, Rishav Agarwal, Anton Boykov, Tze Hao Chong, Michael Stark

6DoF Pose estimation has been gaining increased importance in vision for over a decade however it does not yet meet the reliability and accuracy standards for mass deployment in industrial robotics. To this effect we present the Industrial Plenoptic Dataset (IPD): the first dataset for the co-evaluation of cameras HDR and algorithms targeted at reliable high-accuracy industrial automation. Specifically we capture 2300 physical scenes of 20 industrial parts covering a 1mx1mx0.5m working volume resulting in over 100000 distinct object views. Each scene is captured with 13 well-calibrated multi-modal cameras including polarization and high-resolution structured light. In terms of lighting we capture each scene at 4 exposures and in 3 challenging lighting conditions ranging from 100 lux to 100000 lux. We also present validate and analyze robot consistency an evaluation method targeted at scalable high accuracy evaluation. We hope that vision systems that succeed on this dataset will have direct industry impact. The dataset and evaluation code are available at https://github.com/intrinsic-ai/ipd.

Takami Sato, Justin Yue, Nanze Chen, Ningfei Wang, Qi Alfred Chen

Denoising probabilistic diffusion models have shown breakthrough performance to generate more photo-realistic images or human-level illustrations than the prior models such as GANs. This high image-generation capability has stimulated the creation of many downstream applications in various areas. However we find that this technology is actually a double-edged sword: We identify a new type of attack called the Natural Denoising Diffusion (NDD) attack based on the finding that state-of-the-art deep neural network (DNN) models still hold their prediction even if we intentionally remove their robust features which are essential to the human visual system (HVS) through text prompts. The NDD attack shows a significantly high capability to generate low-cost model-agnostic and transferable adversarial attacks by exploiting the natural attack capability in diffusion models. To systematically evaluate the risk of the NDD attack we perform a large-scale empirical study with our newly created dataset the Natural Denoising Diffusion Attack (NDDA) dataset. We evaluate the natural attack capability by answering 6 research questions. Through a user study we find that it can achieve an 88% detection rate while being stealthy to 93% of human subjects; we also find that the non-robust features embedded by diffusion models contribute to the natural attack capability. To confirm the model-agnostic and transferable attack capability we perform the NDD attack against the Tesla Model 3 and find that 73% of the physically printed attacks can be detected as stop signs. Our hope is that the study and dataset can help our community be aware of the risks in diffusion models and facilitate further research toward robust DNN models.

Zhongyu Li, Lei Zhang

Scene recovery the restoration of images degraded by adverse weather conditions presents significant challenges for existing methods. Physical models constrained by their inherent assumptions often fail when these assumptions are not met; Deep learning models are powerful they are limited by the diversity of their training datasets leading to poor generalization and high computational demands. To address these limitations we propose the Luminous Flux Prior (LFP) to recover degraded images under diverse adverse weather without learning. Luminous flux a physical measure that reflects image brightness has a rate of change that demonstrates a significant correlation with transmission. Consequently we leverage this rate of change in luminous flux as prior knowledge to estimate transmission which in turn assists in image recovery. This approach reduces dependency on physical parameters and enhances adaptability to various weather. Experimental validation under diverse conditions such as sandstorms underwater environments and haze attests to the robustness of LFP in restoring clear images. With a time complexity of \mathcal O (N\log N) LFP enables real-time recovery making it a suitable for devices with limited computational resources.

Xin Huang, Ruizhi Shao, Qi Zhang, Hongwen Zhang, Ying Feng, Yebin Liu, Qing Wang

Recent text-to-3D methods employing diffusion models have made significant advancements in 3D human generation. However these approaches face challenges due to the limitations of text-to-image diffusion models which lack an understanding of 3D structures. Consequently these methods struggle to achieve high-quality human generation resulting in smooth geometry and cartoon-like appearances. In this paper we propose HumanNorm a novel approach for high-quality and realistic 3D human generation. The main idea is to enhance the model's 2D perception of 3D geometry by learning a normal-adapted diffusion model and a normal-aligned diffusion model. The normal-adapted diffusion model can generate high-fidelity normal maps corresponding to user prompts with view-dependent and body-aware text. The normal-aligned diffusion model learns to generate color images aligned with the normal maps thereby transforming physical geometry details into realistic appearance. Leveraging the proposed normal diffusion model we devise a progressive geometry generation strategy and a multi-step Score Distillation Sampling (SDS) loss to enhance the performance of 3D human generation. Comprehensive experiments substantiate HumanNorm's ability to generate 3D humans with intricate geometry and realistic appearances. HumanNorm outperforms existing text-to-3D methods in both geometry and texture quality. The project page of HumanNorm is https://humannorm.github.io/.

Zhiyang Yao, Shuyang Liu, Xiaoyun Yuan, Lu Fang

Compressive spectral image reconstruction is a critical method for acquiring images with high spatial and spectral resolution. Current advanced methods which involve designing deeper networks or adding more self-attention modules are limited by the scope of attention modules and the irrelevance of attentions across different dimensions. This leads to difficulties in capturing non-local mutation features in the spatial-spectral domain and results in a significant parameter increase but only limited performance improvement. To address these issues we propose SPECAT a SPatial-spEctral Cumulative-Attention Transformer designed for high-resolution hyperspectral image reconstruction. SPECAT utilizes Cumulative-Attention Blocks (CABs) within an efficient hierarchical framework to extract features from non-local spatial-spectral details. Furthermore it employs a projection-object Dual-domain Loss Function (DLF) to integrate the optical path constraint a physical aspect often overlooked in current methodologies. Ultimately SPECAT not only significantly enhances the reconstruction quality of spectral details but also breaks through the bottleneck of mutual restriction between the cost and accuracy in existing algorithms. Our experimental results demonstrate the superiority of SPECAT achieving 40.3 dB in hyperspectral reconstruction benchmarks outperforming the state-of-the-art (SOTA) algorithms by 1.2 dB while using only 5% of the network parameters and 10% of the computational cost. The code is available at https://github.com/THU-luvision/SPECAT.

Yunkai Tang, Chengxuan Zhu, Renjie Wan, Chao Xu, Boxin Shi

Among the numerous efforts towards digitally recovering the physical world Neural Radiance Fields (NeRFs) have proved effective in most cases. However underwater scene introduces unique challenges due to the absorbing water medium the local change in lighting and the dynamic contents in the scene. We aim at developing a neural underwater scene representation for these challenges modeling the complex process of attenuation unstable in-scattering and moving objects during light transport. The proposed method can reconstruct the scenes from both established datasets and in-the-wild videos with outstanding fidelity.

Lihua Jing, Rui Wang, Wenqi Ren, Xin Dong, Cong Zou

Adversarial patch attacks present a significant threat to real-world object detectors due to their practical feasibility. Existing defense methods which rely on attack data or prior knowledge struggle to effectively address a wide range of adversarial patches. In this paper we show two inherent characteristics of adversarial patches semantic independence and spatial heterogeneity independent of their appearance shape size quantity and location. Semantic independence indicates that adversarial patches operate autonomously within their semantic context while spatial heterogeneity manifests as distinct image quality of the patch area that differs from original clean image due to the independent generation process. Based on these observations we propose PAD a novel adversarial patch localization and removal method that does not require prior knowledge or additional training. PAD offers patch-agnostic defense against various adversarial patches compatible with any pre-trained object detectors. Our comprehensive digital and physical experiments involving diverse patch types such as localized noise printable and naturalistic patches exhibit notable improvements over state-of-the-art works. Our code is available at https://github.com/Lihua-Jing/PAD.

Zhenye Luo, Min Ren, Xuecai Hu, Yongzhen Huang, Li Yao

Generating dances that are both lifelike and well-aligned with music continues to be a challenging task in the cross-modal domain. This paper introduces PopDanceSet the first dataset tailored to the preferences of young audiences enabling the generation of aesthetically oriented dances. And it surpasses the AIST++ dataset in music genre diversity and the intricacy and depth of dance movements. Moreover the proposed POPDG model within the iDDPM framework enhances dance diversity and through the Space Augmentation Algorithm strengthens spatial physical connections between human body joints ensuring that increased diversity does not compromise generation quality. A streamlined Alignment Module is also designed to improve the temporal alignment between dance and music. Extensive experiments show that POPDG achieves SOTA results on two datasets. Furthermore the paper also expands on current evaluation metrics. The dataset and code are available at https://github.com/Luke-Luo1/POPDG.

Yichen Li, Kaichun Mo, Yueqi Duan, He Wang, Jiequan Zhang, Lin Shao

Shape assembly composes complex shapes geometries by arranging simple part geometries and has wide applications in autonomous robotic assembly and CAD modeling. Existing works focus on geometry reasoning and neglect the actual physical assembly process of matching and fitting joints which are the contact surfaces connecting different parts. In this paper we consider contacting joints for the task of multi-part assembly. A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment. We propose a hierarchical graph learning approach composed of two levels of graph representation learning. The part graph takes part geometries as input to build the desired shape structure. The joint-level graph uses part joints information and focuses on matching and aligning joints. The two kinds of information are combined to achieve the bilateral objectives. Extensive experiments demonstrate that our method outperforms previous methods achieving better shape structure and higher joint alignment accuracy.

Mingyuan Zhou, Rakib Hyder, Ziwei Xuan, Guojun Qi

Recent advances in 3D avatar generation have gained significant attention. These breakthroughs aim to produce more realistic animatable avatars narrowing the gap between virtual and real-world experiences. Most of existing works employ Score Distillation Sampling (SDS) loss combined with a differentiable renderer and text condition to guide a diffusion model in generating 3D avatars. However SDS often generates over-smoothed results with few facial details thereby lacking the diversity compared with ancestral sampling. On the other hand other works generate 3D avatar from a single image where the challenges of unwanted lighting effects perspective views and inferior image quality make them difficult to reliably reconstruct the 3D face meshes with the aligned complete textures. In this paper we propose a novel 3D avatar generation approach termed UltrAvatar with enhanced fidelity of geometry and superior quality of physically based rendering (PBR) textures without unwanted lighting. To this end the proposed approach presents a diffuse color extraction model and an authenticity guided texture diffusion model. The former removes the unwanted lighting effects to reveal true diffuse colors so that the generated avatars can be rendered under various lighting conditions. The latter follows two gradient-based guidances for generating PBR textures to render diverse face-identity features and details better aligning with 3D mesh geometry. We demonstrate the effectiveness and robustness of the proposed method outperforming the state-of-the-art methods by a large margin in the experiments.

Khiem Vuong, N Dinesh Reddy, Robert Tamburo, Srinivasa G. Narasimhan

Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments partly due to the lack of large-scale labeled ground-truth annotations for learning occlusion. In this work we introduce a novel framework for automatically generating a large realistic dataset of dynamic objects under occlusions using freely available time-lapse imagery. By leveraging off-the-shelf 2D (bounding box segmentation keypoint) and 3D (pose shape) predictions as pseudo-groundtruth unoccluded 3D objects are identified automatically and composited into the background in a clip-art style ensuring realistic appearances and physically accurate occlusion configurations. The resulting clip-art image with pseudo-groundtruth enables efficient training of object reconstruction methods that are robust to occlusions. Our method demonstrates significant improvements in both 2D and 3D reconstruction particularly in scenarios with heavily occluded objects like vehicles and people in urban scenes.