Existing photorealistic relightable hand models require extensive identity-specific observations in different views poses and illuminations and face challenges in generalizing to natural illuminations and novel identities. To bridge this gap we present URHand the first universal relightable hand model that generalizes across viewpoints poses illuminations and identities. Our model allows few-shot personalization using images captured with a mobile phone and is ready to be photorealistically rendered under novel illuminations. To simplify the personalization process while retaining photorealism we build a powerful universal relightable prior based on neural relighting from multi-view images of hands captured in a light stage with hundreds of identities. The key challenge is scaling the cross-identity training while maintaining personalized fidelity and sharp details without compromising generalization under natural illuminations. To this end we propose a spatially varying linear lighting model as the neural renderer that takes physics-inspired shading as input feature. By removing non-linear activations and bias our specifically designed lighting model explicitly keeps the linearity of light transport. This enables single-stage training from light-stage data while generalizing to real-time rendering under arbitrary continuous illuminations across diverse identities. In addition we introduce the joint learning of a physically based model and our neural relighting model which further improves fidelity and generalization. Extensive experiments show that our approach achieves superior performance over existing methods in terms of both quality and generalizability. We also demonstrate quick personalization of URHand from a short phone scan of an unseen identity.
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A Physics-informed Low-rank Deep Neural Network for Blind and Universal Lens Aberration Correction
PDF ↗High-end lenses although offering high-quality images suffer from both insufficient affordability and bulky design which hamper their applications in low-budget scenarios or on low-payload platforms. A flexible scheme is to tackle the optical aberration of low-end lenses computationally. However it is highly demanded but quite challenging to build a general model capable of handling non-stationary aberrations and covering diverse lenses especially in a blind manner. To address this issue we propose a universal solution by extensively utilizing the physical properties of camera lenses: (i) reducing the complexity of lens aberrations i.e. lens-specific non-stationary blur by warping annual-ring-shaped sub-images into rectangular stripes to transform non-uniform degenerations into a uniform one (ii) building a low-dimensional non-negative orthogonal representation of lens blur kernels to cover diverse lenses; (iii) designing a decoupling network to decompose the input low-quality image into several components degenerated by above kernel bases and applying corresponding pre-trained deconvolution networks to reverse the degeneration. Benefiting from the proper incorporation of lenses' physical properties and unique network design the proposed method achieves superb imaging quality wide applicability for various lenses high running efficiency and is totally free of kernel calibration. These advantages bring great potential for scenarios requiring lightweight high-quality photography.
In this paper we study a new problem Film Removal (FR) which attempts to remove the interference of wrinkled transparent films and reconstruct the original information under films for industrial recognition systems. We first physically model the imaging of industrial materials covered by the film. Considering the specular highlight from the film can be effectively recorded by the polarized camera we build a practical dataset with polarization information containing paired data with and without transparent film. We aim to remove interference from the film (specular highlights and other degradations) with an end-to-end framework. To locate the specular highlight we use an angle estimation network to optimize the polarization angle with the minimized specular highlight. The image with minimized specular highlight is set as a prior for supporting the reconstruction network. Based on the prior and the polarized images the reconstruction network can decouple all degradations from the film. Extensive experiments show that our framework achieves SOTA performance in both image reconstruction and industrial downstream tasks. Our code will be released at https://github.com/jqtangust/FilmRemoval.
We propose CG-HOI the first method to address the task of generating dynamic 3D human-object interactions (HOIs) from text. We model the motion of both human and object in an interdependent fashion as semantically rich human motion rarely happens in isolation without any interactions. Our key insight is that explicitly modeling contact between the human body surface and object geometry can be used as strong proxy guidance both during training and inference. Using this guidance to bridge human and object motion enables generating more realistic and physically plausible interaction sequences where the human body and corresponding object move in a coherent manner. Our method first learns to model human motion object motion and contact in a joint diffusion process inter-correlated through cross-attention. We then leverage this learned contact for guidance during inference to synthesize realistic and coherent HOIs. Extensive evaluation shows that our joint contact-based human-object interaction approach generates realistic and physically plausible sequences and we show two applications highlighting the capabilities of our method. Conditioned on a given object trajectory we can generate the corresponding human motion without re-training demonstrating strong human-object interdependency learning. Our approach is also flexible and can be applied to static real-world 3D scene scans.
HOIAnimator: Generating Text-prompt Human-object Animations using Novel Perceptive Diffusion Models
PDF ↗To date the quest to rapidly and effectively produce human-object interaction (HOI) animations directly from textual descriptions stands at the forefront of computer vision research. The underlying challenge demands both a discriminating interpretation of language and a comprehensive physics-centric model supporting real-world dynamics. To ameliorate this paper advocates HOIAnimator a novel and interactive diffusion model with perception ability and also ingeniously crafted to revolutionize the animation of complex interactions from linguistic narratives. The effectiveness of our model is anchored in two ground-breaking innovations: (1) Our Perceptive Diffusion Models (PDM) brings together two types of models: one focused on human movements and the other on objects. This combination allows for animations where humans and objects move in concert with each other making the overall motion more realistic. Additionally we propose a Perceptive Message Passing (PMP) mechanism to enhance the communication bridging the two models ensuring that the animations are smooth and unified; (2) We devise an Interaction Contact Field (ICF) a sophisticated model that implicitly captures the essence of HOIs. Beyond mere predictive contact points the ICF assesses the proximity of human and object to their respective environment informed by a probabilistic distribution of interactions learned throughout the denoising phase. Our comprehensive evaluation showcases HOIanimator's superior ability to produce dynamic context-aware animations that surpass existing benchmarks in text-driven animation synthesis.
PhysPT: Physics-aware Pretrained Transformer for Estimating Human Dynamics from Monocular Videos
PDF ↗While current methods have shown promising progress on estimating 3D human motion from monocular videos their motion estimates are often physically unrealistic because they mainly consider kinematics. In this paper we introduce Physics-aware Pretrained Transformer (PhysPT) which improves kinematics-based motion estimates and infers motion forces. PhysPT exploits a Transformer encoder-decoder backbone to effectively learn human dynamics in a self-supervised manner. Moreover it incorporates physics principles governing human motion. Specifically we build a physics-based body representation and contact force model. We leverage them to impose novel physics-inspired training losses (i.e. force loss contact loss and Euler-Lagrange loss) enabling PhysPT to capture physical properties of the human body and the forces it experiences. Experiments demonstrate that once trained PhysPT can be directly applied to kinematics-based estimates to significantly enhance their physical plausibility and generate favourable motion forces. Furthermore we show that these physically meaningful quantities translate into improved accuracy of an important downstream task: human action recognition.
Multi-view inverse rendering is the problem of estimating the scene parameters such as shapes materials or illuminations from a sequence of images captured under different viewpoints. Many approaches however assume single light bounce and thus fail to recover challenging scenarios like inter-reflections. On the other hand simply extending those methods to consider multi-bounced light requires more assumptions to alleviate the ambiguity. To address this problem we propose Neural Incident Stokes Fields (NeISF) a multi-view inverse rendering framework that reduces ambiguities using polarization cues. The primary motivation for using polarization cues is that it is the accumulation of multi-bounced light providing rich information about geometry and material. Based on this knowledge the proposed incident Stokes field efficiently models the accumulated polarization effect with the aid of an original physically-based differentiable polarimetric renderer. Lastly experimental results show that our method outperforms the existing works in synthetic and real scenarios.
Text-to-image generative models can generate high-quality humans but realism is lost when generating hands. Common artifacts include irregular hand poses shapes incorrect numbers of fingers and physically implausible finger orientations. To generate images with realistic hands we propose a novel diffusion-based architecture called HanDiffuser that achieves realism by injecting hand embeddings in the generative process. HanDiffuser consists of two components: a Text-to-Hand-Params diffusion model to generate SMPL-Body and MANO-Hand parameters from input text prompts and a Text-Guided Hand-Params-to-Image diffusion model to synthesize images by conditioning on the prompts and hand parameters generated by the previous component. We incorporate multiple aspects of hand representation including 3D shapes and joint-level finger positions orientations and articulations for robust learning and reliable performance during inference. We conduct extensive quantitative and qualitative experiments and perform user studies to demonstrate the efficacy of our method in generating images with high-quality hands.
We propose a new method for cloth digitalization. Deviating from existing methods which learn from data captured under relatively casual settings we propose to learn from data captured in strictly tested measuring protocols and find plausible physical parameters of the cloths. However such data is currently absent so we first propose a new dataset with accurate cloth measurements. Further the data size is considerably smaller than the ones in current deep learning due to the nature of the data capture process. To learn from small data we propose a new Bayesian differentiable cloth model to estimate the complex material heterogeneity of real cloths. It can provide highly accurate digitalization from very limited data samples. Through exhaustive evaluation and comparison we show our method is accurate in cloth digitalization efficient in learning from limited data samples and general in capturing material variations. Code and data are available in: https://github.com/realcrane/Bayesian-Differentiable-Physics-for-Cloth-Digitalization
We present En3D an enhanced generative scheme for sculpting high-quality 3D human avatars. Unlike previous works that rely on scarce 3D datasets or limited 2D collections with imbalanced viewing angles and imprecise pose priors our approach aims to develop a zero-shot 3D generative scheme capable of producing visually realistic geometrically accurate and content-wise diverse 3D humans without relying on pre-existing 3D or 2D assets. To address this challenge we introduce a meticulously crafted workflow that implements accurate physical modeling to learn the enhanced 3D generative model from synthetic 2D data. During inference we integrate optimization modules to bridge the gap between realistic appearances and coarse 3D shapes. Specifically En3D comprises three modules: a 3D generator that accurately models generalizable 3D humans with realistic appearance from synthesized balanced diverse and structured human images; a geometry sculptor that enhances shape quality using multi-view normal constraints for intricate human structure; and a texturing module that disentangles explicit texture maps with fidelity and editability leveraging semantical UV partitioning and a differentiable rasterizer. Experimental results show that our approach significantly outperforms prior works in terms of image quality geometry accuracy and content diversity. We also showcase the applicability of our generated avatars for animation and editing as well as the scalability of our approach for content-style free adaptation.
Understanding and reasoning about spatial relationships is crucial for Visual Question Answering (VQA) and robotics. Vision Language Models (VLMs) have shown impressive performance in some VQA benchmarks but struggle with 3D spatial reasoning such as recognizing distances or size differences between physical objects. This limitation may stem from a lack of 3D spatial knowledge in their training data. To address this we propose training VLMs with extensive spatial reasoning data from the internet. Our approach includes developing an automatic 3D spatial VQA data generation framework capable of creating 2 billion VQA examples from 10 million real-world images. We explore various factors in the training process such as data quality training pipeline and VLM architecture. Our work introduces the first Internet-scale 3D spatial reasoning dataset in metric space. By co-training a VLM with this dataset we significantly improve its performance in both qualitative and quantitative spatial VQA. Additionally this enhanced VLM enables new applications in chain-of-thought spatial reasoning and robotics particularly in quantitative estimation.
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.
Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domains
PDF ↗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.
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.
Video2Game: Real-time Interactive Realistic and Browser-Compatible Environment from a Single Video
PDF ↗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.
UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather
PDF ↗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.
Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models
PDF ↗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.
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.
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/.
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.