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Shaofei Wang, Bozidar Antic, Andreas Geiger, Siyu Tang

We present IntrinsicAvatar a novel approach to recovering the intrinsic properties of clothed human avatars including geometry albedo material and environment lighting from only monocular videos. Recent advancements in human-based neural rendering have enabled high-quality geometry and appearance reconstruction of clothed humans from just monocular videos. However these methods bake intrinsic properties such as albedo material and environment lighting into a single entangled neural representation. On the other hand only a handful of works tackle the problem of estimating geometry and disentangled appearance properties of clothed humans from monocular videos. They usually achieve limited quality and disentanglement due to approximations of secondary shading effects via learned MLPs. In this work we propose to model secondary shading effects explicitly via Monte-Carlo ray tracing. We model the rendering process of clothed humans as a volumetric scattering process and combine ray tracing with body articulation. Our approach can recover high-quality geometry albedo material and lighting properties of clothed humans from a single monocular video without requiring supervised pre-training using ground truth materials. Furthermore since we explicitly model the volumetric scattering process and ray tracing our model naturally generalizes to novel poses enabling animation of the reconstructed avatar in novel lighting conditions.

Amira Guesmi, Ruitian Ding, Muhammad Abdullah Hanif, Ihsen Alouani, Muhammad Shafique

Patch-based adversarial attacks were proven to compromise the robustness and reliability of computer vision systems. However their conspicuous and easily detectable nature challenge their practicality in real-world setting. To address this recent work has proposed using Generative Adversarial Networks (GANs) to generate naturalistic patches that may not attract human attention. However such approaches suffer from a limited latent space making it challenging to produce a patch that is efficient stealthy and robust to multiple real-world transformations. This paper introduces a novel approach that produces a Dynamic Adversarial Patch (DAP) designed to overcome these limitations. DAP maintains a naturalistic appearance while optimizing attack efficiency and robustness to real-world transformations. The approach involves redefining the optimization problem and introducing a novel objective function that incorporates a similarity metric to guide the patch's creation. Unlike GAN-based techniques the DAP directly modifies pixel values within the patch providing increased flexibility and adaptability to multiple transformations. Furthermore most clothing-based physical attacks assume static objects and ignore the possible transformations caused by non-rigid deformation due to changes in a person's pose. To address this limitation a `Creases Transformation' (CT) block is introduced enhancing the patch's resilience to a variety of real-world distortions. Experimental results demonstrate that the proposed approach outperforms state-of-the-art attacks achieving a success rate of up to 82.28% in the digital world when targeting the YOLOv7 detector and 65% in the physical world when targeting YOLOv3tiny detector deployed in edge-based smart cameras.

Vivek Gopalakrishnan, Neel Dey, Polina Golland

Surgical decisions are informed by aligning rapid portable 2D intraoperative images (e.g. X-rays) to a high-fidelity 3D preoperative reference scan (e.g. CT). However 2D/3D registration can often fail in practice: conventional optimization methods are prohibitively slow and susceptible to local minima while neural networks trained on small datasets fail on new patients or require impractical landmark supervision. We present DiffPose a self-supervised approach that leverages patient-specific simulation and differentiable physics-based rendering to achieve accurate 2D/3D registration without relying on manually labeled data. Preoperatively a CNN is trained to regress the pose of a randomly oriented synthetic X-ray rendered from the preoperative CT. The CNN then initializes rapid intraoperative test-time optimization that uses the differentiable X-ray renderer to refine the solution. Our work further proposes several geometrically principled methods for sampling camera poses from SE(3) for sparse differentiable rendering and for driving registration in the tangent space se(3) with geodesic and multiscale locality-sensitive losses. DiffPose achieves sub-millimeter accuracy across surgical datasets at intraoperative speeds improving upon existing unsupervised methods by an order of magnitude and even outperforming supervised baselines. Our implementation is at https://github.com/eigenvivek/DiffPose.

Hongyu Zhou, Jiahao Shao, Lu Xu, Dongfeng Bai, Weichao Qiu, Bingbing Liu, Yue Wang, Andreas Geiger, Yiyi Liao

Holistic understanding of urban scenes based on RGB images is a challenging yet important problem. It encompasses understanding both the geometry and appearance to enable novel view synthesis parsing semantic labels and tracking moving objects. Despite considerable progress existing approaches often focus on specific aspects of this task and require additional inputs such as LiDAR scans or manually annotated 3D bounding boxes. In this paper we introduce a novel pipeline that utilizes 3D Gaussian Splatting for holistic urban scene understanding. Our main idea involves the joint optimization of geometry appearance semantics and motion using a combination of static and dynamic 3D Gaussians where moving object poses are regularized via physical constraints. Our approach offers the ability to render new viewpoints in real-time yielding 2D and 3D semantic information with high accuracy and reconstruct dynamic scenes even in scenarios where 3D bounding box detection are highly noisy. Experimental results on KITTI KITTI-360 and Virtual KITTI 2 demonstrate the effectiveness of our approach. Our project page is at https://xdimlab.github.io/hugs_website.

Yutao Feng, Yintong Shang, Xuan Li, Tianjia Shao, Chenfanfu Jiang, Yin Yang

We show that physics-based simulations can be seamlessly integrated with NeRF to generate high-quality elastodynamics of real-world objects. Unlike existing methods we discretize nonlinear hyperelasticity in a meshless way obviating the necessity for intermediate auxiliary shape proxies like a tetrahedral mesh or voxel grid. A quadratic generalized moving least square is employed to capture nonlinear dynamics and large deformation on the implicit model. Such meshless integration enables versatile simulations of complex and codimensional shapes. We adaptively place the least-square kernels according to the NeRF density field to significantly reduce the complexity of the nonlinear simulation. As a result physically realistic animations can be conveniently synthesized using our method for a wide range of hyperelastic materials at an interactive rate. For more information please visit https://fytalon.github.io/pienerf.

Yunhao Li, Xiaodong Wang, Ping Wang, Xin Yuan, Peidong Liu

In this paper we explore the potential of Snapshot Com- pressive Imaging (SCI) technique for recovering the under- lying 3D scene representation from a single temporal com- pressed image. SCI is a cost-effective method that enables the recording of high-dimensional data such as hyperspec- tral or temporal information into a single image using low- cost 2D imaging sensors. To achieve this a series of spe- cially designed 2D masks are usually employed which not only reduces storage requirements but also offers potential privacy protection. Inspired by this to take one step further our approach builds upon the powerful 3D scene represen- tation capabilities of neural radiance fields (NeRF). Specif- ically we formulate the physical imaging process of SCI as part of the training of NeRF allowing us to exploit its impressive performance in capturing complex scene struc- tures. To assess the effectiveness of our method we con- duct extensive evaluations using both synthetic data and real data captured by our SCI system. Extensive experi- mental results demonstrate that our proposed approach sur- passes the state-of-the-art methods in terms of image re- construction and novel view image synthesis. Moreover our method also exhibits the ability to restore high frame- rate multi-view consistent images by leveraging SCI and the rendering capabilities of NeRF. The code is available at https://github.com/WU-CVGL/SCINeRF.

Tao Hu, Fangzhou Hong, Ziwei Liu

Dynamic human rendering from video sequences has achieved remarkable progress by formulating the rendering as a mapping from static poses to human images. However existing methods focus on the human appearance reconstruction of every single frame while the temporal motion relations are not fully explored. In this paper we propose a new 4D motion modeling paradigm SurMo that jointly models the temporal dynamics and human appearances in a unified framework with three key designs: 1) Surface-based motion encoding that models 4D human motions with an efficient compact surface-based triplane. It encodes both spatial and temporal motion relations on the dense surface manifold of a statistical body template which inherits body topology priors for generalizable novel view synthesis with sparse training observations. 2) Physical motion decoding that is designed to encourage physical motion learning by decoding the motion triplane features at timestep t to predict both spatial derivatives and temporal derivatives at the next timestep t+1 in the training stage. 3) 4D appearance decoding that renders the motion triplanes into images by an efficient volumetric surface-conditioned renderer that focuses on the rendering of body surfaces with motion learning conditioning. Extensive experiments validate the state-of-the-art performance of our new paradigm and illustrate the expressiveness of surface-based motion triplanes for rendering high-fidelity view-consistent humans with fast motions and even motion-dependent shadows. Our project page is at: https://taohuumd.github.io/projects/SurMo.

Fan Fei, Jiajun Tang, Ping Tan, Boxin Shi

This paper introduces a versatile multi-view inverse rendering framework with near- and far-field light sources. Tackling the fundamental challenge of inherent ambiguity in inverse rendering our framework adopts a lightweight yet inclusive lighting model for different near- and far-field lights thus is able to make use of input images under varied lighting conditions available during capture. It leverages observations under each lighting to disentangle the intrinsic geometry and material from the external lighting using both neural radiance field rendering and physically-based surface rendering on the 3D implicit fields. After training the reconstructed scene is extracted to a textured triangle mesh for seamless integration into industrial rendering software for various applications. Quantitatively and qualitatively tested on synthetic and real-world scenes our method shows superiority to state-of-the-art multi-view inverse rendering methods in both speed and quality.

Mohammad Sadil Khan, Elona Dupont, Sk Aziz Ali, Kseniya Cherenkova, Anis Kacem, Djamila Aouada

Reverse engineering in the realm of Computer-Aided Design (CAD) has been a longstanding aspiration though not yet entirely realized. Its primary aim is to uncover the CAD process behind a physical object given its 3D scan. We propose CAD-SIGNet an end-to-end trainable and auto-regressive architecture to recover the design history of a CAD model represented as a sequence of sketch- and-extrusion from an input point cloud. Our model learns CAD visual-language representations by layer-wise cross-attention between point cloud and CAD language embedding. In particular a new Sketch instance Guided Attention (SGA) module is proposed in order to reconstruct the fine- grained details of the sketches. Thanks to its auto-regressive nature CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an in- put point cloud but also provides multiple plausible design choices. This allows for an interactive reverse engineering scenario by providing designers with multiple next step choices along with the design process. Extensive experiments on publicly available CAD datasets showcase the effectiveness of our approach against existing baseline models in two settings namely full design history recovery and conditional auto-completion from point clouds.

Junuk Cha, Jihyeon Kim, Jae Shin Yoon, Seungryul Baek

This paper introduces the first text-guided work for generating the sequence of hand-object interaction in 3D. The main challenge arises from the lack of labeled data where existing ground-truth datasets are nowhere near generalizable in interaction type and object category which inhibits the modeling of diverse 3D hand-object interaction with the correct physical implication (e.g. contacts and semantics) from text prompts. To address this challenge we propose to decompose the interaction generation task into two subtasks: hand-object contact generation; and hand-object motion generation. For contact generation a VAE-based network takes as input a text and an object mesh and generates the probability of contacts between the surfaces of hands and the object during the interaction. The network learns a variety of local geometry structure of diverse objects that is independent of the objects' category and thus it is applicable to general objects. For motion generation a Transformer-based diffusion model utilizes this 3D contact map as a strong prior for generating physically plausible hand-object motion as a function of text prompts by learning from the augmented labeled dataset; where we annotate text labels from many existing 3D hand and object motion data. Finally we further introduce a hand refiner module that minimizes the distance between the object surface and hand joints to improve the temporal stability of the object-hand contacts and to suppress the penetration artifacts. In the experiments we demonstrate that our method can generate more realistic and diverse interactions compared to other baseline methods. We also show that our method is applicable to unseen objects. We will release our model and newly labeled data as a strong foundation for future research. Codes and data are available in: https://github.com/JunukCha/Text2HOI.

Yifei Li, Hsiao-yu Chen, Egor Larionov, Nikolaos Sarafianos, Wojciech Matusik, Tuur Stuyck

The realism of digital avatars is crucial in enabling telepresence applications with self-expression and customization. While physical simulations can produce realistic motions for clothed humans they require high-quality garment assets with associated physical parameters for cloth simulations. However manually creating these assets and calibrating their parameters is labor-intensive and requires specialized expertise. Current methods focus on reconstructing geometry but don't generate complete assets for physics-based applications. To address this gap we propose DiffAvatar a novel approach that performs body and garment co-optimization using differentiable simulation. By integrating physical simulation into the optimization loop and accounting for the complex nonlinear behavior of cloth and its intricate interaction with the body our framework recovers body and garment geometry and extracts important material parameters in a physically plausible way. Our experiments demonstrate that our approach generates realistic clothing and body shape suitable for downstream applications. We provide additional insights and results on our webpage: people.csail.mit.edu/liyifei/publication/diffavatar.

Nan Jiang, Zhiyuan Zhang, Hongjie Li, Xiaoxuan Ma, Zan Wang, Yixin Chen, Tengyu Liu, Yixin Zhu, Siyuan Huang

Confronting the challenges of data scarcity and advanced motion synthesis in human-scene interaction modeling we introduce the TRUMANS dataset alongside a novel HSI motion synthesis method. TRUMANS stands as the most comprehensive motion-captured HSI dataset currently available encompassing over 15 hours of human interactions across 100 indoor scenes. It intricately captures whole-body human motions and part-level object dynamics focusing on the realism of contact. This dataset is further scaled up by transforming physical environments into exact virtual models and applying extensive augmentations to appearance and motion for both humans and objects while maintaining interaction fidelity. Utilizing TRUMANS we devise a diffusion-based autoregressive model that efficiently generates HSI sequences of any length taking into account both scene context and intended actions. In experiments our approach shows remarkable zero-shot generalizability on a range of 3D scene datasets (e.g. PROX Replica ScanNet ScanNet++) producing motions that closely mimic original motion-captured sequences as confirmed by quantitative experiments and human studies.

Li Ma, Vasu Agrawal, Haithem Turki, Changil Kim, Chen Gao, Pedro Sander, Michael Zollhöfer, Christian Richardt

Neural radiance fields have achieved remarkable performance in modeling the appearance of 3D scenes. However existing approaches still struggle with the view-dependent appearance of glossy surfaces especially under complex lighting of indoor environments. Unlike existing methods which typically assume distant lighting like an environment map we propose a learnable Gaussian directional encoding to better model the view-dependent effects under near-field lighting conditions. Importantly our new directional encoding captures the spatially-varying nature of near-field lighting and emulates the behavior of prefiltered environment maps. As a result it enables the efficient evaluation of preconvolved specular color at any 3D location with varying roughness coefficients. We further introduce a data-driven geometry prior that helps alleviate the shape radiance ambiguity in reflection modeling. We show that our Gaussian directional encoding and geometry prior significantly improve the modeling of challenging specular reflections in neural radiance fields which helps decompose appearance into more physically meaningful components.

Bailey Miller, Hanyu Chen, Alice Lai, Ioannis Gkioulekas

We develop a theory for the representation of opaque solids as volumes. Starting from a stochastic representation of opaque solids as random indicator functions we prove the conditions under which such solids can be modeled using exponential volumetric transport. We also derive expressions for the volumetric attenuation coefficient as a functional of the probability distributions of the underlying indicator functions. We generalize our theory to account for isotropic and anisotropic scattering at different parts of the solid and for representations of opaque solids as stochastic implicit surfaces. We derive our volumetric representation from first principles which ensures that it satisfies physical constraints such as reciprocity and reversibility. We use our theory to explain compare and correct previous volumetric representations as well as propose meaningful extensions that lead to improved performance in 3D reconstruction tasks.

Qi Fang, Yinghui Fan, Yanjun Li, Junting Dong, Dingwei Wu, Weidong Zhang, Kang Chen

In this paper we focus on capturing closely interacted two-person motions from monocular videos an important yet understudied topic. Unlike less-interacted motions closely interacted motions contain frequently occurring inter-human occlusions which pose significant challenges to existing capturing algorithms. To address this problem our key observation is that close physical interactions between two subjects typically happen under very specific situations (e.g. handshake hug etc.) and such situational contexts contain strong prior semantics to help infer the poses of occluded joints. In this spirit we introduce reaction priors which are invertible neural networks that bi-directionally model the pose probability distributions of one person given the pose of the other. The learned reaction priors are then incorporated into a query-based pose estimator which is a decoder-only Transformer with self-attentions on both intra-joint and inter-joint relationships. We demonstrate that our design achieves considerably higher performance than previous methods on multiple benchmarks. What's more as existing datasets lack sufficient cases of close human-human interactions we also build a new dataset called Dual-Human to better evaluate different methods. Dual-Human contains around 2k sequences of closely interacted two-person motions each with synthetic multi-view renderings contact annotations and text descriptions. We believe that this new public dataset can significantly promote further research in this area.

Jiraphon Yenphraphai, Xichen Pan, Sainan Liu, Daniele Panozzo, Saining Xie

We present Image Sculpting a new framework for editing 2D images by incorporating tools from 3D geometry and graphics. This approach differs markedly from existing methods which are confined to 2D spaces and typically rely on textual instructions leading to ambiguity and limited control. Image Sculpting converts 2D objects into 3D enabling direct interaction with their 3D geometry. Post-editing these objects are re-rendered into 2D merging into the original image to produce high-fidelity results through a coarse-to-fine enhancement process. The framework supports precise quantifiable and physically-plausible editing options such as pose editing rotation translation 3D composition carving and serial addition. It marks an initial step towards combining the creative freedom of generative models with the precision of graphics pipelines.

Yunhao Ge, Yihe Tang, Jiashu Xu, Cem Gokmen, Chengshu Li, Wensi Ai, Benjamin Jose Martinez, Arman Aydin, Mona Anvari, Ayush K Chakravarthy 等

The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels which real-world vision datasets rarely satisfy. While current synthetic data generators offer a promising alternative particularly for embodied AI tasks they often fall short for computer vision tasks due to low asset and rendering quality limited diversity and unrealistic physical properties. We introduce the BEHAVIOR Vision Suite (BVS) a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models based on the newly developed embodied AI benchmark BEHAVIOR-1K. BVS supports a large number of adjustable parameters at the scene level (e.g. lighting object placement) the object level (e.g. joint configuration attributes such as "filled" and "folded") and the camera level (e.g. field of view focal length). Researchers can arbitrarily vary these parameters during data generation to perform controlled experiments. We showcase three example application scenarios: systematically evaluating the robustness of models across different continuous axes of domain shift evaluating scene understanding models on the same set of images and training and evaluating simulation-to-real transfer for a novel vision task: unary and binary state prediction. Project website: https://behavior-vision-suite.github.io/

Tianshu Huang, John Miller, Akarsh Prabhakara, Tao Jin, Tarana Laroia, Zico Kolter, Anthony Rowe

Simulation is an invaluable tool for radio-frequency system designers that enables rapid prototyping of various algorithms for imaging target detection classification and tracking. However simulating realistic radar scans is a challenging task that requires an accurate model of the scene radio frequency material properties and a corresponding radar synthesis function. Rather than specifying these models explicitly we propose DART - Doppler Aided Radar Tomography a Neural Radiance Field-inspired method which uses radar-specific physics to create a reflectance and transmittance-based rendering pipeline for range-Doppler images. We then evaluate DART by constructing a custom data collection platform and collecting a novel radar dataset together with accurate position and instantaneous velocity measurements from lidar-based localization. In comparison to state-of-the-art baselines DART synthesizes superior radar range-Doppler images from novel views across all datasets and additionally can be used to generate high quality tomographic images.

Yu-Pei Song, Xiao Wu, Zhaoquan Yuan, Jian-Jun Qiao, Qiang Peng

Human Mesh Recovery (HMR) aims to estimate the 3D human body from 2D images which is a challenging task due to inherent ambiguities in translating 2D observations to 3D space. A novel approach called PostureHMR is proposed to leverage a multi-step diffusion-style process which converts this task into a posture transformation from an SMPL T-pose mesh to the target mesh. To inject the learning process of posture transformation with the physical structure of the human body model a kinematics-based forward process is proposed to interpolate the intermediate state with pose and shape decomposition. Moreover a mesh-to-posture (M2P) decoder is designed by combining the input of 3D and 2D mesh constraints estimated from the image to model the posture changes in the reverse process. It mitigates the difficulties of posture change learning directly from RGB pixels. To overcome the limitation of pixel-level misalignment of modeling results with the input image a new trimap-based rendering loss is designed to highlight the areas with poor recognition. Experiments conducted on three widely used datasets demonstrate that the proposed approach outperforms the state-of-the-art methods.

Wenjing Wang, Huan Yang, Jianlong Fu, Jiaying Liu

Understanding illumination and reducing the need for supervision pose a significant challenge in low-light enhancement. Current approaches are highly sensitive to data usage during training and illumination-specific hyper-parameters limiting their ability to handle unseen scenarios. In this paper we propose a new zero-reference low-light enhancement framework trainable solely with normal light images. To accomplish this we devise an illumination-invariant prior inspired by the theory of physical light transfer. This prior serves as the bridge between normal and low-light images. Then we develop a prior-to-image framework trained without low-light data. During testing this framework is able to restore our illumination-invariant prior back to images automatically achieving low-light enhancement. Within this framework we leverage a pretrained generative diffusion model for model ability introduce a bypass decoder to handle detail distortion as well as offer a lightweight version for practicality. Extensive experiments demonstrate our framework's superiority in various scenarios as well as good interpretability robustness and efficiency. Code is available on our project homepage: http://daooshee.github.io/QuadPrior-Website/