We investigate a new task in human motion prediction which is predicting motions under unexpected physical perturbation potentially involving multiple people. Compared with existing research this task involves predicting less controlled unpremeditated and pure reactive motions in response to external impact and how such motions can propagate through people. It brings new challenges such as data scarcity and predicting complex interactions. To this end we propose a new method capitalizing differentiable physics and deep neural networks leading to an explicit Latent Differentiable Physics (LDP) model. Through experiments we demonstrate that LDP has high data efficiency outstanding prediction accuracy strong generalizability and good explainability. Since there is no similar research a comprehensive comparison with 11 adapted baselines from several relevant domains is conducted showing LDP outperforming existing research both quantitatively and qualitatively improving prediction accuracy by as much as 70% and demonstrating significantly stronger generalization.
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SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting
PDF ↗We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore to overcome the limitation of scarce high-quality lightstage data we have developed a self-supervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism.
Compared with transferable untargeted attacks transferable targeted adversarial attacks could specify the misclassification categories of adversarial samples posing a greater threat to security-critical tasks. In the meanwhile 3D adversarial samples due to their potential of multi-view robustness can more comprehensively identify weaknesses in existing deep learning systems possessing great application value. However the field of transferable targeted 3D adversarial attacks remains vacant. The goal of this work is to develop a more effective technique that could generate transferable targeted 3D adversarial examples filling the gap in this field. To achieve this goal we design a novel framework named TT3D that could rapidly reconstruct from few multi-view images into Transferable Targeted 3D textured meshes. While existing mesh-based texture optimization methods compute gradients in the high-dimensional mesh space and easily fall into local optima leading to unsatisfactory transferability and distinct distortions TT3D innovatively performs dual optimization towards both feature grid and Multi-layer Perceptron (MLP) parameters in the grid-based NeRF space which significantly enhances black-box transferability while enjoying naturalness. Experimental results show that TT3D not only exhibits superior cross-model transferability but also maintains considerable adaptability across different renders and vision tasks. More importantly we produce 3D adversarial examples with 3D printing techniques in the real world and verify their robust performance under various scenarios.
RAW images are rarely shared mainly due to its excessive data size compared to their sRGB counterparts obtained by camera ISPs. Learning the forward and inverse processes of camera ISPs has been recently demonstrated enabling physically-meaningful RAW-level image processing on input sRGB images. However existing learning-based ISP methods fail to handle the large variations in the ISP processes with respect to camera parameters such as ISO and exposure time and have limitations when used for various applications. In this paper we propose ParamISP a learning-based method for forward and inverse conversion between sRGB and RAW images that adopts a novel neural-network module to utilize camera parameters which is dubbed as ParamNet. Given the camera parameters provided in the EXIF data ParamNet converts them into a feature vector to control the ISP networks. Extensive experiments demonstrate that ParamISP achieve superior RAW and sRGB reconstruction results compared to previous methods and it can be effectively used for a variety of applications such as deblurring dataset synthesis raw deblurring HDR reconstruction and camera-to-camera transfer.
Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives
PDF ↗We propose Lodge a network capable of generating extremely long dance sequences conditioned on given music. We design Lodge as a two-stage coarse to fine diffusion architecture and propose the characteristic dance primitives that possess significant expressiveness as intermediate representations between two diffusion models. The first stage is global diffusion which focuses on comprehending the coarse-level music-dance correlation and production characteristic dance primitives. In contrast the second-stage is the local diffusion which parallelly generates detailed motion sequences under the guidance of the dance primitives and choreographic rules. In addition we propose a Foot Refine Block to optimize the contact between the feet and the ground enhancing the physical realism of the motion. Code available at https://li-ronghui.github.io/lodge
Continual Learning for Motion Prediction Model via Meta-Representation Learning and Optimal Memory Buffer Retention Strategy
PDF ↗Embodied AI such as autonomous vehicles suffers from insufficient long-tailed data because it must be obtained from the physical world. In fact data must be continuously obtained in a series of small batches and the model must also be continuously trained to achieve generalizability and scalability by improving the biased data distribution. This paper addresses the training cost and catastrophic forgetting problems when continuously updating models to adapt to incoming small batches from various environments for real-world motion prediction in autonomous driving. To this end we propose a novel continual motion prediction (CMP) learning framework based on sparse meta-representation learning and an optimal memory buffer retention strategy. In meta-representation learning a model explicitly learns a sparse representation of each driving environment from road geometry to vehicle states by training to reduce catastrophic forgetting based on an augmented modulation network with sparsity regularization. Also in the adaptation phase We develop an Optimal Memory Buffer Retention strategy that smartly preserves diverse samples by focusing on representation similarity. This approach handles the nuanced task distribution shifts characteristic of motion prediction datasets ensuring our model stays responsive to evolving input variations without requiring extensive resources. The experiment results demonstrate that the proposed method shows superior adaptation performance to the conventional continual learning approach which is developed using a synthetic dataset for the continual learning problem.
Recent years have seen immense progress in 3D computer vision and computer graphics with emerging tools that can virtualize real-world 3D environments for numerous Mixed Reality (XR) applications. However alongside immersive visual experiences immersive auditory experiences are equally vital to our holistic perception of an environment. In this paper we aim to reconstruct the spatial acoustic characteristics of an arbitrary environment given only a sparse set of (roughly 12) room impulse response (RIR) recordings and a planar reconstruction of the scene a setup that is easily achievable by ordinary users. To this end we introduce DiffRIR a differentiable RIR rendering framework with interpretable parametric models of salient acoustic features of the scene including sound source directivity and surface reflectivity. This allows us to synthesize novel auditory experiences through the space with any source audio. To evaluate our method we collect a dataset of RIR recordings and music in four diverse real environments. We show that our model outperforms state-of-the-art baselines on rendering monaural and binaural RIRs and music at unseen locations and learns physically interpretable parameters characterizing acoustic properties of the sound source and surfaces in the scene.
This paper presents a novel category agnostic model for visual rearrangement task which can help an embodied agent to physically recover the shuffled scene configuration without any category concepts to the goal configuration. Previous methods usually follow a similar architecture completing the rearrangement task by aligning the scene changes of the goal and shuffled configuration according to the semantic scene graphs. However constructing scene graphs requires the inference of category labels which not only causes the accuracy drop of the entire task but also limits the application in real world scenario. In this paper we delve deep into the essence of visual rearrangement task and focus on the two most essential issues scene change detection and scene change matching. We utilize the movement and the protrusion of point cloud to accurately identify the scene changes and match these changes depending on the similarity of category agnostic appearance feature. Moreover to assist the agent to explore the environment more efficiently and comprehensively we propose a closer-aligned-retrace exploration policy aiming to observe more details of the scene at a closer distance. We conduct extensive experiments on AI2THOR Rearrangement Challenge based on RoomR dataset and a new multi-room multi-instance dataset MrMiR collected by us. The experimental results demonstrate the effectiveness of our proposed method.
We present SimXR a method for controlling a simulated avatar from information (headset pose and cameras) obtained from AR / VR headsets. Due to the challenging viewpoint of head-mounted cameras the human body is often clipped out of view making traditional image-based egocentric pose estimation challenging. On the other hand headset poses provide valuable information about overall body motion but lack fine-grained details about the hands and feet. To synergize headset poses with cameras we control a humanoid to track headset movement while analyzing input images to decide body movement. When body parts are seen the movements of hands and feet will be guided by the images; when unseen the laws of physics guide the controller to generate plausible motion. We design an end-to-end method that does not rely on any intermediate representations and learns to directly map from images and headset poses to humanoid control signals. To train our method we also propose a large-scale synthetic dataset created using camera configurations compatible with a commercially available VR headset (Quest 2) and show promising results on real-world captures. To demonstrate the applicability of our framework we also test it on an AR headset with a forward-facing camera.
Single RGB or LiDAR is the mainstream sensor for the challenging scene flow which relies heavily on visual features to match motion features. Compared with single modality existing methods adopt a fusion strategy to directly fuse the cross-modal complementary knowledge in motion space. However these direct fusion methods may suffer the modality gap due to the visual intrinsic heterogeneous nature between RGB and LiDAR thus deteriorating motion features. We discover that event has the homogeneous nature with RGB and LiDAR in both visual and motion spaces. In this work we bring the event as a bridge between RGB and LiDAR and propose a novel hierarchical visual-motion fusion framework for scene flow which explores a homogeneous space to fuse the cross-modal complementary knowledge for physical interpretation. In visual fusion we discover that event has a complementarity (relative v.s. absolute) in luminance space with RGB for high dynamic imaging and has a complementarity (local boundary v.s. global shape) in scene structure space with LiDAR for structure integrity. In motion fusion we figure out that RGB event and LiDAR are complementary (spatial-dense temporal-dense v.s. spatiotemporal-sparse) to each other in correlation space which motivates us to fuse their motion correlations for motion continuity. The proposed hierarchical fusion can explicitly fuse the multimodal knowledge to progressively improve scene flow from visual space to motion space. Extensive experiments have been performed to verify the superiority of the proposed method.
GPLD3D: Latent Diffusion of 3D Shape Generative Models by Enforcing Geometric and Physical Priors
PDF ↗State-of-the-art man-made shape generative models usually adopt established generative models under a suitable implicit shape representation. A common theme is to perform distribution alignment which does not explicitly model important shape priors. As a result many synthetic shapes are not connected. Other synthetic shapes present problems of physical stability and geometric feasibility. This paper introduces a novel latent diffusion shape-generative model regularized by a quality checker that outputs a score of a latent code. The scoring function employs a learned function that provides a geometric feasibility score and a deterministic procedure to quantify a physical stability score. The key to our approach is a new diffusion procedure that combines the discrete empirical data distribution and a continuous distribution induced by the quality checker. We introduce a principled approach to determine the tradeoff parameters for learning the denoising network at different noise levels. Experimental results show that our approach outperforms state-of-the-art shape generations quantitatively and qualitatively on ShapeNet-v2.
iToF is a prevalent cost-effective technology for 3D perception. While its reliance on multi-measurement commonly leads to reduced performance in dynamic environments. Based on the analysis of the physical iToF imaging process we propose the iToF flow composed of crossmode transformation and uni-mode photometric correction to model the variation of measurements caused by different measurement modes and 3D motion respectively. We propose a local linear transform (LLT) based cross-mode transfer module (LCTM) for mode-varying and pixel shift compensation of cross-mode flow and uni-mode photometric correct module (UPCM) for estimating the depth-wise motion caused photometric residual of uni-mode flow. The iToF flow-based depth extraction network is proposed which could facilitate the estimation of the 4-phase measurements at each individual time for high framerate and accurate depth estimation. Extensive experiments including both simulation and real-world experiments are conducted to demonstrate the effectiveness of the proposed methods. Compared with the SOTA method our approach reduces the computation time by 75% while improving the performance by 38%. The code and database are available at https://github.com/ComputationalPerceptionLab/iToF_flow.
Grouping is inherently ambiguous due to the multiple levels of granularity in which one can decompose a scene --- should the wheels of an excavator be considered separate or part of the whole? We propose Group Anything with Radiance Fields (GARField) an approach for decomposing 3D scenes into a hierarchy of semantically meaningful groups from posed image inputs. To do this we embrace group ambiguity through physical scale: by optimizing a scale-conditioned 3D affinity feature field a point in the world can belong to different groups of different sizes. We optimize this field from a set of 2D masks provided by Segment Anything (SAM) in a way that respects coarse-to-fine hierarchy using scale to consistently fuse conflicting masks from different viewpoints. From this field we can derive a hierarchy of possible groupings via automatic tree construction or user interaction. We evaluate GARField on a variety of in-the-wild scenes and find it effectively extracts groups at many levels: clusters of objects objects and various subparts. GARField inherently represents multi-view consistent groupings and produces higher fidelity groups than the input SAM masks. GARField's hierarchical grouping could have exciting downstream applications such as 3D asset extraction or dynamic scene understanding. Project site: https://www.garfield.studio/
FineSports: A Multi-person Hierarchical Sports Video Dataset for Fine-grained Action Understanding
PDF ↗Fine-grained action analysis in multi-person sports is complex due to athletes' quick movements and intense physical confrontations which result in severe visual obstructions in most scenes. In addition accessible multi-person sports video datasets lack fine-grained action annotations in both space and time adding to the difficulty in fine-grained action analysis. To this end we construct a new multi-person basketball sports video dataset named FineSports which contains fine-grained semantic and spatial-temporal annotations on 10000 NBA game videos covering 52 fine-grained action types 16000 action instances and 123000 spatial-temporal bounding boxes. We also propose a new prompt-driven spatial-temporal action location approach called PoSTAL composed of a prompt-driven target action encoder (PTA) and an action tube-specific detector (ATD) to directly generate target action tubes with fine-grained action types without any off-line proposal generation. Extensive experiments on the FineSports dataset demonstrate that PoSTAL outperforms state-of-the-art methods. Data and code are available at https://github.com/PKU-ICST-MIPL/FineSports_CVPR2024.
We propose a method to control material attributes of objects like roughness metallic albedo and transparency in real images. Our method capitalizes on the generative prior of text-to-image models known for photorealism employing a scalar value and instructions to alter low-level material properties. Addressing the lack of datasets with controlled material attributes we generated an object-centric synthetic dataset with physically-based materials. Fine-tuning a modified pre-trained text-to-image model on this synthetic dataset enables us to edit material properties in real-world images while preserving all other attributes. We show the potential application of our model to material edited NeRFs.
Existing multi-person human reconstruction approaches mainly focus on recovering accurate poses or avoiding penetration but overlook the modeling of close interactions. In this work we tackle the task of reconstructing closely interactive humans from a monocular video. The main challenge of this task comes from insufficient visual information caused by depth ambiguity and severe inter-person occlusion. In view of this we propose to leverage knowledge from proxemic behavior and physics to compensate the lack of visual information. This is based on the observation that human interaction has specific patterns following the social proxemics. Specifically we first design a latent representation based on Vector Quantised-Variational AutoEncoder (VQ-VAE) to model human interaction. A proxemics and physics guided diffusion model is then introduced to denoise the initial distribution. We design the diffusion model as dual branch with each branch representing one individual such that the interaction can be modeled via cross attention. With the learned priors of VQ-VAE and physical constraint as the additional information our proposed approach is capable of estimating accurate poses that are also proxemics and physics plausible. Experimental results on Hi4D 3DPW and CHI3D demonstrate that our method outperforms existing approaches. The code is available at https://github.com/boycehbz/HumanInteraction.
RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation
PDF ↗Object detection in radar imagery with neural networks shows great potential for improving autonomous driving. However obtaining annotated datasets from real radar images crucial for training these networks is challenging especially in scenarios with long-range detection and adverse weather and lighting conditions where radar performance excels. To address this challenge we present RadSimReal an innovative physical radar simulation capable of generating synthetic radar images with accompanying annotations for various radar types and environmental conditions all without the need for real data collection. Remarkably our findings demonstrate that training object detection models on RadSimReal data and subsequently evaluating them on real-world data produce performance levels comparable to models trained and tested on real data from the same dataset and even achieves better performance when testing across different real datasets. RadSimReal offers advantages over other physical radar simulations that it does not necessitate knowledge of the radar design details which are often not disclosed by radar suppliers and has faster run-time. This innovative tool has the potential to advance the development of computer vision algorithms for radar-based autonomous driving applications.
We focus on the generalization ability of the 6-DoF grasp detection method in this paper. While learning-based grasp detection methods can predict grasp poses for unseen objects using the grasp distribution learned from the training set they often exhibit a significant performance drop when encountering objects with diverse shapes and structures. To enhance the grasp detection methods' generalization ability we incorporate domain prior knowledge of robotic grasping enabling better adaptation to objects with significant shape and structure differences. More specifically we employ the physical constraint regularization during the training phase to guide the model towards predicting grasps that comply with the physical rule on grasping. For the unstable grasp poses predicted on novel objects we design a contact-score joint optimization using the projection contact map to refine these poses in cluttered scenarios. Extensive experiments conducted on the GraspNet-1billion benchmark demonstrate a substantial performance gain on the novel object set and the real-world grasping experiments also demonstrate the effectiveness of our generalizing 6-DoF grasp detection method.
Action understanding matters for intelligent agents and has attracted long-term attention. It can be formed as the mapping from the action physical space to the semantic space. Typically researchers built action datasets according to idiosyncratic choices to define classes and push the envelope of benchmarks respectively. Thus datasets are incompatible with each other like "Isolated Islands" due to semantic gaps and various class granularities e.g. do housework in dataset A and wash plate in dataset B. We argue that a more principled semantic space is an urgent need to concentrate the community efforts and enable us to use all datasets together to pursue generalizable action learning. To this end we design a structured action semantic space in view of verb taxonomy hierarchy and covering massive actions. By aligning the classes of previous datasets to our semantic space we gather (image/video/skeleton/MoCap) datasets into a unified database in a unified label system i.e. bridging "isolated islands" into a "Pangea". Accordingly we propose a novel model mapping from the physical space to semantic space to fully use Pangea. In extensive experiments our new system shows significant superiority especially in transfer learning. Our code and data will be made public at https://mvig-rhos.com/pangea.
Improving Physics-Augmented Continuum Neural Radiance Field-Based Geometry-Agnostic System Identification with Lagrangian Particle Optimization
PDF ↗Geometry-agnostic system identification is a technique for identifying the geometry and physical properties of an object from video sequences without any geometric assumptions. Recently physics-augmented continuum neural radiance fields (PAC-NeRF) has demonstrated promising results for this technique by utilizing a hybrid Eulerian-Lagrangian representation in which the geometry is represented by the Eulerian grid representations of NeRF the physics is described by a material point method (MPM) and they are connected via Lagrangian particles. However a notable limitation of PAC-NeRF is that its performance is sensitive to the learning of the geometry from the first frames owing to its two-step optimization. First the grid representations are optimized with the first frames of video sequences and then the physical properties are optimized through video sequences utilizing the fixed first-frame grid representations. This limitation can be critical when learning of the geometric structure is difficult for example in a few-shot (sparse view) setting. To overcome this limitation we propose Lagrangian particle optimization (LPO) in which the positions and features of particles are optimized through video sequences in Lagrangian space. This method allows for the optimization of the geometric structure across the entire video sequence within the physical constraints imposed by the MPM. The experimental results demonstrate that the LPO is useful for geometric correction and physical identification in sparse-view settings.