CVPR 2024 Papers

While there has been remarkable progress recently in the fields of manipulation and locomotion mobile manipulation remains a long-standing challenge. Compared to locomotion or static manipulation a mobile system must make a diverse range of long-horizon tasks feasible in unstructured and dynamic environments. While the applications are broad and interesting there are a plethora of challenges in developing these systems such as coordination between the base and arm reliance on onboard perception for perceiving and interacting with the environment and most importantly simultaneously integrating all these parts together. Prior works approach the problem using disentangled modular skills for mobility and manipulation that are trivially tied together. This causes several limitations such as compounding errors delays in decision-making and no whole-body coordination. In this work we present a reactive mobile manipulation framework that uses an active visual system to consciously perceive and react to its environment. Similar to how humans leverage whole-body and hand-eye coordination we develop a mobile manipulator that exploits its ability to move and see more specifically -- to move in order to see and to see in order to move. This allows it to not only move around and interact with its environment but also choose "when" to perceive "what" using an active visual system. We observe that such an agent learns to navigate around complex cluttered scenarios while displaying agile whole-body coordination using only ego-vision without needing to create environment maps. Videos are available at https://spin-robot.github.io

Event cameras offer many advantages over traditional frame-based cameras such as high dynamic range and low latency. Therefore event cameras are widely applied in diverse computer vision applications where event-based keypoint detection is a fundamental task. However achieving robust event-based keypoint detection remains challenging because the ground truth of event keypoints is difficult to obtain descriptors extracted by CNN usually lack discriminative ability in the presence of intense noise and fixed keypoint detectors are limited in detecting varied keypoint patterns. To address these challenges a novel event-based keypoint detection method is proposed by learning dynamic detectors and contextual descriptors in a self-supervised manner (SD2Event) including a contextual feature descriptor learning (CFDL) module and a dynamic keypoint detector learning (DKDL) module. The proposed SD2Event enjoys several merits. First the proposed CFDL module can model long-range contexts efficiently and effectively. Second the DKDL module generates dynamic keypoint detectors which can detect keypoints with diverse patterns across various event streams. Third the proposed self-supervised signals can guide the model's adaptation to event data. Extensive experimental results on three challenging benchmarks show that our proposed method significantly outperforms stateof-the-art event-based keypoint detection methods.

Revolutionizing the field of deep learning Transformer-based models have achieved remarkable performance in many tasks. Recent research has recognized these models are robust to shuffling but are limited to inter-token permutation in the forward propagation. In this work we propose our definition of permutation equivariance a broader concept covering both inter- and intra- token permutation in the forward and backward propagation of neural networks. We rigorously proved that such permutation equivariance property can be satisfied on most vanilla Transformer-based models with almost no adaptation. We examine the property over a range of state-of-the-art models including ViT Bert GPT and others with experimental validations. Further as a proof-of-concept we explore how real-world applications including privacy-enhancing split learning and model authorization could exploit the permutation equivariance property which implicates wider intriguing application scenarios. The code is available at https://github.com/Doby-Xu/ST

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.

The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors. Existing concept erasing methods in academia are all based on full parameter or specification-based fine-tuning from which we observe the following issues: 1) Generation alteration towards erosion: Parameter drift during target elimination causes alterations and potential deformations across all generations even eroding other concepts at varying degrees which is more evident with multi-concept erased; 2) Transfer inability & deployment inefficiency: Previous model-specific erasure impedes the flexible combination of concepts and the training-free transfer towards other models resulting in linear cost growth as the deployment scenarios increase. To achieve non-invasive precise customizable and transferable elimination we ground our erasing framework on one-dimensional adapters to erase multiple concepts from most DMs at once across versatile erasing applications. The concept-SemiPermeable structure is injected as a Membrane (SPM) into any DM to learn targeted erasing and meantime the alteration and erosion phenomenon is effectively mitigated via a novel Latent Anchoring fine-tuning strategy. Once obtained SPMs can be flexibly combined and plug-and-play for other DMs without specific re-tuning enabling timely and efficient adaptation to diverse scenarios. During generation our Facilitated Transport mechanism dynamically regulates the permeability of each SPM to respond to different input prompts further minimizing the impact on other concepts. Quantitative and qualitative results across 40 concepts 7 DMs and 4 erasing applications have demonstrated the superior erasing of SPM. Our code and pre-tuned SPMs are available on the project page https://lyumengyao.github.io/projects/spm.

Neural fields have emerged as a powerful and broadly applicable method for representing signals. However in contrast to classical discrete digital signal processing the portfolio of tools to process such representations is still severely limited and restricted to Euclidean domains. In this paper we address this problem by showing how a probabilistic re-interpretation of neural fields can enable their training and inference processes to become "filter-aware". The formulation we propose not only merges training and filtering in an efficient way but also generalizes beyond the familiar Euclidean coordinate spaces to the more general set of smooth manifolds and convolutions induced by the actions of Lie groups. We demonstrate how this framework can enable novel integrations of signal processing techniques for neural field applications on both Euclidean domains such as images and audio as well as non-Euclidean domains such as rotations and rays. A noteworthy benefit of our method is its applicability. Our method can be summarized as primarily a modification of the loss function and in most cases does not require changes to the network architecture or the inference process.

We present Neural 3D Strokes a novel technique to generate stylized images of a 3D scene at arbitrary novel views from multi-view 2D images. Different from existing methods which apply stylization to trained neural radiance fields at the voxel level our approach draws inspiration from image-to-painting methods simulating the progressive painting process of human artwork with vector strokes. We develop a palette of stylized 3D strokes from basic primitives and splines and consider the 3D scene stylization task as a multi-view reconstruction process based on these 3D stroke primitives. Instead of directly searching for the parameters of these 3D strokes which would be too costly we introduce a differentiable renderer that allows optimizing stroke parameters using gradient descent and propose a training scheme to alleviate the vanishing gradient issue. The extensive evaluation demonstrates that our approach effectively synthesizes 3D scenes with significant geometric and aesthetic stylization while maintaining a consistent appearance across different views. Our method can be further integrated with style loss and image-text contrastive models to extend its applications including color transfer and text-driven 3D scene drawing. Results and code are available at http://buaavrcg.github.io/Neural3DStrokes.

Brain decoding a pivotal field in neuroscience aims to reconstruct stimuli from acquired brain signals primarily utilizing functional magnetic resonance imaging (fMRI). Currently brain decoding is confined to a per-subject-per-model paradigm limiting its applicability to the same individual for whom the decoding model is trained. This constraint stems from three key challenges: 1) the inherent variability in input dimensions across subjects due to differences in brain size; 2) the unique intrinsic neural patterns influencing how different individuals perceive and process sensory information; 3) limited data availability for new subjects in real-world scenarios hampers the performance of decoding models. In this paper we present a novel approach MindBridge that achieves cross-subject brain decoding by employing only one model. Our proposed framework establishes a generic paradigm capable of addressing these challenges by introducing biological-inspired aggregation function and novel cyclic fMRI reconstruction mechanism for subject-invariant representation learning. Notably by cycle reconstruction of fMRI MindBridge can enable novel fMRI synthesis which also can serve as pseudo data augmentation. Within the framework we also devise a novel reset-tuning method for adapting a pretrained model to a new subject. Experimental results demonstrate MindBridge's ability to reconstruct images for multiple subjects which is competitive with dedicated subject-specific models. Furthermore with limited data for a new subject we achieve a high level of decoding accuracy surpassing that of subject-specific models. This advancement in cross-subject brain decoding suggests promising directions for wider applications in neuroscience and indicates potential for more efficient utilization of limited fMRI data in real-world scenarios. Project page: https://littlepure2333.github.io/MindBridge

DensePose provides a pixel-accurate association of images with 3D mesh coordinates but does not provide a 3D mesh while Human Mesh Reconstruction (HMR) systems have high 2D reprojection error as measured by DensePose localization metrics. In this work we introduce MeshPose to jointly tackle DensePose and HMR. For this we first introduce new losses that allow us to use weak DensePose supervision to accurately localize in 2D a subset of the mesh vertices ('VertexPose'). We then lift these vertices to 3D yielding a low-poly body mesh ('MeshPose'). Our system is trained in an end-to-end manner and is the first HMR method to attain competitive DensePose accuracy while also being lightweight and amenable to efficient inference making it suitable for real-time AR applications.

In this work we introduce two types of makeup prior models to extend existing 3D face prior models: PCA-based and StyleGAN2-based priors. The PCA-based prior model is a linear model that is easy to construct and is computationally efficient. However it retains only low-frequency information. Conversely the StyleGAN2-based model can represent high-frequency information with relatively higher computational cost than the PCA-based model. Although there is a trade-off between the two models both are applicable to 3D facial makeup estimation and related applications. By leveraging makeup prior models and designing a makeup consistency module we effectively address the challenges that previous methods faced in robustly estimating makeup particularly in the context of handling self-occluded faces. In experiments we demonstrate that our approach reduces computational costs by several orders of magnitude achieving speeds up to 180 times faster. In addition by improving the accuracy of the estimated makeup we confirm that our methods are highly advantageous for various 3D facial makeup applications such as 3D makeup face reconstruction user-friendly makeup editing makeup transfer and interpolation.

In the context of autonomous navigation of terrestrial robots the creation of realistic models for agent dynamics and sensing is a widespread habit in the robotics literature and in commercial applications where they are used for model based control and/or for localization and mapping. The more recent Embodied AI literature on the other hand focuses on modular or end-to-end agents trained in simulators like Habitat or AI-Thor where the emphasis is put on photo-realistic rendering and scene diversity but high-fidelity robot motion is assigned a less privileged role. The resulting sim2real gap significantly impacts transfer of the trained models to real robotic platforms. In this work we explore end-to-end training of agents in simulation in settings which minimize the sim2real gap both in sensing and in actuation. Our agent directly predicts (discretized) velocity commands which are maintained through closed-loop control in the real robot. The behavior of the real robot (including the underlying low-level controller) is identified and simulated in a modified Habitat simulator. Noise models for odometry and localization further contribute in lowering the sim2real gap. We evaluate on real navigation scenarios explore different localization and point goal calculation methods and report significant gains in performance and robustness compared to prior work.

Event cameras with their high temporal resolution dynamic range and low power consumption are particularly good at time-sensitive applications like deblurring and frame interpolation. However their performance is hindered by latency variability especially under low-light conditions and with fast-moving objects. This paper addresses the challenge of latency in event cameras -- the temporal discrepancy between the actual occurrence of changes in the corresponding timestamp assigned by the sensor. Focusing on event-guided deblurring and frame interpolation tasks we propose a latency correction method based on a parameterized latency model. To enable data-driven learning we develop an event-based temporal fidelity to describe the sharpness of latent images reconstructed from events and the corresponding blurry images and reformulate the event-based double integral model differentiable to latency. The proposed method is validated using synthetic and real-world datasets demonstrating the benefits of latency correction for deblurring and interpolation across different lighting conditions.

Lithic Use-Wear Analysis (LUWA) using microscopic images is an underexplored vision-for-science research area. It seeks to distinguish the worked material which is critical for understanding archaeological artifacts material interactions tool functionalities and dental records. However this challenging task goes beyond the well-studied image classification problem for common objects. It is affected by many confounders owing to the complex wear mechanism and microscopic imaging which makes it difficult even for human experts to identify the worked material successfully. In this paper we investigate the following three questions on this unique vision task for the first time:(i) How well can state-of-the-art pre-trained models (like DINOv2) generalize to the rarely seen domain? (ii) How can few-shot learning be exploited for scarce microscopic images? (iii) How do the ambiguous magnification and sensing modality influence the classification accuracy? To study these we collaborated with archaeologists and built the first open-source and the largest LUWA dataset containing 23130 microscopic images with different magnifications and sensing modalities. Extensive experiments show that existing pre-trained models notably outperform human experts but still leave a large gap for improvements. Most importantly the LUWA dataset provides an underexplored opportunity for vision and learning communities and complements existing image classification problems on common objects.

Advanced life forms sustained by the synergistic interaction of neural cognitive mechanisms continually acquire and transfer knowledge throughout their lifespan. In contrast contemporary machine learning paradigms exhibit limitations in emulating the facets of continual learning (CL). Nonetheless the emergence of large language models (LLMs) presents promising avenues for realizing CL via interactions with these models. Drawing on Complementary Learning System theory this paper presents a novel Interactive Continual Learning (ICL) framework enabled by collaborative interactions among models of various sizes. Specifically we assign the ViT model as System1 and multimodal LLM as System2. To enable the memory module to deduce tasks from class information and enhance Set2Set retrieval we propose the Class-Knowledge-Task Multi-Head Attention (CKT-MHA). Additionally to improve memory retrieval in System1 through enhanced geometric representation we introduce the CL-vMF mechanism based on the von Mises-Fisher (vMF) distribution. Meanwhile we introduce the von Mises-Fisher Outlier Detection and Interaction (vMF-ODI) strategy to identify hard examples thus enhancing collaboration between System1 and System2 for complex reasoning realization. Comprehensive evaluation of our proposed ICL demonstrates significant resistance to forgetting and superior performance relative to existing methods. Code is available at github.com/ICL.

Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities including images shapes and scenes. Subsequently a number of works have explored the use of NeFs as representations for downstream tasks e.g. classifying an image based on the parameters of a NeF that has been fit to it. However the impact of the NeF hyperparameters on their quality as downstream representation is scarcely understood and remains largely unexplored. This is in part caused by the large amount of time required to fit datasets of neural fields.In this work we propose a JAX-based library that leverages parallelization to enable fast optimization of large-scale NeF datasets resulting in a significant speed-up. With this library we perform a comprehensive study that investigates the effects of different hyperparameters on fitting NeFs for downstream tasks. In particular we explore the use of a shared initialization the effects of overtraining and the expressiveness of the network architectures used. Our study provides valuable insights on how to train NeFs and offers guidance for optimizing their effectiveness in downstream applications. Finally based on the proposed library and our analysis we propose Neural Field Arena a benchmark consisting of neural field variants of popular vision datasets including MNIST CIFAR variants of ImageNet and ShapeNetv2. Our library and the Neural Field Arena will be open-sourced to introduce standardized benchmarking and promote further research on neural fields.

In this work we present a novel dense-correspondence method for 6DoF object pose estimation from a single RGB-D image. While many existing data-driven methods achieve impressive performance they tend to be time-consuming due to their reliance on rendering-based refinement approaches. To circumvent this limitation we present HiPose which establishes 3D-3D correspondences in a coarse-to-fine manner with a hierarchical binary surface encoding. Unlike previous dense-correspondence methods we estimate the correspondence surface by employing point-to-surface matching and iteratively constricting the surface until it becomes a correspondence point while gradually removing outliers. Extensive experiments on public benchmarks LM-O YCB-V and T-Less demonstrate that our method surpasses all refinement-free methods and is even on par with expensive refinement-based approaches. Crucially our approach is computationally efficient and enables real-time critical applications with high accuracy requirements.

While modeling people wearing tight-fitting clothing has made great strides in recent years loose-fitting clothing remains a challenge. We propose a method that delivers realistic garment models from real-world images regardless of garment shape or deformation. To this end we introduce a fitting approach that utilizes shape and deformation priors learned from synthetic data to accurately capture garment shapes and deformations including large ones. Not only does our approach recover the garment geometry accurately it also yields models that can be directly used by downstream applications such as animation and simulation.

The Laplace-Beltrami operator (LBO) emerges from studying manifolds equipped with a Riemannian metric. It is often called the swiss army knife of geometry processing as it allows to capture intrinsic shape information and gives rise to heat diffusion geodesic distances and a multitude of shape descriptors. It also plays a central role in geometric deep learning. In this work we explore Finsler manifolds as a generalization of Riemannian manifolds. We revisit the Finsler heat equation and derive a Finsler heat kernel and a Finsler-Laplace-Beltrami Operator (FLBO): a novel theoretically justified anisotropic Laplace-Beltrami operator (ALBO). In experimental evaluations we demonstrate that the proposed FLBO is a valuable alternative to the traditional Riemannian-based LBO and ALBOs for spatial filtering and shape correspondence estimation. We hope that the proposed Finsler heat kernel and the FLBO will inspire further exploration of Finsler geometry in the computer vision community.

In this paper we propose an efficient data-driven solution to self-localization within a floorplan. Floorplan data is readily available long-term persistent and inherently robust to changes in the visual appearance. Our method does not require retraining per map and location or demand a large database of images of the area of interest. We propose a novel probabilistic model consisting of an observation and a novel temporal filtering module. Operating internally with an efficient ray-based representation the observation module consists of a single and a multiview module to predict horizontal depth from images and fuses their results to benefit from advantages offered by either methodology. Our method operates on conventional consumer hardware and overcomes a common limitation of competing methods that often demand upright images. Our full system meets real-time requirements while outperforming the state-of-the-art by a significant margin.