CVPR 2024 论文列表

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/

We present GALA a framework that takes as input a single-layer clothed 3D human mesh and decomposes it into complete multi-layered 3D assets. The outputs can then be combined with other assets to create novel clothed human avatars with any pose. Existing reconstruction approaches often treat clothed humans as a single-layer of geometry and overlook the inherent compositionality of humans with hairstyles clothing and accessories thereby limiting the utility of the meshes for down-stream applications. Decomposing a single-layer mesh into separate layers is a challenging task because it requires the synthesis of plausible geometry and texture for the severely occluded regions. Moreover even with successful decomposition meshes are not normalized in terms of poses and body shapes failing coherent composition with novel identities and poses. To address these challenges we propose to leverage the general knowledge of a pretrained 2D diffusion model as geometry and appearance prior for humans and other assets. We first separate the input mesh using the 3D surface segmentation extracted from multi-view 2D segmentations. Then we synthesize the missing geometry of different layers in both posed and canonical spaces using a novel pose-guided Score Distillation Sampling (SDS) loss. Once we complete inpainting high-fidelity 3D geometry we also apply the same SDS loss to its texture to obtain the complete appearance including the initially occluded regions. Through a series of decomposition steps we obtain multiple layers of 3D assets in a shared canonical space normalized in terms of poses and human shapes hence supporting effortless composition to novel identities and reanimation with novel poses. Our experiments demonstrate the effectiveness of our approach for decomposition canonicalization and composition tasks compared to existing solutions.

Recent years have witnessed the remarkable progress of 3D multi-modality object detection methods based on the Bird's-Eye-View (BEV) perspective. However most of them overlook the complementary interaction and guidance between LiDAR and camera. In this work we propose a novel multi-modality 3D objection detection method named GAFusion with LiDAR-guided global interaction and adaptive fusion. Specifically we introduce sparse depth guidance (SDG) and LiDAR occupancy guidance (LOG) to generate 3D features with sufficient depth information. In the following LiDAR-guided adaptive fusion transformer (LGAFT) is developed to adaptively enhance the interaction of different modal BEV features from a global perspective. Meanwhile additional downsampling with sparse height compression and multi-scale dual-path transformer (MSDPT) are designed to enlarge the receptive fields of different modal features. Finally a temporal fusion module is introduced to aggregate features from previous frames. GAFusion achieves state-of-the-art 3D object detection results with 73.6% mAP and 74.9% NDS on the nuScenes test set.

We introduce a novel 3D generative method Generative 3D Reconstruction (G3DR) in ImageNet capable of generating diverse and high-quality 3D objects from single images addressing the limitations of existing methods. At the heart of our framework is a novel depth regularization technique that enables the generation of scenes with high-geometric fidelity. G3DR also leverages a pretrained language-vision model such as CLIP to enable reconstruction in novel views and improve the visual realism of generations. Additionally G3DR designs a simple but effective sampling procedure to further improve the quality of generations. G3DR offers diverse and efficient 3D asset generation based on class or text conditioning. Despite its simplicity G3DR is able to beat state-of-theart methods improving over them by up to 22% in perceptual metrics and 90% in geometry scores while needing only half of the training time. Code is available at https://github.com/preddy5/G3DR

Novel view synthesis aims to generate new view images of a given view image collection. Recent attempts address this problem relying on 3D geometry priors (e.g. shapes sizes and positions) learned from multi-view images. However such methods encounter the following limitations: 1) they require a set of multi-view images as training data for a specific scene (e.g. face car or chair) which is often unavailable in many real-world scenarios; 2) they fail to extract the geometry priors from single-view images due to the lack of multi-view supervision. In this paper we propose a Geometry-enhanced NeRF (G-NeRF) which seeks to enhance the geometry priors by a geometry-guided multi-view synthesis approach followed by a depth-aware training. In the synthesis process inspired that existing 3D GAN models can unconditionally synthesize high-fidelity multi-view images we seek to adopt off-the-shelf 3D GAN models such as EG3D as a free source to provide geometry priors through synthesizing multi-view data. Simultaneously to further improve the geometry quality of the synthetic data we introduce a truncation method to effectively sample latent codes within 3D GAN models. To tackle the absence of multi-view supervision for single-view images we design the depth-aware training approach incorporating a depth-aware discriminator to guide geometry priors through depth maps. Experiments demonstrate the effectiveness of our method in terms of both qualitative and quantitative results.

We propose G-HOP a denoising diffusion based generative prior for hand-object interactions that allows modeling both the 3D object and a human hand conditioned on the object category. To learn a 3D spatial diffusion model that can capture this joint distribution we represent the human hand via a skeletal distance field to obtain a representation aligned with the (latent) signed distance field for the object. We show that this hand-object prior can then serve as a generic guidance to facilitate other tasks like reconstruction from interaction clip and human grasp synthesis. We believe that our model trained by aggregating several diverse real-world interaction datasets spanning 155 categories represents a first approach that allows jointly generating both hand and object. Our empirical evaluations demonstrate the benefit of this joint prior in video-based reconstruction and human grasp synthesis outperforming current task-specific baselines.

This paper proposes a novel task named "3D part grouping". Suppose there is a mixed set containing scattered parts from various shapes. This task requires algorithms to find out every possible combination among all the parts. To address this challenge we propose the so called Gradient Field-based Auto-Regressive Sampling framework (G-FARS) tailored specifically for the 3D part grouping task. In our framework we design a gradient-field-based selection graph neural network (GNN) to learn the gradients of a log conditional probability density in terms of part selection where the condition is the given mixed part set. This innovative approach implemented through the gradient-field-based selection GNN effectively captures complex relationships among all the parts in the input. Upon completion of the training process our framework becomes capable of autonomously grouping 3D parts by iteratively selecting them from the mixed part set leveraging the knowledge acquired by the trained gradient-field-based selection GNN. Our code is available at: https://github.com/J-F-Cheng/G-FARS-3DPartGrouping.

We present a generative approach to forecast long-term future human behavior in 3D requiring only weak supervision from readily available 2D human action data. This is a fundamental task enabling many downstream applications. The required ground-truth data is hard to capture in 3D (mocap suits expensive setups) but easy to acquire in 2D (simple RGB cameras). Thus we design our method to only require 2D RGB data at inference time while being able to generate 3D human motion sequences. We use a differentiable 2D projection scheme in an autoregressive manner for weak supervision and an adversarial loss for 3D regularization. Our method predicts long and complex human behavior sequences (e.g. cooking assembly) consisting of multiple sub-actions. We tackle this in a semantically hierarchical manner jointly predicting high-level coarse action labels together with their low-level fine-grained realizations as characteristic 3D human poses. We observe that these two action representations are coupled in nature and joint prediction benefits both action and pose forecasting. Our experiments demonstrate the complementary nature of joint action and 3D pose prediction: our joint approach outperforms each task treated individually enables robust longer-term sequence prediction and improves over alternative approaches to forecast actions and characteristic 3D poses.

As wearable cameras become more popular an important question emerges: how to identify camera wearers within the perspective of conventional static cameras. The drastic difference between first-person (egocentric) and third-person (exocentric) camera views makes this a challenging task. We present PersonEnvironmentNet (PEN) a framework designed to integrate information from both the individuals in the two views and geometric cues inferred from the background environment. To facilitate research in this direction we also present TF2023 a novel dataset comprising synchronized first-person and third-person views along with masks of camera wearers and labels associating these masks with the respective first-person views. In addition we propose a novel quantitative metric designed to measure a model's ability to comprehend the relationship between the two views. Our experiments reveal that PEN outperforms existing methods. The code and dataset are available at https://github.com/ziweizhao1993/PEN.

We propose functional diffusion a generative diffusion model focused on infinite-dimensional function data samples. In contrast to previous work functional diffusion works on samples that are represented by functions with a continuous domain. Functional diffusion can be seen as an extension of classical diffusion models to an infinite-dimensional domain. Functional diffusion is very versatile as images videos audio 3D shapes deformations etc. can be handled by the same framework with minimal changes. In addition functional diffusion is especially suited for irregular data or data defined in non-standard domains. In our work we derive the necessary foundations for functional diffusion and propose a first implementation based on the transformer architecture. We show generative results on complicated signed distance functions and deformation functions defined on 3D surfaces.

Principal component analysis (PCA) along with its extensions to manifolds and outlier contaminated data have been indispensable in computer vision and machine learning. In this work we present a unifying formalism for PCA and its variants and introduce a framework based on the flags of linear subspaces i.e. a hierarchy of nested linear subspaces of increasing dimension which not only allows for a common implementation but also yields novel variants not explored previously. We begin by generalizing traditional PCA methods that either maximize variance or minimize reconstruction error. We expand these interpretations to develop a wide array of new dimensionality reduction algorithms by accounting for outliers and the data manifold. To devise a common computational approach we recast robust and dual forms of PCA as optimization problems on flag manifolds. We then integrate tangent space approximations of principal geodesic analysis (tangent-PCA) into this flag-based framework creating novel robust and dual geodesic PCA variations. The remarkable flexibility offered by the `flagification' introduced here enables even more algorithmic variants identified by specific flag types. Last but not least we propose an effective convergent solver for these flag-formulations employing the Stiefel manifold. Our empirical results on both real-world and synthetic scenarios demonstrate the superiority of our novel algorithms especially in terms of robustness to outliers on manifolds.

We introduce a lightweight and accurate localization method that only utilizes the geometry of 2D-3D lines. Given a pre-captured 3D map our approach localizes a panorama image taking advantage of the holistic 360 degree view. The system mitigates potential privacy breaches or domain discrepancies by avoiding trained or hand-crafted visual descriptors. However as lines alone can be ambiguous we express distinctive yet compact spatial contexts from relationships between lines namely the dominant directions of parallel lines and the intersection between non-parallel lines. The resulting representations are efficient in processing time and memory compared to conventional visual descriptor-based methods. Given the groups of dominant line directions and their intersections we accelerate the search process to test thousands of pose candidates in less than a millisecond without sacrificing accuracy. We empirically show that the proposed 2D-3D matching can localize panoramas for challenging scenes with similar structures dramatic domain shifts or illumination changes. Our fully geometric approach does not involve extensive parameter tuning or neural network training making it a practical algorithm that can be readily deployed in the real world. Project page including the code is available through this link: https://82magnolia.github.io/fgpl/.

Model stealing (MS) involves querying and observing the output of a machine learning model to steal its capabilities. The quality of queried data is crucial yet obtaining a large amount of real data for MS is often challenging. Recent works have reduced reliance on real data by using generative models. However when high-dimensional query data is required these methods are impractical due to the high costs of querying and the risk of model collapse. In this work we propose using sample gradients (SG) to enhance the utility of each real sample as SG provides crucial guidance on the decision boundaries of the victim model. However utilizing SG in the model stealing scenario faces two challenges: 1. Pixel-level gradient estimation requires extensive query volume and is susceptible to defenses. 2. The estimation of sample gradients has a significant variance. This paper proposes Superpixel Sample Gradient stealing (SPSG) for model stealing under the constraint of limited real samples. With the basic idea of imitating the victim model's low-variance patch-level gradients instead of pixel-level gradients SPSG achieves efficient sample gradient estimation through two steps. First we perform patch-wise perturbations on query images to estimate the average gradient in different regions of the image. Then we filter the gradients through a threshold strategy to reduce variance. Exhaustive experiments demonstrate that with the same number of real samples SPSG achieves accuracy agreements and adversarial success rate significantly surpassing the current state-of-the-art MS methods. Codes are available at https://github.com/zyl123456aB/SPSG_attack.

In magnetic resonance imaging (MRI) slice-to-volume reconstruction (SVR) refers to computational reconstruction of an unknown 3D magnetic resonance volume from stacks of 2D slices corrupted by motion. While promising current SVR methods require multiple slice stacks for accurate 3D reconstruction leading to long scans and limiting their use in time-sensitive applications such as fetal fMRI. Here we propose a SVR method that overcomes the shortcomings of previous work and produces state-of-the-art reconstructions in the presence of extreme inter-slice motion. Inspired by the recent success of single-view depth estimation methods we formulate SVR as a single-stack motion estimation task and train a fully convolutional network to predict a motion stack for a given slice stack producing a 3D reconstruction as a byproduct of the predicted motion. Extensive experiments on the SVR of adult and fetal brains demonstrate that our fully convolutional method is twice as accurate as previous SVR methods. Our code is available at github.com/seannz/svr.

Training a linear classifier or lightweight model on top of pretrained vision model outputs so-called 'frozen features' leads to impressive performance on a number of downstream few-shot tasks. Currently frozen features are not modified during training. On the other hand when networks are trained directly on images data augmentation is a standard recipe that improves performance with no substantial overhead. In this paper we conduct an extensive pilot study on few-shot image classification that explores applying data augmentations in the frozen feature space dubbed 'frozen feature augmentation (FroFA)' covering twenty augmentations in total. Our study demonstrates that adopting a deceptively simple pointwise FroFA such as brightness can improve few-shot performance consistently across three network architectures three large pretraining datasets and eight transfer datasets.

Self-supervised monocular depth estimation (DE) is an approach to learning depth without costly depth ground truths. However it often struggles with moving objects that violate the static scene assumption during training. To address this issue we introduce a coarse-to-fine training strategy leveraging the ground contacting prior based on the observation that most moving objects in outdoor scenes contact the ground. In the coarse training stage we exclude the objects in dynamic classes from the reprojection loss calculation to avoid inaccurate depth learning. To provide precise supervision on the depth of the objects we present a novel Ground-contacting-prior Disparity Smoothness Loss (GDS-Loss) that encourages a DE network to align the depth of the objects with their ground-contacting points. Subsequently in the fine training stage we refine the DE network to learn the detailed depth of the objects from the reprojection loss while ensuring accurate DE on the moving object regions by employing our regularization loss with a cost-volume-based weighting factor. Our overall coarse-to-fine training strategy can easily be integrated with existing DE methods without any modifications significantly enhancing DE performance on challenging Cityscapes and KITTI datasets especially in the moving object regions.

Various pose estimation and tracking problems in robotics can be decomposed into a correspondence estimation problem (often computed using a deep network) followed by a weighted least squares optimization problem to solve for the poses. Recent work has shown that coupling the two problems by iteratively refining one conditioned on the other's output yields SOTA results across domains. However training these models has proved challenging requiring a litany of tricks to stabilize and speed up training. In this work we take the visual odometry problem as an example and identify three plausible causes: (1) flow loss interference (2) linearization errors in the bundle adjustment (BA) layer and (3) dependence of weight gradients on the BA residual. We show how these issues result in noisy and higher variance gradients potentially leading to a slow down in training and instabilities. We then propose a simple solution to reduce the gradient variance by using the weights predicted by the network in the inner optimization loop to also weight the correspondence objective in the training problem. This helps the training objective 'focus' on the more important points thereby reducing the variance and mitigating the influence of outliers. We show that the resulting method leads to faster training and can be more flexibly trained in varying training setups without sacrificing performance. In particular we show 2-2.5x training speedups over a baseline visual odometry model we modify.

Weakly Supervised Semantic Segmentation (WSSS) aims to learn the concept of segmentation using image-level class labels. Recent WSSS works have shown promising results by using the Segment Anything Model (SAM) a foundation model for segmentation during the inference phase. However we observe that these methods can still be vulnerable to the noise of class activation maps (CAMs) serving as initial seeds. As a remedy this paper introduces From-SAM-to-CAMs (S2C) a novel WSSS framework that directly transfers the knowledge of SAM to the classifier during the training process enhancing the quality of CAMs itself. S2C comprises SAM-segment Contrasting (SSC) and a CAM-based prompting module (CPM) which exploit SAM at the feature and logit levels respectively. SSC performs prototype-based contrasting using SAM's automatic segmentation results. It constrains each feature to be close to the prototype of its segment and distant from prototypes of the others. Meanwhile CPM extracts prompts from the CAM of each class and uses them to generate class-specific segmentation masks through SAM. The masks are aggregated into unified self-supervision based on the confidence score designed to consider the reliability of both SAM and CAMs. S2C achieves a new state-of-the-art performance across all benchmarks outperforming existing studies by significant margins. The code is available at https://github.com/sangrockEG/S2C.