CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Neural Radiance Fields (NeRFs) excel in photorealistically rendering static scenes. However rendering dynamic long-duration radiance fields on ubiquitous devices remains challenging due to data storage and computational constraints. In this paper we introduce VideoRF the first approach to enable real-time streaming and rendering of dynamic human-centric radiance fields on mobile platforms. At the core is a serialized 2D feature image stream representing the 4D radiance field all in one. We introduce a tailored training scheme directly applied to this 2D domain to impose the temporal and spatial redundancy of the feature image stream. By leveraging the redundancy we show that the feature image stream can be efficiently compressed by 2D video codecs which allows us to exploit video hardware accelerators to achieve real-time decoding. On the other hand based on the feature image stream we propose a novel rendering pipeline for VideoRF which has specialized space mappings to query radiance properties efficiently. Paired with a deferred shading model VideoRF has the capability of real-time rendering on mobile devices thanks to its efficiency.
On the other hand based on the feature image stream we propose a novel rendering pipeline for VideoRF which has specialized space mappings to query radiance properties efficiently. Paired with a deferred shading model VideoRF has the capability of real-time rendering on mobile devices thanks to its efficiency. We have developed a real-time interactive player that enables online streaming and rendering of dynamic scenes offering a seamless and immersive free-viewpoint experience across a range of devices from desktops to mobile phones. Our project page is available at https://aoliao12138.github.io/VideoRF/.
We introduce Diffusion Parametric Head Models (DPHMs) a generative model that enables robust volumetric head reconstruction and tracking from monocular depth sequences. While recent volumetric head models such as NPHMs can now excel in representing high-fidelity head geometries tracking and reconstructing heads from real-world single-view depth sequences remains very challenging as the fitting to partial and noisy observations is underconstrained. To tackle these challenges we propose a latent diffusion-based prior to regularize volumetric head reconstruction and tracking. This prior-based regularizer effectively constrains the identity and expression codes to lie on the underlying latent manifold which represents plausible head shapes. To evaluate the effectiveness of the diffusion-based prior we collect a dataset of monocular Kinect sequences consisting of various complex facial expression motions and rapid transitions. We compare our method to state-of-the-art tracking methods and demonstrate improved head identity reconstruction as well as robust expression tracking.
Current perceptive models heavily depend on resource-intensive datasets prompting the need for innovative solutions. Leveraging recent advances in diffusion models synthetic data by constructing image inputs from various annotations proves beneficial for downstream tasks. While prior methods have separately addressed generative and perceptive models DetDiffusion for the first time harmonizes both tackling the challenges in generating effective data for perceptive models. To enhance image generation with perceptive models we introduce perception-aware loss (P.A. loss) through segmentation improving both quality and controllability. To boost the performance of specific perceptive models our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation. Experimental results from the object detection task highlight DetDiffusion's superior performance establishing a new state-of-the-art in layout-guided generation. Furthermore image syntheses from DetDiffusion can effectively augment training data significantly enhancing downstream detection performance.
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.
Previous methods for Video Frame Interpolation (VFI) have encountered challenges notably the manifestation of blur and ghosting effects. These issues can be traced back to two pivotal factors: unavoidable motion errors and misalignment in supervision. In practice motion estimates often prove to be error-prone resulting in misaligned features. Furthermore the reconstruction loss tends to bring blurry results particularly in misaligned regions. To mitigate these challenges we propose a new paradigm called PerVFI (Perception-oriented Video Frame Interpolation). Our approach incorporates an Asymmetric Synergistic Blending module (ASB) that utilizes features from both sides to synergistically blend intermediate features. One reference frame emphasizes primary content while the other contributes complementary information. To impose a stringent constraint on the blending process we introduce a self-learned sparse quasi-binary mask which effectively mitigates ghosting and blur artifacts in the output. Additionally we employ a normalizing flow-based generator and utilize the negative log-likelihood loss to learn the conditional distribution of the output which further facilitates the generation of clear and fine details. Experimental results validate the superiority of PerVFI demonstrating significant improvements in perceptual quality compared to existing methods. Codes are available at https://github.com/mulns/PerVFI
State-of-the-art personalized text-to-image generation systems are usually trained on a few reference images to learn novel visual representations. However this is likely to incur infringement of copyright for the reference image owners when these images are personal and publicly available. Recent progress has been made in protecting these images from unauthorized use by adding protective noises. Yet current protection methods work under the assumption that these protected images are not changed which is in contradiction to the fact that most public platforms intend to modify user-uploaded content e.g. image compression. This paper introduces a robust watermarking method namely InMark to protect images from unauthorized learning. Inspired by influence functions the proposed method forges protective watermarks on more important pixels for these reference images from both heuristic and statistical perspectives. In this way the personal semantics of these images are under protection even if these images are modified to some extent. Extensive experiments demonstrate that the proposed InMark outperforms previous state-of-the-art methods in both protective performance and robustness.
In recent years there has been a growing interest in training Neural Networks to approximate Unsigned Distance Fields (UDFs) for representing open surfaces in the context of 3D reconstruction. However UDFs are non-differentiable at the zero level set which leads to significant errors in distances and gradients generally resulting in fragmented and discontinuous surfaces. In this paper we propose to learn a hyperbolic scaling of the unsigned distance field which defines a new Eikonal problem with distinct boundary conditions. This allows our formulation to integrate seamlessly with state-of-the-art continuously differentiable implicit neural representation networks largely applied in the literature to represent signed distance fields. Our approach not only addresses the challenge of open surface representation but also demonstrates significant improvement in reconstruction quality and training performance. Moreover the unlocked field's differentiability allows the accurate computation of essential topological properties such as normal directions and curvatures pervasive in downstream tasks such as rendering. Through extensive experiments we validate our approach across various data sets and against competitive baselines. The results demonstrate enhanced accuracy and up to an order of magnitude increase in speed compared to previous methods.
The vision-language model has brought great improvement to few-shot industrial anomaly detection which usually needs to design of hundreds of prompts through prompt engineering. For automated scenarios we first use conventional prompt learning with many-class paradigm as the baseline to automatically learn prompts but found that it can not work well in one-class anomaly detection. To address the above problem this paper proposes a one-class prompt learning method for few-shot anomaly detection termed PromptAD. First we propose semantic concatenation which can transpose normal prompts into anomaly prompts by concatenating normal prompts with anomaly suffixes thus constructing a large number of negative samples used to guide prompt learning in one-class setting. Furthermore to mitigate the training challenge caused by the absence of anomaly images we introduce the concept of explicit anomaly margin which is used to explicitly control the margin between normal prompt features and anomaly prompt features through a hyper-parameter. For image-level/pixel-level anomaly detection PromptAD achieves first place in 11/12 few-shot settings on MVTec and VisA.
Graph Contrastive Learning (GCL) a Self-Supervised Learning (SSL) architecture tailored for graphs has shown notable potential for mitigating label scarcity. Its core idea is to amplify feature similarities between the positive sample pairs and reduce them between the negative sample pairs. Unfortunately most existing GCLs consistently present suboptimal performances on both homophilic and heterophilic graphs. This is primarily attributed to two limitations of positive sampling that is incomplete local sampling and blind sampling. To address these limitations this paper introduces a novel GCL framework with an adaptive positive sampling module named grapH contrastivE Adaptive posiTive Samples (HEATS). Motivated by the observation that the affinity matrix corresponding to optimal positive sample sets has a block-diagonal structure with equal weights within each block a self-expressive learning objective incorporating the block and idempotent constraint is presented. This learning objective and the contrastive learning objective are iteratively optimized to improve the adaptability and robustness of HEATS. Extensive experiments on graphs and images validate the effectiveness and generality of HEATS.
Deep unfolding networks (DUNs) renowned for their interpretability and superior performance have invigorated the realm of compressive sensing (CS). Nonetheless existing DUNs frequently suffer from issues related to insufficient feature extraction and feature attrition during the iterative steps. In this paper we propose Unrolling Fixed-point Continuous Network (UFC-Net) a novel deep CS framework motivated by the traditional fixed-point continuous optimization algorithm. Specifically we introduce Convolution-guided Attention Module (CAM) to serve as a critical constituent within the reconstruction phase encompassing tailored components such as Multi-head Attention Residual Block (MARB) Auxiliary Iterative Reconstruction Block (AIRB) etc. MARB effectively integrates multi-head attention mechanisms with convolution to reinforce feature extraction transcending the confinement of localized attributes and facilitating the apprehension of long-range correlations. Meanwhile AIRB introduces auxiliary variables significantly bolstering the preservation of features within each iterative stage. Extensive experiments demonstrate that our proposed UFC-Net achieves remarkable performance both on image CS and CS-magnetic resonance imaging (CS-MRI) in contrast to state-of-the-art methods.
In the absence of parallax cues a learning-based single image depth estimation (SIDE) model relies heavily on shading and contextual cues in the image. While this simplicity is attractive it is necessary to train such models on large and varied datasets which are difficult to capture. It has been shown that using embeddings from pre-trained foundational models such as CLIP improves zero shot transfer in several applications. Taking inspiration from this in our paper we explore the use of global image priors generated from a pre-trained ViT model to provide more detailed contextual information. We argue that the embedding vector from a ViT model pre-trained on a large dataset captures greater relevant information for SIDE than the usual route of generating pseudo image captions followed by CLIP based text embeddings. Based on this idea we propose a new SIDE model using a diffusion backbone which is conditioned on ViT embeddings. Our proposed design establishes a new state-of-the-art (SOTA) for SIDE on NYUv2 dataset achieving Abs Rel error of 0.059(14% improvement) compared to 0.069 by the current SOTA (VPD).
Our proposed design establishes a new state-of-the-art (SOTA) for SIDE on NYUv2 dataset achieving Abs Rel error of 0.059(14% improvement) compared to 0.069 by the current SOTA (VPD). And on KITTI dataset achieving Sq Rel error of 0.139 (2% improvement) compared to 0.142 by the current SOTA (GEDepth). For zero-shot transfer with a model trained on NYUv2 we report mean relative improvement of (20% 23% 81% 25%) over NeWCRFs on (Sun-RGBD iBims1 DIODE HyperSim) datasets compared to (16% 18% 45% 9%) by ZoeDepth. The project page is available at https://ecodepth-iitd.github.io
We have witnessed significant progress in deep learning-based 3D vision ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However existing scene-level datasets for deep learning-based 3D vision limited to either synthetic environments or a narrow selection of real-world scenes are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap we present DL3DV-10K a large-scale scene dataset featuring 51.2 million frames from 10510 videos captured from 65 types of point-of-interest (POI) locations covering both bounded and unbounded scenes with different levels of reflection transparency and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K which revealed valuable insights for future research in NVS. In addition we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset benchmark results and models will be publicly accessible.
Recently building on the foundation of neural radiance field various techniques have emerged to learn unsigned distance fields (UDF) to reconstruct 3D non-watertight models from multi-view images. Yet a central challenge in UDF-based volume rendering is formulating a proper way to convert unsigned distance values into volume density ensuring that the resulting weight function remains unbiased and sensitive to occlusions. Falling short on these requirements often results in incorrect topology or large reconstruction errors in resulting models. This paper addresses this challenge by presenting a novel two-stage algorithm 2S-UDF for learning a high-quality UDF from multi-view images. Initially the method applies an easily trainable density function that while slightly biased and transparent aids in coarse reconstruction. The subsequent stage then refines the geometry and appearance of the object to achieve a high-quality reconstruction by directly adjusting the weight function used in volume rendering to ensure that it is unbiased and occlusion-aware. Decoupling density and weight in two stages makes our training stable and robust distinguishing our technique from existing UDF learning approaches. Evaluations on the DeepFashion3D DTU and BlendedMVS datasets validate the robustness and effectiveness of our proposed approach.
Decoupling density and weight in two stages makes our training stable and robust distinguishing our technique from existing UDF learning approaches. Evaluations on the DeepFashion3D DTU and BlendedMVS datasets validate the robustness and effectiveness of our proposed approach. In both quantitative metrics and visual quality the results indicate our superior performance over other UDF learning techniques in reconstructing 3D non-watertight models from multi-view images. Our code is available at https://bitbucket.org/jkdeng/2sudf/.
The YOLO series has become the most popular framework for real-time object detection due to its reasonable trade-off between speed and accuracy. However we observe that the speed and accuracy of YOLOs are negatively affected by the NMS. Recently end-to-end Transformer-based detectors (DETRs) have provided an alternative to eliminating NMS. Nevertheless the high computational cost limits their practicality and hinders them from fully exploiting the advantage of excluding NMS. In this paper we propose the Real-Time DEtection TRansformer (RT-DETR) the first real-time end-to-end object detector to our best knowledge that addresses the above dilemma. We build RT-DETR in two steps drawing on the advanced DETR: first we focus on maintaining accuracy while improving speed followed by maintaining speed while improving accuracy. Specifically we design an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed. Then we propose the uncertainty-minimal query selection to provide high-quality initial queries to the decoder thereby improving accuracy. In addition RT-DETR supports flexible speed tuning by adjusting the number of decoder layers to adapt to various scenarios without retraining.
Then we propose the uncertainty-minimal query selection to provide high-quality initial queries to the decoder thereby improving accuracy. In addition RT-DETR supports flexible speed tuning by adjusting the number of decoder layers to adapt to various scenarios without retraining. Our RT-DETR-R50 / R101 achieves 53.1% / 54.3% AP on COCO and 108 / 74 FPS on T4 GPU outperforming previously advanced YOLOs in both speed and accuracy. We also develop scaled RT-DETRs that outperform the lighter YOLO detectors (S and M models). Furthermore RT-DETR-R50 outperforms DINO-R50 by 2.2% AP in accuracy and about 21 times in FPS. After pre-training with Objects365 RT-DETR-R50 / R101 achieves 55.3% / 56.2% AP. The project page: https://zhao-yian.github.io/RTDETR.
Despite the recent advances in unified image segmentation (IS) developing a unified video segmentation (VS) model remains a challenge. This is mainly because generic category-specified VS tasks need to detect all objects and track them across consecutive frames while prompt-guided VS tasks require re-identifying the target with visual/text prompts throughout the entire video making it hard to handle the different tasks with the same architecture. We make an attempt to address these issues and present a novel unified VS architecture namely UniVS by using prompts as queries. UniVS averages the prompt features of the target from previous frames as its initial query to explicitly decode masks and introduces a target-wise prompt cross-attention layer in the mask decoder to integrate prompt features in the memory pool. By taking the predicted masks of entities from previous frames as their visual prompts UniVS converts different VS tasks into prompt-guided target segmentation eliminating the heuristic inter-frame matching process. Our framework not only unifies the different VS tasks but also naturally achieves universal training and testing ensuring robust performance across different scenarios. UniVS shows a commendable balance between performance and universality on 10 challenging VS benchmarks covering video instance semantic panoptic object and referring segmentation tasks. Code can be found at https://github.com/MinghanLi/UniVS.
Human-Object Interaction (HOI) Detection constitutes an important aspect of human-centric scene understanding which requires precise object detection and interaction recognition. Despite increasing advancement in detection recognizing subtle and intricate interactions remains challenging. Recent methods have endeavored to leverage the rich semantic representation from pre-trained CLIP yet fail to efficiently capture finer-grained spatial features that are highly informative for interaction discrimination. In this work instead of solely using representations from CLIP we fill the gap by proposing a spatial adapter that efficiently utilizes the multi-scale spatial information in the pre-trained detector. This leads to a bilateral adaptation that mutually produces complementary features. To further improve interaction recognition under occlusion which is common in crowded scenarios we propose an Occluded Part Extrapolation module that guides the model to recover the spatial details from manually occluded feature maps. Moreover we design a Conditional Contextual Mining module that further mines informative contextual clues from the spatial features via a tailored cross-attention mechanism. Extensive experiments on V-COCO and HICO-DET benchmarks demonstrate that our method significantly outperforms prior art on both standard and zero-shot settings resulting in new state-of-the-art performance. Additional ablation studies further validate the effectiveness of each component in our method.
Unsupervised fine-grained image hashing aims to learn compact binary hash codes in unsupervised settings addressing challenges posed by large-scale datasets and dependence on supervision. In this paper we first identify a granularity gap between generic and fine-grained datasets for unsupervised hashing methods highlighting the inadequacy of conventional self-supervised learning for fine-grained visual objects. To bridge this gap we propose the Asymmetric Augmented Self-Supervised Learning (A^2-SSL) method comprising three modules. The asymmetric augmented SSL module employs suitable augmentation strategies for positive/negative views preventing fine-grained category confusion inherent in conventional SSL. Part-oriented dense contrastive learning utilizes the Fisher Vector framework to capture and model fine-grained object parts enhancing unsupervised representations through part-level dense contrastive learning. Self-consistent hash code learning introduces a reconstruction task aligned with the self-consistency principle guiding the model to emphasize comprehensive features particularly fine-grained patterns. Experimental results on five benchmark datasets demonstrate the superiority of A^2-SSL over existing methods affirming its efficacy in unsupervised fine-grained image hashing.