CVPR 2024 论文列表

High-quality human reconstruction and photo-realistic rendering of a dynamic scene is a long-standing problem in computer vision and graphics. Despite considerable efforts invested in developing various capture systems and reconstruction algorithms recent advancements still struggle with loose or oversized clothing and overly complex poses. In part this is due to the challenges of acquiring high-quality human datasets. To facilitate the development of these fields in this paper we present PKU-DyMVHumans a versatile human-centric dataset for high-fidelity reconstruction and rendering of dynamic human scenarios from dense multi-view videos. It comprises 8.2 million frames captured by more than 56 synchronized cameras across diverse scenarios. These sequences comprise 32 human subjects across 45 different scenarios each with a high-detailed appearance and realistic human motion. Inspired by recent advancements in neural radiance field (NeRF)-based scene representations we carefully set up an off-the-shelf framework that is easy to provide those state-of-the-art NeRF-based implementations and benchmark on PKU-DyMVHumans dataset. It is paving the way for various applications like fine-grained foreground/background decomposition high-quality human reconstruction and photo-realistic novel view synthesis of a dynamic scene. Extensive studies are performed on the benchmark demonstrating new observations and challenges that emerge from using such high-fidelity dynamic data. The project page and data is available at: https://pku-dymvhumans.github.io.

Vision-Language Models (VLMs) such as Flamingo and GPT-4V have shown immense potential by integrating large language models with vision systems. Nevertheless these models face challenges in the fundamental computer vision task of object localisation due to their training on multimodal data containing mostly captions without explicit spatial grounding. While it is possible to construct custom supervised training pipelines with bounding box annotations that integrate with VLMs these result in specialized and hard-to-scale models. In this paper we aim to explore the limits of caption-based VLMs and instead propose to tackle the challenge in a simpler manner by i) keeping the weights of a caption-based VLM frozen and ii) not using any supervised detection data. To this end we introduce an input-agnostic Positional Insert (PIN) a learnable spatial prompt containing a minimal set of parameters that are slid inside the frozen VLM unlocking object localisation capabilities. Our PIN module is trained with a simple next-token prediction task on synthetic data without requiring the introduction of new output heads. Our experiments demonstrate strong zero-shot localisation performances on a variety of images including Pascal VOC COCO LVIS and diverse images like paintings or cartoons.

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

In this paper we propose a novel virtual try-on from unconstrained designs (ucVTON) task to enable photorealistic synthesis of personalized composite clothing on input human images. Unlike prior arts constrained by specific input types our method allows flexible specification of style (text or image) and texture (full garment cropped sections or texture patches) conditions. To address the entanglement challenge when using full garment images as conditions we develop a two-stage pipeline with explicit disentanglement of style and texture. In the first stage we generate a human parsing map reflecting the desired style conditioned on the input. In the second stage we composite textures onto the parsing map areas based on the texture input. To represent complex and non-stationary textures that have never been achieved in previous fashion editing works we first propose extracting hierarchical and balanced CLIP features and applying position encoding in VTON. Experiments demonstrate superior synthesis quality and personalization enabled by our method. The flexible control over style and texture mixing brings virtual try-on to a new level of user experience for online shopping and fashion design.

Recent advancements in personalized text-to-image (T2I) models have revolutionized content creation empowering non-experts to generate stunning images with unique styles. While promising animating these personalized images with realistic motions poses significant challenges in preserving distinct styles high-fidelity details and achieving motion controllability by text. In this paper we present PIA a Personalized Image Animator that excels in aligning with condition images achieving motion controllability by text and the compatibility with various personalized T2I models without specific tuning. To achieve these goals PIA builds upon a base T2I model with well-trained temporal alignment layers allowing for the seamless transformation of any personalized T2I model into an image animation model. A key component of PIA is the introduction of the condition module which takes as inputs the condition frame and inter-frame affinity. This module leverages the affinity hint to transfer appearance information from the condition frame to individual frames in the latent space. This design mitigates the challenges of appearance-related frame alignment within PIA and allows for a stronger focus on aligning with motion-related guidance. To address the lack of a benchmark for this field we introduce AnimateBench a comprehensive benchmark comprising diverse personalized T2I models curated images and motion-related prompts. We show extensive evaluations and applications on AnimateBench to verify the superiority of PIA.

Diffusion models trained on large-scale text-image datasets have demonstrated a strong capability of controllable high-quality image generation from arbitrary text prompts. However the generation quality and generalization ability of 3D diffusion models is hindered by the scarcity of high-quality and large-scale 3D datasets. In this paper we present PI3D a framework that fully leverages the pre-trained text-to-image diffusion models' ability to generate high-quality 3D shapes from text prompts in minutes. The core idea is to connect the 2D and 3D domains by representing a 3D shape as a set of Pseudo RGB Images. We fine-tune an existing text-to-image diffusion model to produce such pseudo-images using a small number of text-3D pairs. Surprisingly we find that it can already generate meaningful and consistent 3D shapes given complex text descriptions. We further take the generated shapes as the starting point for a lightweight iterative refinement using score distillation sampling to achieve high-quality generation under a low budget. PI3D generates a single 3D shape from text in only 3 minutes and the quality is validated to outperform existing 3D generative models by a large margin.

We present a novel semi-supervised framework for breast ultrasound (BUS) image segmentation which is a very challenging task owing to (1) large scale and shape variations of breast lesions and (2) extremely ambiguous boundaries caused by massive speckle noise and artifacts in BUS images. While existing models achieved certain progress in this task we believe the main bottleneck nowadays for further improvement is that we still cannot deal with hard cases well. Our framework aims to break through this bottleneck which includes two innovative components: an adaptive patch augmentation scheme and a hard-patch contrastive learning module. We first identify hard patches by computing the average entropy of each patch and then shield hard patches to prevent them from being cropped out while performing random patch cutmix. Such a scheme is able to prevent hard regions from being inadequately trained under strong augmentation. We further develop a new hard-patch contrastive learning algorithm to direct model attention to hard regions by applying extra contrast to pixels in hard patches further improving segmentation performance on hard cases. We demonstrate the superiority of our framework to state-of-the-art approaches on two famous BUS datasets achieving better performance under different labeling conditions. The code is available at https://github.com/jjjsyyy/PH-Net.

Recent developments in face restoration have achieved remarkable results in producing high-quality and lifelike outputs. The stunning results however often fail to be faithful with respect to the identity of the person as the models lack necessary context. In this paper we explore the potential of personalized face restoration with diffusion models. In our approach a restoration model is personalized using a few images of the identity leading to tailored restoration with respect to the identity while retaining fine-grained details. By using independent trainable blocks for personalization the rich prior of a base restoration model can be exploited to its fullest. To avoid the model relying on parts of identity left in the conditioning low-quality images a generative regularizer is employed. With a learnable parameter the model learns to balance between the details generated based on the input image and the degree of personalization. Moreover we improve the training pipeline of face restoration models to enable an alignment-free approach. We showcase the robust capabilities of our approach in several real-world scenarios with multiple identities demonstrating our method's ability to generate fine-grained details with faithful restoration. In the user study we evaluate the perceptual quality and faithfulness of the generated details with our method being voted best 61% of the time compared to the second best with 25% of the votes.

Recent transformer-based architectures have shown impressive results in the field of image segmentation. Thanks to their flexibility they obtain outstanding performance in multiple segmentation tasks such as semantic and panoptic under a single unified framework. To achieve such impressive performance these architectures employ intensive operations and require substantial computational resources which are often not available especially on edge devices. To fill this gap we propose Prototype-based Efficient MaskFormer (PEM) an efficient transformer-based architecture that can operate in multiple segmentation tasks. PEM proposes a novel prototype-based cross-attention which leverages the redundancy of visual features to restrict the computation and improve the efficiency without harming the performance. In addition PEM introduces an efficient multi-scale feature pyramid network capable of extracting features that have high semantic content in an efficient way thanks to the combination of deformable convolutions and context-based self-modulation. We benchmark the proposed PEM architecture on two tasks semantic and panoptic segmentation evaluated on two different datasets Cityscapes and ADE20K. PEM demonstrates outstanding performance on every task and dataset outperforming task-specific architectures while being comparable and even better than computationally expensive baselines. Code is available at https://github.com/NiccoloCavagnero/PEM.

Applying a pre-trained large model to downstream tasks is prohibitive under resource-constrained conditions. Recent dominant approaches for addressing efficiency issues involve adding a few learnable parameters to the fixed backbone model. This strategy however leads to more challenges in loading large models for downstream fine-tuning with limited resources. In this paper we propose a novel method for increasing the parameter efficiency of pre-trained models by introducing an intermediate pre-training stage. To this end we first employ low-rank approximation to compress the original large model and then devise a feature distillation module and a weight perturbation regularization module. These modules are specifically designed to enhance the low-rank model. In particular we update only the low-rank model while freezing the backbone parameters during pre-training. This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks. The proposed method achieves both efficiencies in terms of required parameters and computation time while maintaining comparable results with minimal modifications to the backbone architecture. Specifically when applied to three vision-only and one vision-language Transformer models our approach often demonstrates a merely 0.6-point decrease in performance while reducing the original parameter size by 1/3 to 2/3.

We present PEGASUS a method for constructing a personalized generative 3D face avatar from monocular video sources. Our generative 3D avatar enables disentangled controls to selectively alter the facial attributes (e.g. hair or nose) while preserving the identity. Our approach consists of two stages: synthetic database generation and constructing a personalized generative avatar. We generate a synthetic video collection of the target identity with varying facial attributes where the videos are synthesized by borrowing the attributes from monocular videos of diverse identities. Then we build a person-specific generative 3D avatar that can modify its attributes continuously while preserving its identity. Through extensive experiments we demonstrate that our method of generating a synthetic database and creating a 3D generative avatar is the most effective in preserving identity while achieving high realism. Subsequently we introduce a zero-shot approach to achieve the same goal of generative modeling more efficiently by leveraging a previously constructed personalized generative model.

Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However a significant limitation is their inability to offer interactive control to users a feature that promises to open up unprecedented applications and creativity. In this work we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present Peekaboo a novel masked attention module which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that Peekaboo achieves up to a 3.8x improvement in mIoU over baseline models all while maintaining the same latency. Code and benchmark are available on the webpage.

Existing point cloud semantic segmentation networks cannot identify unknown classes and update their knowledge due to a closed-set and static perspective of the real world which would induce the intelligent agent to make bad decisions. To address this problem we propose a Probability-Driven Framework (PDF) for open world semantic segmentation that includes (i) a lightweight U-decoder branch to identify unknown classes by estimating the uncertainties (ii) a flexible pseudo-labeling scheme to supply geometry features along with probability distribution features of unknown classes by generating pseudo labels and (iii) an incremental knowledge distillation strategy to incorporate novel classes into the existing knowledge base gradually. Our framework enables the model to behave like human beings which could recognize unknown objects and incrementally learn them with the corresponding knowledge. Experimental results on the S3DIS and ScanNetv2 datasets demonstrate that the proposed PDF outperforms other methods by a large margin in both important tasks of open world semantic segmentation.

In this paper we present an end-to-end 3D-building-wireframe reconstruction method to regress edges directly from aerial light-detection-and-ranging (LiDAR) point clouds. Our method named parametric-building-wireframe reconstruction (PBWR) takes aerial LiDAR point clouds and initial edge entities as input and fully uses the self-attention mechanism of transformers to regress edge parameters without any intermediate steps such as corner prediction. We propose an edge non-maximum suppression (E-NMS) module based on edge similarity to remove redundant edges. Additionally a dedicated edge loss function is utilized to guide the PBWR in regressing edges parameters when the simple use of the edge distance loss is not suitable. In our experiments our proposed method demonstrated state-of-the-art results on the Building3D dataset achieving an improvement of approximately 36% in Entry-level dataset edge accuracy and around a 42% improvement in the Tallinn dataset.

Recent works have proposed end-to-end autonomous vehicle (AV) architectures comprised of differentiable modules achieving state-of-the-art driving performance. While they provide advantages over the traditional perception-prediction-planning pipeline (e.g. removing information bottlenecks between components and alleviating integration challenges) they do so using a diverse combination of tasks modules and their interconnectivity. As of yet however there has been no systematic analysis of the necessity of these modules or the impact of their connectivity placement and internal representations on overall driving performance. Addressing this gap our work conducts a comprehensive exploration of the design space of end-to-end modular AV stacks. Our findings culminate in the development of PARA-Drive: a fully parallel end-to-end AV architecture. PARA-Drive not only achieves state-of-the-art performance in perception prediction and planning but also significantly enhances runtime speed by nearly 3x without compromising on interpretability or safety.

Generative image editing has recently witnessed extremely fast-paced growth. Some works use high-level conditioning such as text while others use low-level conditioning. Nevertheless most of them lack fine-grained control over the properties of the different objects present in the image i.e. object-level image editing. In this work we tackle the task by perceiving the images as an amalgamation of various objects and aim to control the properties of each object in a fine-grained manner. Out of these properties we identify structure and appearance as the most intuitive to understand and useful for editing purposes. We propose PAIR Diffusion a generic framework that enables a diffusion model to control the structure and appearance properties of each object in the image. We show that having control over the properties of each object in an image leads to comprehensive editing capabilities. Our framework allows for various object-level editing operations on real images such as reference image-based appearance editing free-form shape editing adding objects and variations. Thanks to our design we do not require any inversion step. Additionally we propose multimodal classifier-free guidance which enables editing images using both reference images and text when using our approach with foundational diffusion models. We validate the above claims by extensively evaluating our framework on both unconditional and foundational diffusion models.

Adversarial patch attacks present a significant threat to real-world object detectors due to their practical feasibility. Existing defense methods which rely on attack data or prior knowledge struggle to effectively address a wide range of adversarial patches. In this paper we show two inherent characteristics of adversarial patches semantic independence and spatial heterogeneity independent of their appearance shape size quantity and location. Semantic independence indicates that adversarial patches operate autonomously within their semantic context while spatial heterogeneity manifests as distinct image quality of the patch area that differs from original clean image due to the independent generation process. Based on these observations we propose PAD a novel adversarial patch localization and removal method that does not require prior knowledge or additional training. PAD offers patch-agnostic defense against various adversarial patches compatible with any pre-trained object detectors. Our comprehensive digital and physical experiments involving diverse patch types such as localized noise printable and naturalistic patches exhibit notable improvements over state-of-the-art works. Our code is available at https://github.com/Lihua-Jing/PAD.

We address the challenge of content diversity and controllability in pedestrian simulation for driving scenarios. Recent pedestrian animation frameworks have a significant limitation wherein they primarily focus on either following trajectory or the content of the reference video consequently overlooking the potential diversity of human motion within such scenarios. This limitation restricts the ability to generate pedestrian behaviors that exhibit a wider range of variations and realistic motions and therefore restricts its usage to provide rich motion content for other components in the driving simulation system e.g. suddenly changed motion to which the autonomous vehicle should respond. In our approach we strive to surpass the limitation by showcasing diverse human motions obtained from various sources such as generated human motions in addition to following the given trajectory. The fundamental contribution of our framework lies in combining the motion tracking task with trajectory following which enables the tracking of specific motion parts (e.g. upper body) while simultaneously following the given trajectory by a single policy. This way we significantly enhance both the diversity of simulated human motion within the given scenario and the controllability of the content including language-based control. Our framework facilitates the generation of a wide range of human motions contributing to greater realism and adaptability in pedestrian simulations for driving scenarios.

Nowadays the deployment of deep learning-based applications is an essential task owing to the increasing demands on intelligent services. In this paper we investigate latency attacks on deep learning applications. Unlike common adversarial attacks for misclassification the goal of latency attacks is to increase the inference time which may stop applications from responding to the requests within a reasonable time. This kind of attack is ubiquitous for various applications and we use object detection to demonstrate how such kind of attacks work. We also design a framework named Overload to generate latency attacks at scale. Our method is based on a newly formulated optimization problem and a novel technique called spatial attention. This attack serves to escalate the required computing costs during the inference time consequently leading to an extended inference time for object detection. It presents a significant threat especially to systems with limited computing resources. We conducted experiments using YOLOv5 models on Nvidia NX. Compared to existing methods our method is simpler and more effective. The experimental results show that with latency attacks the inference time of a single image can be increased ten times longer in reference to the normal setting. Moreover our findings pose a potential new threat to all object detection tasks requiring non-maximum suppression (NMS) as our attack is NMS-agnostic.