GenAI4ELab/papercli-papers huggingface dataset:
Generative Proxemics: A Prior for 3D Social Interaction from Images
Authors: Lea Müller; Vickie Ye; Georgios Pavlakos; Michael Black; Angjoo Kanazawa
Venue: CVPR 2024 (main)

Abstract: Social interaction is a fundamental aspect of human behavior and communication. The way individuals position themselves in relation to others also known as proxemics conveys social cues and affects the dynamics of social interaction. Reconstructing such interaction from images presents challenges because of mutual occlusion and the limited availability of large training datasets. To address this we present a novel approach that learns a prior over the 3D proxemics two people in close social interaction and demonstrate its use for single-view 3D reconstruction. We start by creating 3D training data of interacting people using image datasets with contact annotations. We then model the proxemics using a novel denoising diffusion model called BUDDI that learns the joint distribution over the poses of two people in close social interaction. Sampling from our generative proxemics model produces realistic 3D human interactions which we validate through a perceptual study. We use BUDDI in reconstructing two people in close proximity from an image without any contact annotation via an optimization approach that uses the diffusion model as a prior. Our approach recovers accurate 3D social interactions from noisy initial estimates outperforming state-of-the-art methods. Our code data and model are available at: muelea.github.io/buddi.
A Simple and Effective Point-based Network for Event Camera 6-DOFs Pose Relocalization
Authors: Hongwei Ren; Jiadong Zhu; Yue Zhou; Haotian Fu; Yulong Huang; Bojun Cheng
Venue: CVPR 2024 (main)

Abstract: Event cameras exhibit remarkable attributes such as high dynamic range asynchronicity and low latency making them highly suitable for vision tasks that involve high-speed motion in challenging lighting conditions. These cameras implicitly capture movement and depth information in events making them appealing sensors for Camera Pose Relocalization (CPR) tasks. Nevertheless existing CPR networks based on events neglect the pivotal fine-grained temporal information in events resulting in unsatisfactory performance. Moreover the energy-efficient features are further compromised by the use of excessively complex models hindering efficient deployment on edge devices. In this paper we introduce PEPNet a simple and effective point-based network designed to regress six degrees of freedom (6-DOFs) event camera poses. We rethink the relationship between the event camera and CPR tasks leveraging the raw Point Cloud directly as network input to harness the high-temporal resolution and inherent sparsity of events. PEPNet is adept at abstracting the spatial and implicit temporal features through hierarchical structure and explicit temporal features by Attentive Bi-directional Long Short-Term Memory (A-Bi-LSTM). By employing a carefully crafted lightweight design PEPNet delivers state-of-the-art (SOTA) performance on both indoor and outdoor datasets with meager computational resources. Specifically PEPNet attains a significant 38% and 33% performance improvement on the random split IJRR and M3ED datasets respectively. Moreover the lightweight design version PEPNet_ tiny  accomplishes results comparable to the SOTA while employing a mere 0.5% of the parameters.
Semantic-Aware Multi-Label Adversarial Attacks
Authors: Hassan Mahmood; Ehsan Elhamifar
Venue: CVPR 2024 (main)

Abstract: Despite its importance generating attacks for multi label learning (MLL) models has received much less attention compared to multi-class recognition. Attacking an MLL model by optimizing a loss on the target set of labels has often the undesired consequence of changing the predictions for other labels. On the other hand adding a loss on the remaining labels to keep them fixed leads to highly negatively correlated gradient directions reducing the attack effectiveness. In this paper we develop a framework for crafting effective and semantic aware adversarial attacks for MLL. First to obtain an attack that leads to semantically consistent predictions across all labels we find a minimal superset of the target labels referred to as consistent target set. To do so we develop an efficient search algorithm over a knowledge graph which encodes label dependencies. Next we propose an optimization that searches for an attack that modifies the predictions of labels in the consistent target set while ensuring other labels will not get affected. This leads to an efficient algorithm that projects the gradient of the consistent target set loss onto the orthogonal direction of the gradient of the loss on other labels. Our framework can generate attacks on different target set sizes and for MLL with thousands of labels (as in OpenImages). Finally by extensive experiments on three datasets and several MLL models we show that our method generates both successful and semantically consistent attacks.
EasyDrag: Efficient Point-based Manipulation on Diffusion Models
Authors: Xingzhong Hou; Boxiao Liu; Yi Zhang; Jihao Liu; Yu Liu; Haihang You
Venue: CVPR 2024 (main)

Abstract: Generative models are gaining increasing popularity and the demand for precisely generating images is on the rise. However generating an image that perfectly aligns with users' expectations is extremely challenging. The shapes of objects the poses of animals the structures of landscapes and more may not match the user's desires and this applies to real images as well. This is where point-based image editing becomes essential. An excellent image editing method needs to meet the following criteria: user-friendly interaction high performance and good generalization capability. Due to the limitations of StyleGAN DragGAN exhibits limited robustness across diverse scenarios while DragDiffusion lacks user-friendliness due to the necessity of LoRA fine-tuning and masks. In this paper we introduce a novel interactive point-based image editing framework called EasyDrag that leverages pretrained diffusion models to achieve high-quality editing outcomes and user-friendship. Extensive experimentation demonstrates that our approach surpasses DragDiffusion in terms of both image quality and editing precision for point-based image manipulation tasks.
Region-Based Representations Revisited
Authors: Michal Shlapentokh-Rothman; Ansel Blume; Yao Xiao; Yuqun Wu; Sethuraman TV; Heyi Tao; Jae Yong Lee; Wilfredo Torres; Yu-Xiong Wang; Derek Hoiem
Venue: CVPR 2024 (main)

Abstract: We investigate whether region-based representations are effective for recognition. Regions were once a mainstay in recognition approaches but pixel and patch-based features are now used almost exclusively. We show that recent class-agnostic segmenters like SAM can be effectively combined with strong unsupervised representations like DINOv2 and used for a wide variety of tasks including semantic segmentation object-based image retrieval and multi-image analysis. Once the masks and features are extracted these representations even with linear decoders enable competitive performance making them well suited to applications that require custom queries. The compactness of the representation also makes it well-suited to video analysis and other problems requiring inference across many images.
GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation Demonstration and Imitation
Authors: Zifan Wang; Junyu Chen; Ziqing Chen; Pengwei Xie; Rui Chen; Li Yi
Venue: CVPR 2024 (main)

Abstract: This paper presents GenH2R a framework for learning generalizable vision-based human-to-robot (H2R) handover skills. The goal is to equip robots with the ability to reliably receive objects with unseen geometry handed over by humans in various complex trajectories. We acquire such generalizability by learning H2R handover at scale with a comprehensive solution including procedural simulation assets creation automated demonstration generation and effective imitation learning. We leverage large-scale 3D model repositories dexterous grasp generation methods and curve-based 3D animation to create an H2R handover simulation environment named GenH2R-Sim surpassing the number of scenes in existing simulators by three orders of magnitude. We further introduce a distillation-friendly demonstration generation method that automatically generates a million high-quality demonstrations suitable for learning. Finally we present a 4D imitation learning method augmented by a future forecasting objective to distill demonstrations into a visuo-motor handover policy. Experimental evaluations in both simulators and the real world demonstrate significant improvements (at least +10% success rate) over baselines in all cases.
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration
Authors: Tony C. W. Mok; Zi Li; Yunhao Bai; Jianpeng Zhang; Wei Liu; Yan-Jie Zhou; Ke Yan; Dakai Jin; Yu Shi; Xiaoli Yin; Le Lu; Ling Zhang
Venue: CVPR 2024 (main)

Abstract: Establishing dense anatomical correspondence across distinct imaging modalities is a foundational yet challenging procedure for numerous medical image analysis studies and image-guided radiotherapy. Existing multi-modality image registration algorithms rely on statistical-based similarity measures or local structural image representations. However the former is sensitive to locally varying noise while the latter is not discriminative enough to cope with complex anatomical structures in multimodal scans causing ambiguity in determining the anatomical correspondence across scans with different modalities. In this paper we propose a modality-agnostic structural representation learning method which leverages Deep Neighbourhood Self-similarity (DNS) and anatomy-aware contrastive learning to learn discriminative and contrast-invariance deep structural image representations (DSIR) without the need for anatomical delineations or pre-aligned training images. We evaluate our method on multiphase CT abdomen MR-CT and brain MR T1w-T2w registration. Comprehensive results demonstrate that our method is superior to the conventional local structural representation and statistical-based similarity measures in terms of discriminability and accuracy.
Any-Shift Prompting for Generalization over Distributions
Authors: Zehao Xiao; Jiayi Shen; Mohammad Mahdi Derakhshani; Shengcai Liao; Cees G. M. Snoek
Venue: CVPR 2024 (main)

Abstract: Image-language models with prompt learning have shown remarkable advances in numerous downstream vision tasks. Nevertheless conventional prompt learning methods overfit the training distribution and lose the generalization ability on the test distributions. To improve the generalization across various distribution shifts we propose any-shift prompting: a general probabilistic inference framework that considers the relationship between training and test distributions during prompt learning. We explicitly connect training and test distributions in the latent space by constructing training and test prompts in a hierarchical architecture. Within this framework the test prompt exploits the distribution relationships to guide the generalization of the CLIP image-language model from training to any test distribution. To effectively encode the distribution information and their relationships we further introduce a transformer inference network with a pseudo-shift training mechanism. The network generates the tailored test prompt with both training and test information in a feedforward pass avoiding extra training costs at test time. Extensive experiments on twenty-three datasets demonstrate the effectiveness of any-shift prompting on the generalization over various distribution shifts.
InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusion
Authors: Jihyun Lee; Shunsuke Saito; Giljoo Nam; Minhyuk Sung; Tae-Kyun Kim
Venue: CVPR 2024 (main)

Abstract: We present InterHandGen a novel framework that learns the generative prior of two-hand interaction. Sampling from our model yields plausible and diverse two-hand shapes in close interaction with or without an object. Our prior can be incorporated into any optimization or learning methods to reduce ambiguity in an ill-posed setup. Our key observation is that directly modeling the joint distribution of multiple instances imposes high learning complexity due to its combinatorial nature. Thus we propose to decompose the modeling of joint distribution into the modeling of factored unconditional and conditional single instance distribution. In particular we introduce a diffusion model that learns the single-hand distribution unconditional and conditional to another hand via conditioning dropout. For sampling we combine anti-penetration and classifier-free guidance to enable plausible generation. Furthermore we establish the rigorous evaluation protocol of two-hand synthesis where our method significantly outperforms baseline generative models in terms of plausibility and diversity. We also demonstrate that our diffusion prior can boost the performance of two-hand reconstruction from monocular in-the-wild images achieving new state-of-the-art accuracy.
CPR-Coach: Recognizing Composite Error Actions based on Single-class Training
Authors: Shunli Wang; Shuaibing Wang; Dingkang Yang; Mingcheng Li; Haopeng Kuang; Xiao Zhao; Liuzhen Su; Peng Zhai; Lihua Zhang
Venue: CVPR 2024 (main)

Abstract: Fine-grained medical action analysis plays a vital role in improving medical skill training efficiency but it faces the problems of data and algorithm shortage. Cardiopulmonary Resuscitation (CPR) is an essential skill in emergency treatment. Currently the assessment of CPR skills mainly depends on dummies and trainers leading to high training costs and low efficiency. For the first time this paper constructs a vision-based system to complete error action recognition and skill assessment in CPR. Specifically we define 13 types of single-error actions and 74 types of composite error actions during external cardiac compression and then develop a video dataset named CPR-Coach. By taking the CPR-Coach as a benchmark this paper investigates and compares the performance of existing action recognition models based on different data modalities. To solve the unavoidable "Single-class Training & Multi-class Testing" problem we propose a human-cognition-inspired framework named ImagineNet to improve the model's multi-error recognition performance under restricted supervision. Extensive comparison and actual deployment experiments verify the effectiveness of the framework. We hope this work could bring new inspiration to the computer vision and medical skills training communities simultaneously. The dataset and the code are publicly available on https://github.com/Shunli-Wang/CPR-Coach.
Video2Game: Real-time Interactive Realistic and Browser-Compatible Environment from a Single Video
Authors: Hongchi Xia; Zhi-Hao Lin; Wei-Chiu Ma; Shenlong Wang
Venue: CVPR 2024 (main)

Abstract: Creating high-quality and interactive virtual environments such as games and simulators often involves complex and costly manual modeling processes. In this paper we present Video2Game a novel approach that automatically converts videos of real-world scenes into realistic and interactive game environments. At the heart of our system are three core components: (i) a neural radiance fields (NeRF) module that effectively captures the geometry and visual appearance of the scene; (ii) a mesh module that distills the knowledge from NeRF for faster rendering; and (iii) a physics module that models the interactions and physical dynamics among the objects. By following the carefully designed pipeline one can construct an interactable and actionable digital replica of the real world. We benchmark our system on both indoor and large-scale outdoor scenes. We show that we can not only produce highly-realistic renderings in real-time but also build interactive games on top.
Tackling the Singularities at the Endpoints of Time Intervals in Diffusion Models
Authors: Pengze Zhang; Hubery Yin; Chen Li; Xiaohua Xie
Venue: CVPR 2024 (main)

Abstract: Most diffusion models assume that the reverse process adheres to a Gaussian distribution. However this approximation has not been rigorously validated especially at singularities where t=0 and t=1. Improperly dealing with such singularities leads to an average brightness issue in applications and limits the generation of images with extreme brightness or darkness. We primarily focus on tackling singularities from both theoretical and practical perspectives. Initially we establish the error bounds for the reverse process approximation and showcase its Gaussian characteristics at singularity time steps. Based on this theoretical insight we confirm the singularity at t=1 is conditionally removable while it at t=0 is an inherent property. Upon these significant conclusions we propose a novel plug-and-play method SingDiffusion to address the initial singular time step sampling which not only effectively resolves the average brightness issue for a wide range of diffusion models without extra training efforts but also enhances their generation capability in achieving notable lower FID scores.
MatSynth: A Modern PBR Materials Dataset
Authors: Giuseppe Vecchio; Valentin Deschaintre
Venue: CVPR 2024 (main)

Abstract: We introduce MatSynth a dataset of 4000+ CC0 ultra-high resolution PBR materials. Materials are crucial components of virtual relightable assets defining the interaction of light at the surface of geometries. Given their importance significant research effort was dedicated to their representation creation and acquisition. However in the past 6 years most research in material acquisition or generation relied either on the same unique dataset or on company-owned huge library of procedural materials. With this dataset we propose a significantly larger more diverse and higher resolution set of materials than previously publicly available. We carefully discuss the data collection process and demonstrate the benefits of this dataset for material acquisition and generation applications. The complete data further contains metadata with each material's origin license category tags creation method and when available descriptions and physical size as well as 3M+ renderings of the augmented materials in 1K under various environment lightings. The MatSynth dataset is released through the project page at: https://www.gvecchio.com/matsynth.
CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned Normalization
Authors: Yao Ni; Piotr Koniusz
Venue: CVPR 2024 (main)

Abstract: Generative Adversarial Networks (GANs) significantly advanced image generation but their performance heavily depends on abundant training data. In scenarios with limited data GANs often struggle with discriminator overfitting and unstable training. Batch Normalization (BN) despite being known for enhancing generalization and training stability has rarely been used in the discriminator of Data-Efficient GANs. Our work addresses this gap by identifying a critical flaw in BN: the tendency for gradient explosion during the centering and scaling steps. To tackle this issue we present CHAIN (lipsCHitz continuity constrAIned Normalization) which replaces the conventional centering step with zero-mean regularization and integrates a Lipschitz continuity constraint in the scaling step. CHAIN further enhances GAN training by adaptively interpolating the normalized and unnormalized features effectively avoiding discriminator overfitting. Our theoretical analyses firmly establishes CHAIN's effectiveness in reducing gradients in latent features and weights improving stability and generalization in GAN training. Empirical evidence supports our theory. CHAIN achieves state-of-the-art results in data-limited scenarios on CIFAR-10/100 ImageNet five low-shot and seven high-resolution few-shot image datasets.
RTracker: Recoverable Tracking via PN Tree Structured Memory
Authors: Yuqing Huang; Xin Li; Zikun Zhou; Yaowei Wang; Zhenyu He; Ming-Hsuan Yang
Venue: CVPR 2024 (main)

Abstract: Existing tracking methods mainly focus on learning better target representation or developing more robust prediction models to improve tracking performance. While tracking performance has significantly improved the target loss issue occurs frequently due to tracking failures complete occlusion or out-of-view situations. However considerably less attention is paid to the self-recovery issue of tracking methods which is crucial for practical applications. To this end we propose a recoverable tracking framework \ourmethod that uses a tree-structured memory to dynamically associate a tracker and a detector to enable self-recovery ability. Specifically we propose a Positive-Negative Tree-structured memory to chronologically store and maintain positive and negative target samples. Upon the PN tree memory we develop corresponding walking rules for determining the state of the target and define a set of control flows to unite the tracker and the detector in different tracking scenarios. Our core idea is to use the support samples of positive and negative target categories to establish a relative distance-based criterion for a reliable assessment of target loss. The favorable performance in comparison against the state-of-the-art methods on numerous challenging benchmarks demonstrates the effectiveness of the proposed algorithm. All the source code and trained models will be released at https://github.com/NorahGreen/RTracker.
High-Quality Facial Geometry and Appearance Capture at Home
Authors: Yuxuan Han; Junfeng Lyu; Feng Xu
Venue: CVPR 2024 (main)

Abstract: Facial geometry and appearance capture have demonstrated tremendous success in 3D scanning real humans in studios. Recent works propose to democratize this technique while keeping the results high quality. However they are still inconvenient for daily usage. In addition they focus on an easier problem of only capturing facial skin. This paper proposes a novel method for high-quality face capture featuring an easy-to-use system and the capability to model the complete face with skin mouth interior hair and eyes. We reconstruct facial geometry and appearance from a single co-located smartphone flashlight sequence captured in a dim room where the flashlight is the dominant light source (e.g. rooms with curtains or at night). To model the complete face we propose a novel hybrid representation to effectively model both eyes and other facial regions along with novel techniques to learn it from images. We apply a combined lighting model to compactly represent real illuminations and exploit a morphable face albedo model as a reflectance prior to disentangle diffuse and specular. Experiments show that our method can capture high-quality 3D relightable scans. Our code will be released.