GenAI4ELab/papercli-papers huggingface dataset:
A Unified Approach for Text- and Image-guided 4D Scene Generation
Authors: Yufeng Zheng; Xueting Li; Koki Nagano; Sifei Liu; Otmar Hilliges; Shalini De Mello
Venue: CVPR 2024 (main)

Abstract: Large-scale diffusion generative models are greatly simplifying image video and 3D asset creation from user provided text prompts and images. However the challenging problem of text-to-4D dynamic 3D scene generation with diffusion guidance remains largely unexplored. We propose Dream-in-4D which features a novel two-stage approach for text-to-4D synthesis leveraging (1) 3D and 2D diffusion guidance to effectively learn a high-quality static 3D asset in the first stage; (2) a deformable neural radiance field that explicitly disentangles the learned static asset from its deformation preserving quality during motion learning; and (3) a multi-resolution feature grid for the deformation field with a displacement total variation loss to effectively learn motion with video diffusion guidance in the second stage. Through a user preference study we demonstrate that our approach significantly advances image and motion quality 3D consistency and text fidelity for text-to-4D generation compared to baseline approaches. Thanks to its motion-disentangled representation Dream-in-4D can also be easily adapted for controllable generation where appearance is defined by one or multiple images without the need to modify the motion learning stage. Thus our method offers for the first time a unified approach for text-to-4D image-to-4D and personalized 4D generation tasks.
GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians
Authors: Liangxiao Hu; Hongwen Zhang; Yuxiang Zhang; Boyao Zhou; Boning Liu; Shengping Zhang; Liqiang Nie
Venue: CVPR 2024 (main)

Abstract: We present GaussianAvatar an efficient approach to creating realistic human avatars with dynamic 3D appearances from a single video. We start by introducing animatable 3D Gaussians to explicitly represent humans in various poses and clothing styles. Such an explicit and animatable representation can fuse 3D appearances more efficiently and consistently from 2D observations. Our representation is further augmented with dynamic properties to support pose-dependent appearance modeling where a dynamic appearance network along with an optimizable feature tensor is designed to learn the motion-to-appearance mapping. Moreover by leveraging the differentiable motion condition our method enables a joint optimization of motions and appearances during avatar modeling which helps to tackle the long-standing issue of inaccurate motion estimation in monocular settings. The efficacy of GaussianAvatar is validated on both the public dataset and our collected dataset demonstrating its superior performances in terms of appearance quality and rendering efficiency.
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark
Authors: Sanghyun Woo; Kwanyong Park; Inkyu Shin; Myungchul Kim; In So Kweon
Venue: CVPR 2024 (main)

Abstract: Multi-target multi-camera tracking is a crucial task that involves identifying and tracking individuals over time using video streams from multiple cameras. This task has practical applications in various fields such as visual surveillance crowd behavior analysis and anomaly detection. However due to the difficulty and cost of collecting and labeling data existing datasets for this task are either synthetically generated or artificially constructed within a controlled camera network setting which limits their ability to model real-world dynamics and generalize to diverse camera configurations. To address this issue we present MTMMC a real-world large-scale dataset that includes long video sequences captured by 16 multi-modal cameras in two different environments - campus and factory - across various time weather and season conditions. This dataset provides a challenging test bed for studying multi-camera tracking under diverse real-world complexities and includes an additional input modality of spatially aligned and temporally synchronized RGB and thermal cameras which enhances the accuracy of multi-camera tracking. MTMMC is a super-set of existing datasets benefiting independent fields such as person detection re-identification and multiple object tracking. We provide baselines and new learning setups on this dataset and set the reference scores for future studies. The datasets models and test server will be made publicly available.
Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning
Authors: Wei Zhang; Chaoqun Wan; Tongliang Liu; Xinmei Tian; Xu Shen; Jieping Ye
Venue: CVPR 2024 (main)

Abstract: Extending large image-text pre-trained models (e.g. CLIP) for video understanding has made significant advancements. To enable the capability of CLIP to perceive dynamic information in videos existing works are dedicated to equipping the visual encoder with various temporal modules. However these methods exhibit "asymmetry" between the visual and textual sides with neither temporal descriptions in input texts nor temporal modules in text encoder. This limitation hinders the potential of language supervision emphasized in CLIP and restricts the learning of temporal features as the text encoder has demonstrated limited proficiency in motion understanding. To address this issue we propose leveraging "MoTion-Enhanced Descriptions" (MoTED) to facilitate the extraction of distinctive temporal features in videos. Specifically we first generate discriminative motion-related descriptions via querying GPT-4 to compare easy-confusing action categories. Then we incorporate both the visual and textual encoders with additional perception modules to process the video frames and generated descriptions respectively. Finally we adopt a contrastive loss to align the visual and textual motion features. Extensive experiments on five benchmarks show that MoTED surpasses state-of-the-art methods with convincing gaps laying a solid foundation for empowering CLIP with strong temporal modeling.
DAP: A Dynamic Adversarial Patch for Evading Person Detectors
Authors: Amira Guesmi; Ruitian Ding; Muhammad Abdullah Hanif; Ihsen Alouani; Muhammad Shafique
Venue: CVPR 2024 (main)

Abstract: Patch-based adversarial attacks were proven to compromise the robustness and reliability of computer vision systems. However their conspicuous and easily detectable nature challenge their practicality in real-world setting. To address this recent work has proposed using Generative Adversarial Networks (GANs) to generate naturalistic patches that may not attract human attention. However such approaches suffer from a limited latent space making it challenging to produce a patch that is efficient stealthy and robust to multiple real-world transformations. This paper introduces a novel approach that produces a Dynamic Adversarial Patch (DAP) designed to overcome these limitations. DAP maintains a naturalistic appearance while optimizing attack efficiency and robustness to real-world transformations. The approach involves redefining the optimization problem and introducing a novel objective function that incorporates a similarity metric to guide the patch's creation. Unlike GAN-based techniques the DAP directly modifies pixel values within the patch providing increased flexibility and adaptability to multiple transformations. Furthermore most clothing-based physical attacks assume static objects and ignore the possible transformations caused by non-rigid deformation due to changes in a person's pose. To address this limitation a `Creases Transformation' (CT) block is introduced enhancing the patch's resilience to a variety of real-world distortions. Experimental results demonstrate that the proposed approach outperforms state-of-the-art attacks achieving a success rate of up to 82.28% in the digital world when targeting the YOLOv7 detector and 65% in the physical world when targeting YOLOv3tiny detector deployed in edge-based smart cameras.
Learned Lossless Image Compression based on Bit Plane Slicing
Authors: Zhe Zhang; Huairui Wang; Zhenzhong Chen; Shan Liu
Venue: CVPR 2024 (main)

Abstract: Autoregressive Initial Bits (ArIB) a framework that combines subimage autoregression and latent variable models has shown its advantages in lossless image compression. However in current methods the image splitting makes the information of latent variables being uniformly distributed in each subimage and causes inadequate use of latent variables in addition to posterior collapse. To tackle these issues we introduce Bit Plane Slicing (BPS) splitting images in the bit plane dimension with the considerations on different importance for latent variables. Thus BPS provides a more effective representation by arranging subimages with decreasing importance for latent variables. To solve the problem of the increased number of dimensions caused by BPS we further propose a dimension-tailored autoregressive model that tailors autoregression methods for each dimension based on their characteristics efficiently capturing the dependencies in plane space and color dimensions. As shown in the extensive experimental results our method demonstrates the superior compression performance with comparable inference speed when compared to the state-of-the-art normalizing-flow-based methods. The code is at https://github.com/ZZ022/ArIB-BPS.
UV-IDM: Identity-Conditioned Latent Diffusion Model for Face UV-Texture Generation
Authors: Hong Li; Yutang Feng; Song Xue; Xuhui Liu; Bohan Zeng; Shanglin Li; Boyu Liu; Jianzhuang Liu; Shumin Han; Baochang Zhang
Venue: CVPR 2024 (main)

Abstract: 3D face reconstruction aims at generating high-fidelity 3D face shapes and textures from single-view or multi-view images. However current prevailing facial texture generation methods generally suffer from low-quality texture identity information loss and inadequate handling of occlusions. To solve these problems we introduce an Identity-Conditioned Latent Diffusion Model for face UV-texture generation (UV-IDM) to generate photo-realistic textures based on the Basel Face Model (BFM). UV-IDM leverages the powerful texture generation capacity of a latent diffusion model (LDM) to obtain detailed facial textures. To preserve the identity during the reconstruction procedure we design an identity-conditioned module that can utilize any in-the-wild image as a robust condition for the LDM to guide texture generation. UV-IDM can be easily adapted to different BFM-based methods as a high-fidelity texture generator. Furthermore in light of the limited accessibility of most existing UV-texture datasets we build a large-scale and publicly available UV-texture dataset based on BFM termed BFM-UV. Extensive experiments show that our UV-IDM can generate high-fidelity textures in 3D face reconstruction within seconds while maintaining image consistency bringing new state-of-the-art performance in facial texture generation.
Mosaic-SDF for 3D Generative Models
Authors: Lior Yariv; Omri Puny; Oran Gafni; Yaron Lipman
Venue: CVPR 2024 (main)

Abstract: Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models and training directly on 3D shapes. When training a diffusion or flow models on 3D shapes a crucial design choice is the shape representation. An effective shape representation needs to adhere three design principles: it should allow an efficient conversion of large 3D datasets to the representation form; it should provide a good tradeoff of approximation power versus number of parameters; and it should have a simple tensorial form that is compatible with existing powerful neural architectures. While standard 3D shape representations such as volumetric grids and point clouds do not adhere to all these principles simultaneously we advocate in this paper a new representation that does. We introduce Mosaic-SDF (M-SDF): a simple 3D shape representation that approximates the Signed Distance Function (SDF) of a given shape by using a set of local grids spread near the shape's boundary. The M-SDF representation is fast to compute for each shape individually making it readily parallelizable; it is parameter efficient as it only covers the space around the shape's boundary; and it has a simple matrix form compatible with Transformer-based architectures. We demonstrate the efficacy of the M-SDF representation by using it to train a 3D generative flow model including class-conditioned generation with the ShapeNetCore-V2 (3D Warehouse) dataset and text-to-3D generation using a dataset of about 600k caption-shape pairs.
Diffusion Handles Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D
Authors: Karran Pandey; Paul Guerrero; Matheus Gadelha; Yannick Hold-Geoffroy; Karan Singh; Niloy J. Mitra
Venue: CVPR 2024 (main)

Abstract: Diffusion handles is a novel approach to enable 3D object edits on diffusion images requiring only existing pre-trained diffusion models depth estimation without any fine-tuning or 3D object retrieval. The edited results remain plausible photo-real and preserve object identity. Diffusion handles address a critically missing facet of generative image-based creative design. Our key insight is to lift diffusion activations for a selected object to 3D using a proxy depth 3D-transform the depth and associated activations and project them back to image space. The diffusion process guided by the manipulated activations produces plausible edited images showing complex 3D occlusion and lighting effects. We evaluate diffusion handles: quantitatively on a large synthetic data benchmark; and qualitatively by a user study showing our output to be more plausible and better than prior art at both 3D editing and identity control.
A Pedestrian is Worth One Prompt: Towards Language Guidance Person Re-Identification
Authors: Zexian Yang; Dayan Wu; Chenming Wu; Zheng Lin; Jingzi Gu; Weiping Wang
Venue: CVPR 2024 (main)

Abstract: Extensive advancements have been made in person ReID through the mining of semantic information. Nevertheless existing methods that utilize semantic-parts from a single image modality do not explicitly achieve this goal. Whiteness the impressive capabilities in multimodal understanding of Vision Language Foundation Model CLIP a recent two-stage CLIP-based method employs automated prompt engineering to obtain specific textual labels for classifying pedestrians. However we note that the predefined soft prompts may be inadequate in expressing the entire visual context and struggle to generalize to unseen classes. This paper presents an end-to-end Prompt-driven Semantic Guidance (PromptSG) framework that harnesses the rich semantics inherent in CLIP. Specifically we guide the model to attend to regions that are semantically faithful to the prompt. To provide personalized language descriptions for specific individuals we propose learning pseudo tokens that represent specific visual contexts. This design not only facilitates learning fine-grained attribute information but also can inherently leverage language prompts during inference. Without requiring additional labeling efforts our PromptSG achieves state-of-the-art by over 10% on MSMT17 and nearly 5% on the Market-1501 benchmark.
Friendly Sharpness-Aware Minimization
Authors: Tao Li; Pan Zhou; Zhengbao He; Xinwen Cheng; Xiaolin Huang
Venue: CVPR 2024 (main)

Abstract: Sharpness-Aware Minimization (SAM) has been instrumental in improving deep neural network training by minimizing both training loss and loss sharpness. Despite the practical success the mechanisms behind SAM's generalization enhancements remain elusive limiting its progress in deep learning optimization. In this work we investigate SAM's core components for generalization improvement and introduce "Friendly-SAM" (F-SAM) to further enhance SAM's generalization. Our investigation reveals the key role of batch-specific stochastic gradient noise within the adversarial perturbation i.e. the current minibatch gradient which significantly influences SAM's generalization performance. By decomposing the adversarial perturbation in SAM into full gradient and stochastic gradient noise components we discover that relying solely on the full gradient component degrades generalization while excluding it leads to improved performance. The possible reason lies in the full gradient component's increase in sharpness loss for the entire dataset creating inconsistencies with the subsequent sharpness minimization step solely on the current minibatch data. Inspired by these insights F-SAM aims to mitigate the negative effects of the full gradient component. It removes the full gradient estimated by an exponentially moving average (EMA) of historical stochastic gradients and then leverages stochastic gradient noise for improved generalization. Moreover we provide theoretical validation for the EMA approximation and prove the convergence of F-SAM on non-convex problems. Extensive experiments demonstrate the superior generalization performance and robustness of F-SAM over vanilla SAM. Code is available at https://github.com/nblt/F-SAM.
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models
Authors: Fengyuan Shi; Jiaxi Gu; Hang Xu; Songcen Xu; Wei Zhang; Limin Wang
Venue: CVPR 2024 (main)

Abstract: Diffusion models have made tremendous progress in text-driven image and video generation. Now text-to-image foundation models are widely applied to various downstream image synthesis tasks such as controllable image generation and image editing while downstream video synthesis tasks are less explored for several reasons. First it requires huge memory and computation overhead to train a video generation foundation model. Even with video foundation models additional costly training is still required for downstream video synthesis tasks. Second although some works extend image diffusion models into videos in a training-free manner temporal consistency cannot be well preserved. Finally these adaption methods are specifically designed for one task and fail to generalize to different tasks. To mitigate these issues we propose a training-free general-purpose video synthesis framework coined as BIVDiff via bridging specific image diffusion models and general text-to-video foundation diffusion models. Specifically we first use a specific image diffusion model (e.g. ControlNet and Instruct Pix2Pix) for frame-wise video generation then perform Mixed Inversion on the generated video and finally input the inverted latents into the video diffusion models (e.g. VidRD and ZeroScope) for temporal smoothing. This decoupled framework enables flexible image model selection for different purposes with strong task generalization and high efficiency. To validate the effectiveness and general use of BIVDiff we perform a wide range of video synthesis tasks including controllable video generation video editing video inpainting and outpainting.
NC-TTT: A Noise Constrastive Approach for Test-Time Training
Authors: David Osowiechi; Gustavo A. Vargas Hakim; Mehrdad Noori; Milad Cheraghalikhani; Ali Bahri; Moslem Yazdanpanah; Ismail Ben Ayed; Christian Desrosiers
Venue: CVPR 2024 (main)

Abstract: Despite their exceptional performance in vision tasks deep learning models often struggle when faced with domain shifts during testing. Test-Time Training (TTT) methods have recently gained popularity by their ability to enhance the robustness of models through the addition of an auxiliary objective that is jointly optimized with the main task. Being strictly unsupervised this auxiliary objective is used at test time to adapt the model without any access to labels. In this work we propose Noise-Contrastive Test-Time Training (NC-TTT) a novel unsupervised TTT technique based on the discrimination of noisy feature maps. By learning to classify noisy views of projected feature maps and then adapting the model accordingly on new domains classification performance can be recovered by an important margin. Experiments on several popular test-time adaptation baselines demonstrate the advantages of our method compared to recent approaches for this task. The code can be found at: https://github.com/GustavoVargasHakim/NCTTT.git
NetTrack: Tracking Highly Dynamic Objects with a Net
Authors: Guangze Zheng; Shijie Lin; Haobo Zuo; Changhong Fu; Jia Pan
Venue: CVPR 2024 (main)

Abstract: The complex dynamicity of open-world objects presents non-negligible challenges for multi-object tracking (MOT) often manifested as severe deformations fast motion and occlusions. Most methods that solely depend on coarse-grained object cues such as boxes and the overall appearance of the object are susceptible to degradation due to distorted internal relationships of dynamic objects. To address this problem this work proposes NetTrack an efficient generic and affordable tracking framework to introduce fine-grained learning that is robust to dynamicity. Specifically NetTrack constructs a dynamicity-aware association with a fine-grained Net leveraging point-level visual cues. Correspondingly a fine-grained sampler and matching method have been incorporated. Furthermore NetTrack learns object-text correspondence for fine-grained localization. To evaluate MOT in extremely dynamic open-world scenarios a bird flock tracking (BFT) dataset is constructed which exhibits high dynamicity with diverse species and open-world scenarios. Comprehensive evaluation on BFT validates the effectiveness of fine-grained learning on object dynamicity and thorough transfer experiments on challenging open-world benchmarks i.e. TAO TAO-OW AnimalTrack and GMOT-40 validate the strong generalization ability of NetTrack even without finetuning.
Grounded Question-Answering in Long Egocentric Videos
Authors: Shangzhe Di; Weidi Xie
Venue: CVPR 2024 (main)

Abstract: Existing approaches to video understanding mainly designed for short videos from a third-person perspective are limited in their applicability in certain fields such as robotics. In this paper we delve into open-ended question-answering (QA) in long egocentric videos which allows individuals or robots to inquire about their own past visual experiences. This task presents unique challenges including the complexity of temporally grounding queries within extensive video content the high resource demands for precise data annotation and the inherent difficulty of evaluating open-ended answers due to their ambiguous nature. Our proposed approach tackles these challenges by (i) integrating query grounding and answering within a unified model to reduce error propagation; (ii) employing large language models for efficient and scalable data synthesis; and (iii) introducing a close-ended QA task for evaluation to manage answer ambiguity. Extensive experiments demonstrate the effectiveness of our method which also achieves state-of-the-art performance on the QAEgo4D and Ego4D-NLQ benchmarks. Code data and models are open-sourced at https://github.com/Becomebright/GroundVQA.