GenAI4ELab/papercli-papers huggingface dataset:
DanceCamera3D: 3D Camera Movement Synthesis with Music and Dance
Authors: Zixuan Wang; Jia Jia; Shikun Sun; Haozhe Wu; Rong Han; Zhenyu Li; Di Tang; Jiaqing Zhou; Jiebo Luo
Venue: CVPR 2024 (main)

Abstract: Choreographers determine what the dances look like while cameramen determine the final presentation of dances. Recently various methods and datasets have showcased the feasibility of dance synthesis. However camera movement synthesis with music and dance remains an unsolved challenging problem due to the scarcity of paired data. Thus we present DCM a new multi-modal 3D dataset which for the first time combines camera movement with dance motion and music audio. This dataset encompasses 108 dance sequences (3.2 hours) of paired dance-camera-music data from the anime community covering 4 music genres. With this dataset we uncover that dance camera movement is multifaceted and human-centric and possesses multiple influencing factors making dance camera synthesis a more challenging task compared to camera or dance synthesis alone. To overcome these difficulties we propose DanceCamera3D a transformer-based diffusion model that incorporates a novel body attention loss and a condition separation strategy. For evaluation we devise new metrics measuring camera movement quality diversity and dancer fidelity. Utilizing these metrics we conduct extensive experiments on our DCM dataset providing both quantitative and qualitative evidence showcasing the effectiveness of our DanceCamera3D model. Code and video demos are available at https://github.com/ Carmenw1203/DanceCamera3D-Official.
Synthesize Diagnose and Optimize: Towards Fine-Grained Vision-Language Understanding
Authors: Wujian Peng; Sicheng Xie; Zuyao You; Shiyi Lan; Zuxuan Wu
Venue: CVPR 2024 (main)

Abstract: Vision language models (VLM) have demonstrated remarkable performance across various downstream tasks. However understanding fine-grained visual-linguistic concepts such as attributes and inter-object relationships remains a significant challenge. While several benchmarks aim to evaluate VLMs in finer granularity their primary focus remains on the linguistic aspect neglecting the visual dimension. Here we highlight the importance of evaluating VLMs from both a textual and visual perspective. We introduce a progressive pipeline to synthesize images that vary in a specific attribute while ensuring consistency in all other aspects. Utilizing this data engine we carefully design a benchmark SPEC to diagnose the comprehension of object size position existence and count. Subsequently we conduct a thorough evaluation of four leading VLMs on SPEC. Surprisingly their performance is close to random guess revealing significant limitations. With this in mind we propose a simple yet effective approach to optimize VLMs in fine-grained understanding achieving significant improvements on SPEC without compromising the zero-shot performance. Results on two additional fine-grained benchmarks also show consistent improvements further validating the transferability of our approach. Code and data are available at https://github.com/wjpoom/SPEC.
Density-guided Translator Boosts Synthetic-to-Real Unsupervised Domain Adaptive Segmentation of 3D Point Clouds
Authors: Zhimin Yuan; Wankang Zeng; Yanfei Su; Weiquan Liu; Ming Cheng; Yulan Guo; Cheng Wang
Venue: CVPR 2024 (main)

Abstract: 3D synthetic-to-real unsupervised domain adaptive segmentation is crucial to annotating new domains. Self-training is a competitive approach for this task but its performance is limited by different sensor sampling patterns (i.e. variations in point density) and incomplete training strategies. In this work we propose a density-guided translator (DGT) which translates point density between domains and integrates it into a two-stage self-training pipeline named DGT-ST. First in contrast to existing works that simultaneously conduct data generation and feature/output alignment within unstable adversarial training we employ the non-learnable DGT to bridge the domain gap at the input level. Second to provide a well-initialized model for self-training we propose a category-level adversarial network in stage one that utilizes the prototype to prevent negative transfer. Finally by leveraging the designs above a domain-mixed self-training method with source-aware consistency loss is proposed in stage two to narrow the domain gap further. Experiments on two synthetic-to-real segmentation tasks (SynLiDAR ? semanticKITTI and SynLiDAR ? semanticPOSS) demonstrate that DGT-ST outperforms state-of-the-art methods achieving 9.4% and 4.3% mIoU improvements respectively. Code is available at https://github.com/yuan-zm/DGT-ST.
Cross Initialization for Face Personalization of Text-to-Image Models
Authors: Lianyu Pang; Jian Yin; Haoran Xie; Qiping Wang; Qing Li; Xudong Mao
Venue: CVPR 2024 (main)

Abstract: Recently there has been a surge in face personalization techniques benefiting from the advanced capabilities of pretrained text-to-image diffusion models. Among these a notable method is Textual Inversion which generates personalized images by inverting given images into textual embeddings. However methods based on Textual Inversion still struggle with balancing the trade-off between reconstruction quality and editability. In this study we examine this issue through the lens of initialization. Upon closely examining traditional initialization methods we identified a significant disparity between the initial and learned embeddings in terms of both scale and orientation. The scale of the learned embedding can be up to 100 times greater than that of the initial embedding. Such a significant change in the embedding could increase the risk of overfitting thereby compromising the editability. Driven by this observation we introduce a novel initialization method termed Cross Initialization that significantly narrows the gap between the initial and learned embeddings. This method not only improves both reconstruction and editability but also reduces the optimization steps from 5000 to 320. Furthermore we apply a regularization term to keep the learned embedding close to the initial embedding. We show that when combined with Cross Initialization this regularization term can effectively improve editability. We provide comprehensive empirical evidence to demonstrate the superior performance of our method compared to the baseline methods. Notably in our experiments Cross Initialization is the only method that successfully edits an individual's facial expression. Additionally a fast version of our method allows for capturing an input image in roughly 26 seconds while surpassing the baseline methods in terms of both reconstruction and editability. Code is available at https://github.com/lyuPang/CrossInitialization.
LEDITS++: Limitless Image Editing using Text-to-Image Models
Authors: Manuel Brack; Felix Friedrich; Katharia Kornmeier; Linoy Tsaban; Patrick Schramowski; Kristian Kersting; Apolinario Passos
Venue: CVPR 2024 (main)

Abstract: Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their capabilities to real image editing. However existing image-to-image methods are often inefficient imprecise and of limited versatility. They either require time-consuming fine-tuning deviate unnecessarily strongly from the input image and/or lack support for multiple simultaneous edits. To address these issues we introduce LEDITS++ an efficient yet versatile and precise textual image manipulation technique. LEDITS++'s novel inversion approach requires no tuning nor optimization and produces high-fidelity results with a few diffusion steps. Second our methodology supports multiple simultaneous edits and is architecture-agnostic. Third we use a novel implicit masking technique that limits changes to relevant image regions. We propose the novel TEdBench++ benchmark as part of our exhaustive evaluation. Our results demonstrate the capabilities of LEDITS++ and its improvements over previous methods.
Video Interpolation with Diffusion Models
Authors: Siddhant Jain; Daniel Watson; Eric Tabellion; Aleksander Ho?ynski; Ben Poole; Janne Kontkanen
Venue: CVPR 2024 (main)

Abstract: We present VIDIM a generative model for video interpolation which creates short videos given a start and end frame. In order to achieve high fidelity and generate motions unseen in the input data VIDIM uses cascaded diffusion models to first generate the target video at low resolution and then generate the high-resolution video conditioned on the low-resolution generated video. We compare VIDIM to previous state-of-the-art methods on video interpolation and demonstrate how such works fail in most settings where the underlying motion is complex nonlinear or ambiguous while VIDIM can easily handle such cases. We additionally demonstrate how classifier-free guidance on the start and end frame and conditioning the superresolution model on the original high-resolution frames without additional parameters unlocks high-fidelity results. VIDIM is fast to sample from as it jointly denoises all the frames to be generated requires less than a billion parameters per diffusion model to produce compelling results and still enjoys scalability and improved quality at larger parameter counts. Please see our project page at vidiminterpolation.github.io.
WildlifeMapper: Aerial Image Analysis for Multi-Species Detection and Identification
Authors: Satish Kumar; Bowen Zhang; Chandrakanth Gudavalli; Connor Levenson; Lacey Hughey; Jared A. Stabach; Irene Amoke; Gordon Ojwang; Joseph Mukeka; Stephen Mwiu; Joseph Ogutu; Howard Frederick; B.S. Manjunath
Venue: CVPR 2024 (main)

Abstract: We introduce WildlifeMapper (WM) a flexible model designed to detect locate and identify multiple species in aerial imagery. It addresses the limitations of traditional labor-intensive wildlife population assessments that are central to advancing environmental conservation efforts worldwide. While a number of methods exist to automate this process they are often limited in their ability to generalize to different species or landscapes due to the dominance of homogeneous backgrounds and/or poorly captured local image structures. WM introduces two novel modules that help to capture the local structure and context of objects of interest to accurately localize and identify them achieving a state-of-the-art (SOTA) detection rate of 0.56 mAP. Further we introduce a large aerial imagery dataset with more than 11k Images and 28k annotations verified by trained experts. WM also achieves SOTA performance on 3 other publicly available aerial survey datasets collected across 4 different countries improving mAP by 42%. Source code and trained models are available at Github
Learning Adaptive Spatial Coherent Correlations for Speech-Preserving Facial Expression Manipulation
Authors: Tianshui Chen; Jianman Lin; Zhijing Yang; Chunmei Qing; Liang Lin
Venue: CVPR 2024 (main)

Abstract: Speech-preserving facial expression manipulation (SPFEM) aims to modify facial emotions while meticulously maintaining the mouth animation associated with spoken content. Current works depend on inaccessible paired training samples for the person where two aligned frames exhibit the same speech content yet differ in emotional expression limiting the SPFEM applications in real-world scenarios. In this work we discover that speakers who convey the same content with different emotions exhibit highly correlated local facial animations providing valuable supervision for SPFEM. To capitalize on this insight we propose a novel adaptive spatial coherent correlation learning (ASCCL) algorithm which models the aforementioned correlation as an explicit metric and integrates the metric to supervise manipulating facial expression and meanwhile better preserving the facial animation of spoken contents. To this end it first learns a spatial coherent correlation metric ensuring the visual disparities of adjacent local regions of the image belonging to one emotion are similar to those of the corresponding counterpart of the image belonging to another emotion. Recognizing that visual disparities are not uniform across all regions we have also crafted a disparity-aware adaptive strategy that prioritizes regions that present greater challenges. During SPFEM model training we construct the adaptive spatial coherent correlation metric between corresponding local regions of the input and output images as addition loss to supervise the generation process. We conduct extensive experiments on variant datasets and the results demonstrate the effectiveness of the proposed ASCCL algorithm. Code is publicly available at https://github.com/jianmanlincjx/ASCCL
Tune-An-Ellipse: CLIP Has Potential to Find What You Want
Authors: Jinheng Xie; Songhe Deng; Bing Li; Haozhe Liu; Yawen Huang; Yefeng Zheng; Jurgen Schmidhuber; Bernard Ghanem; Linlin Shen; Mike Zheng Shou
Venue: CVPR 2024 (main)

Abstract: Visual prompting of large vision language models such as CLIP exhibits intriguing zero-shot capabilities. A manually drawn red circle commonly used for highlighting can guide CLIP's attention to the surrounding region to identify specific objects within an image. Without precise object proposals however it is insufficient for localization. Our novel simple yet effective approach i.e. Differentiable Visual Prompting enables CLIP to zero-shot localize: given an image and a text prompt describing an object we first pick a rendered ellipse from uniformly distributed anchor ellipses on the image grid via visual prompting then use three loss functions to tune the ellipse coefficients to encapsulate the target region gradually. This yields promising experimental results for referring expression comprehension without precisely specified object proposals. In addition we systematically present the limitations of visual prompting inherent in CLIP and discuss potential solutions.
Neural Spline Fields for Burst Image Fusion and Layer Separation
Authors: Ilya Chugunov; David Shustin; Ruyu Yan; Chenyang Lei; Felix Heide
Venue: CVPR 2024 (main)

Abstract: Each photo in an image burst can be considered a sample of a complex 3D scene: the product of parallax diffuse and specular materials scene motion and illuminant variation. While decomposing all of these effects from a stack of misaligned images is a highly ill-conditioned task the conventional align-and-merge burst pipeline takes the other extreme: blending them into a single image. In this work we propose a versatile intermediate representation: a two-layer alpha-composited image plus flow model constructed with neural spline fields -- networks trained to map input coordinates to spline control points. Our method is able to during test-time optimization jointly fuse a burst image capture into one high-resolution reconstruction and decompose it into transmission and obstruction layers. Then by discarding the obstruction layer we can perform a range of tasks including seeing through occlusions reflection suppression and shadow removal. Tested on complex in-the-wild captures we find that with no post-processing steps or learned priors our generalizable model is able to outperform existing dedicated single-image and multi-view obstruction removal approaches.
WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion
Authors: Soyong Shin; Juyong Kim; Eni Halilaj; Michael J. Black
Venue: CVPR 2024 (main)

Abstract: The estimation of 3D human motion from video has progressed rapidly but current methods still have several key limitations. First most methods estimate the human in camera coordinates. Second prior work on estimating humans in global coordinates often assumes a flat ground plane and produces foot sliding. Third the most accurate methods rely on computationally expensive optimization pipelines limiting their use to offline applications. Finally existing video-based methods are surprisingly less accurate than single-frame methods. We address these limitations with WHAM (World-grounded Humans with Accurate Motion) which accurately and efficiently reconstructs 3D human motion in a global coordinate system from video. WHAM learns to lift 2D keypoint sequences to 3D using motion capture data and fuses this with video features integrating motion context and visual information. WHAM exploits camera angular velocity estimated from a SLAM method together with human motion to estimate the body's global trajectory. We combine this with a contact-aware trajectory refinement method that lets WHAM capture human motion in diverse conditions such as climbing stairs. WHAM outperforms all existing 3D human motion recovery methods across multiple in-the-wild benchmarks. Code is available for research purposes at http://wham.is.tue.mpg.de/.
NAPGuard: Towards Detecting Naturalistic Adversarial Patches
Authors: Siyang Wu; Jiakai Wang; Jiejie Zhao; Yazhe Wang; Xianglong Liu
Venue: CVPR 2024 (main)

Abstract: Recently the emergence of naturalistic adversarial patch (NAP) which possesses a deceptive appearance and various representations underscores the necessity of developing robust detection strategies. However existing approaches fail to differentiate the deep-seated natures in adversarial patches i.e. aggressiveness and naturalness leading to unsatisfactory precision and generalization against NAPs. To tackle this issue we propose NAPGuard to provide strong detection capability against NAPs via the elaborated critical feature modulation framework. For improving precision we propose the aggressive feature aligned learning to enhance the model's capability in capturing accurate aggressive patterns. Considering the challenge of inaccurate model learning caused by deceptive appearance we align the aggressive features by the proposed pattern alignment loss during training. Since the model could learn more accurate aggressive patterns it is able to detect deceptive patches more precisely. To enhance generalization we design the natural feature suppressed inference to universally mitigate the disturbance from different NAPs. Since various representations arise in diverse disturbing forms to hinder generalization we suppress the natural features in a unified approach via the feature shield module. Therefore the models could recognize NAPs within less disturbance and activate the generalized detection ability. Extensive experiments show that our method surpasses state-of-the-art methods by large margins in detecting NAPs (improve 60.24% AP@0.5 on average).
DiffPerformer: Iterative Learning of Consistent Latent Guidance for Diffusion-based Human Video Generation
Authors: Chenyang Wang; Zerong Zheng; Tao Yu; Xiaoqian Lv; Bineng Zhong; Shengping Zhang; Liqiang Nie
Venue: CVPR 2024 (main)

Abstract: Existing diffusion models for pose-guided human video generation mostly suffer from temporal inconsistency in the generated appearance and poses due to the inherent randomization nature of the generation process. In this paper we propose a novel framework DiffPerformer to synthesize high-fidelity and temporally consistent human video. Without complex architecture modification or costly training DiffPerformer finetunes a pretrained diffusion model on a single video of the target character and introduces an implicit video representation as a proxy to learn temporally consistent guidance for the diffusion model. The guidance is encoded into VAE latent space and an iterative optimization loop is constructed between the implicit video representation and the diffusion model allowing to harness the smooth property of the implicit video representation and the generative capabilities of the diffusion model in a mutually beneficial way. Moreover we propose 3D-aware human flow as a temporal constraint during the optimization to explicitly model the correspondence between driving poses and human appearance. This alleviates the misalignment between guided poses and target performer and therefore maintains the appearance coherence under various motions. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods.
Unified Language-driven Zero-shot Domain Adaptation
Authors: Senqiao Yang; Zhuotao Tian; Li Jiang; Jiaya Jia
Venue: CVPR 2024 (main)

Abstract: This paper introduces Unified Language-driven Zero-shot Domain Adaptation (ULDA) a novel task setting that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge. We identify the constraints in the existing language-driven zero-shot domain adaptation task particularly the requirement for domain IDs and domain-specific models which may restrict flexibility and scalability. To overcome these issues we propose a new framework for ULDA consisting of Hierarchical Context Alignment (HCA) Domain Consistent Representation Learning (DCRL) and Text-Driven Rectifier (TDR). These components work synergistically to align simulated features with target text across multiple visual levels retain semantic correlations between different regional representations and rectify biases between simulated and real target visual features respectively. Our extensive empirical evaluations demonstrate that this framework achieves competitive performance in both settings surpassing even the model that requires domain-ID showcasing its superiority and generalization ability. The proposed method is not only effective but also maintains practicality and efficiency as it does not introduce additional computational costs during inference. The code is available on the project website.
Category-Level Multi-Part Multi-Joint 3D Shape Assembly
Authors: Yichen Li; Kaichun Mo; Yueqi Duan; He Wang; Jiequan Zhang; Lin Shao
Venue: CVPR 2024 (main)

Abstract: Shape assembly composes complex shapes geometries by arranging simple part geometries and has wide applications in autonomous robotic assembly and CAD modeling. Existing works focus on geometry reasoning and neglect the actual physical assembly process of matching and fitting joints which are the contact surfaces connecting different parts. In this paper we consider contacting joints for the task of multi-part assembly. A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment. We propose a hierarchical graph learning approach composed of two levels of graph representation learning. The part graph takes part geometries as input to build the desired shape structure. The joint-level graph uses part joints information and focuses on matching and aligning joints. The two kinds of information are combined to achieve the bilateral objectives. Extensive experiments demonstrate that our method outperforms previous methods achieving better shape structure and higher joint alignment accuracy.
Equivariant Multi-Modality Image Fusion
Authors: Zixiang Zhao; Haowen Bai; Jiangshe Zhang; Yulun Zhang; Kai Zhang; Shuang Xu; Dongdong Chen; Radu Timofte; Luc Van Gool
Venue: CVPR 2024 (main)

Abstract: Multi-modality image fusion is a technique that combines information from different sensors or modalities enabling the fused image to retain complementary features from each modality such as functional highlights and texture details. However effective training of such fusion models is challenging due to the scarcity of ground truth fusion data. To tackle this issue we propose the Equivariant Multi-Modality imAge fusion (EMMA) paradigm for end-to-end self-supervised learning. Our approach is rooted in the prior knowledge that natural imaging responses are equivariant to certain transformations. Consequently we introduce a novel training paradigm that encompasses a fusion module a pseudo-sensing module and an equivariant fusion module. These components enable the net training to follow the principles of the natural sensing-imaging process while satisfying the equivariant imaging prior. Extensive experiments confirm that EMMA yields high-quality fusion results for infrared-visible and medical images concurrently facilitating downstream multi-modal segmentation and detection tasks. The code is available at https://github.com/Zhaozixiang1228/MMIF-EMMA.