We propose G-HOP a denoising diffusion based generative prior for hand-object interactions that allows modeling both the 3D object and a human hand conditioned on the object category. To learn a 3D spatial diffusion model that can capture this joint distribution we represent the human hand via a skeletal distance field to obtain a representation aligned with the (latent) signed distance field for the object. We show that this hand-object prior can then serve as a generic guidance to facilitate other tasks like reconstruction from interaction clip and human grasp synthesis. We believe that our model trained by aggregating several diverse real-world interaction datasets spanning 155 categories represents a first approach that allows jointly generating both hand and object. Our empirical evaluations demonstrate the benefit of this joint prior in video-based reconstruction and human grasp synthesis outperforming current task-specific baselines.
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We propose a new method for cloth digitalization. Deviating from existing methods which learn from data captured under relatively casual settings we propose to learn from data captured in strictly tested measuring protocols and find plausible physical parameters of the cloths. However such data is currently absent so we first propose a new dataset with accurate cloth measurements. Further the data size is considerably smaller than the ones in current deep learning due to the nature of the data capture process. To learn from small data we propose a new Bayesian differentiable cloth model to estimate the complex material heterogeneity of real cloths. It can provide highly accurate digitalization from very limited data samples. Through exhaustive evaluation and comparison we show our method is accurate in cloth digitalization efficient in learning from limited data samples and general in capturing material variations. Code and data are available in: https://github.com/realcrane/Bayesian-Differentiable-Physics-for-Cloth-Digitalization
While Transformers have rapidly gained popularity in various computer vision applications post-hoc explanations of their internal mechanisms remain largely unexplored. Vision Transformers extract visual information by representing image regions as transformed tokens and integrating them via attention weights. However existing post-hoc explanation methods merely consider these attention weights neglecting crucial information from the transformed tokens which fails to accurately illustrate the rationales behind the models' predictions. To incorporate the influence of token transformation into interpretation we propose TokenTM a novel post-hoc explanation method that utilizes our introduced measurement of token transformation effects. Specifically we quantify token transformation effects by measuring changes in token lengths and correlations in their directions pre- and post-transformation. Moreover we develop initialization and aggregation rules to integrate both attention weights and token transformation effects across all layers capturing holistic token contributions throughout the model. Experimental results on segmentation and perturbation tests demonstrate the superiority of our proposed TokenTM compared to state-of-the-art Vision Transformer explanation methods.
Recently Vision Transformer has achieved great success in recovering missing details in low-resolution sequences i.e. the video super-resolution (VSR) task. Despite its superiority in VSR accuracy the heavy computational burden as well as the large memory footprint hinder the deployment of Transformer-based VSR models on constrained devices. In this paper we address the above issue by proposing a novel feature-level masked processing framework: VSR with Masked Intra and inter-frame Attention (MIA-VSR). The core of MIA-VSR is leveraging feature-level temporal continuity between adjacent frames to reduce redundant computations and make more rational use of previously enhanced SR features. Concretely we propose an intra-frame and inter-frame attention block which takes the respective roles of past features and input features into consideration and only exploits previously enhanced features to provide supplementary information. In addition an adaptive block-wise mask prediction module is developed to skip unimportant computations according to feature similarity between adjacent frames. We conduct detailed ablation studies to validate our contributions and compare the proposed method with recent state-of-the-art VSR approaches. The experimental results demonstrate that MIA-VSR improves the memory and computation efficiency over state-of-the-art methods without trading off PSNR accuracy. The code is available at https://github.com/LabShuHangGU/MIA-VSR.
We introduce MAGICK a large-scale dataset of generated objects with high-quality alpha mattes. While image generation methods have produced segmentations they cannot generate alpha mattes with accurate details in hair fur and transparencies. This is likely due to the small size of current alpha matting datasets and the difficulty in obtaining ground-truth alpha. We propose a scalable method for synthesizing images of objects with high-quality alpha that can be used as a ground-truth dataset. A key idea is to generate objects on a single-colored background so chroma keying approaches can be used to extract the alpha. However this faces several challenges including that current text-to-image generation methods cannot create images that can be easily chroma keyed and that chroma keying is an underconstrained problem that generally requires manual intervention for high-quality results. We address this using a combination of generation and alpha extraction methods. Using our method we generate a dataset of 150000 objects with alpha. We show the utility of our dataset by training an alpha-to-rgb generation method that outperforms baselines. Please see our project website at https://ryanndagreat.github.io/MAGICK/.
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
PDF ↗Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multimodal conversations. However existing methods encounter challenges in effectively handling both image and video understanding particularly with limited visual tokens. In this work we introduce Chat-UniVi a Unified Vision-language model capable of comprehending and engaging in conversations involving images and videos through a unified visual representation. Specifically we employ a set of dynamic visual tokens to uniformly represent images and videos. This representation framework empowers the model to efficiently utilize a limited number of visual tokens to simultaneously capture the spatial details necessary for images and the comprehensive temporal relationship required for videos. Moreover we leverage a multi-scale representation enabling the model to perceive both high-level semantic concepts and low-level visual details. Notably Chat-UniVi is trained on a mixed dataset containing both images and videos allowing direct application to tasks involving both mediums without requiring any modifications. Extensive experimental results demonstrate that Chat-UniVi consistently outperforms even existing methods exclusively designed for either images or videos. Code is available at https://github.com/PKU-YuanGroup/Chat-UniVi.
GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis
PDF ↗We present a new approach termed GPS-Gaussian for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting. Unlike the original Gaussian Splatting or neural implicit rendering methods that necessitate per-subject optimizations we introduce Gaussian parameter maps defined on the source views and regress directly Gaussian Splatting properties for instant novel view synthesis without any fine-tuning or optimization. To this end we train our Gaussian parameter regression module on a large amount of human scan data jointly with a depth estimation module to lift 2D parameter maps to 3D space. The proposed framework is fully differentiable and experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed.
The anatomical structure detection of fetal cardiac views is crucial for diagnosing fetal congenital heart disease. In practice there is a large domain gap between different hospitals' data such as the variable data quality due to differences in acquisition equipment. In addition accurate annotation information provided by obstetrician experts is always very costly or even unavailable. This study explores the unsupervised domain adaptive fetal cardiac structure detection issue. Existing unsupervised domain adaptive object detection (UDAOD) approaches mainly focus on detecting objects in natural scenes such as Foggy Cityscapes where the structural relationships of natural scenes are uncertain. Unlike all previous UDAOD scenarios we first collected a Fetal Cardiac Structure dataset from two hospital centers called FCS and proposed a multi-matching UDA approach (M3-UDA) including Histogram Matching (HM) Sub-structure Matching (SM) and Global-structure Matching (GM) to better transfer the topological knowledge of anatomical structure for UDA detection in medical scenarios. HM mitigates the domain gap between the source and target caused by pixel transformation. SM fuses the different angle information of the sub-structure to obtain the local topological knowledge for bridging the domain gap of the internal sub-structure. GM is designed to align the global topological knowledge of the whole organ from the source and target domain. Extensive experiments on our collected FCS and CardiacUDA and experimental results show that M3-UDA outperforms existing UDAOD studies significantly. All datasets and source code are available at : https://github.com/xmed-lab/M3-UDA
Interactive Segmentation (IS) segments specific objects or parts in the image according to user input. Current IS pipelines fall into two categories: single-granularity output and multi-granularity output. The latter aims to alleviate the spatial ambiguity present in the former. However the multi-granularity output pipeline suffers from limited interaction flexibility and produces redundant results. In this work we introduce Granularity-Controllable Interactive Segmentation (GraCo) a novel approach that allows precise control of prediction granularity by introducing additional parameters to input. This enhances the customization of the interactive system and eliminates redundancy while resolving ambiguity. Nevertheless the exorbitant cost of annotating multi-granularity masks and the lack of available datasets with granularity annotations make it difficult for models to acquire the necessary guidance to control output granularity. To address this problem we design an any-granularity mask generator that exploits the semantic property of the pre-trained IS model to automatically generate abundant mask-granularity pairs without requiring additional manual annotation. Based on these pairs we propose a granularity-controllable learning strategy that efficiently imparts the granularity controllability to the IS model. Extensive experiments on intricate scenarios at object and part levels demonstrate that our GraCo has significant advantages over previous methods. This highlights the potential of GraCo to be a flexible annotation tool capable of adapting to diverse segmentation scenarios. The project page: https://zhao-yian.github.io/GraCo.
We propose a simple strategy for masking image patches during visual-language contrastive learning that improves the quality of the learned representations and the training speed. During each iteration of training we randomly mask clusters of visually similar image patches as measured by their raw pixel intensities. This provides an extra learning signal beyond the contrastive training itself since it forces a model to predict words for masked visual structures solely from context. It also speeds up training by reducing the amount of data used in each image. We evaluate the effectiveness of our model by pre-training on a number of benchmarks finding that it outperforms other masking strategies such as FLIP on the quality of the learned representation.
Given two images we can estimate the relative camera pose between them by establishing image-to-image correspondences. Usually correspondences are 2D-to-2D and the pose we estimate is defined only up to scale. Some applications aiming at instant augmented reality anywhere require scale-metric pose estimates and hence they rely on external depth estimators to recover the scale. We present MicKey a keypoint matching pipeline that is able to predict metric correspondences in 3D camera space. By learning to match 3D coordinates across images we are able to infer the metric relative pose without depth measurements. Depth measurements are also not required for training nor are scene reconstructions or image overlap information. MicKey is supervised only by pairs of images and their relative poses. MicKey achieves state-of-the-art performance on the Map-Free Relocalisation benchmark while requiring less supervision than competing approaches.
Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors
PDF ↗This paper introduces a novel human pose estimation approach using sparse inertial sensors addressing the shortcomings of previous methods reliant on synthetic data. It leverages a diverse array of real inertial motion capture data from different skeleton formats to improve motion diversity and model generalization. This method features two innovative components: a pseudo-velocity regression model for dynamic motion capture with inertial sensors and a part-based model dividing the body and sensor data into three regions each focusing on their unique characteristics. The approach demonstrates superior performance over state-of-the-art models across five public datasets notably reducing pose error by 19% on the DIP-IMU dataset thus representing a significant improvement in inertial sensor-based human pose estimation. Our codes are available at https://github.com/dx118/dynaip
While existing backdoor attacks have successfully infected multimodal contrastive learning models such as CLIP they can be easily countered by specialized backdoor defenses for MCL models. This paper reveals the threats in this practical scenario and introduces the BadCLIP attack which is resistant to backdoor detection and model fine-tuning defenses. To achieve this we draw motivations from the perspective of the Bayesian rule and propose a dual-embedding guided framework for backdoor attacks. Specifically we ensure that visual trigger patterns approximate the textual target semantics in the embedding space making it challenging to detect the subtle parameter variations induced by backdoor learning on such natural trigger patterns. Additionally we optimize the visual trigger patterns to align the poisoned samples with target vision features in order to hinder backdoor unlearning through clean fine-tuning. Our experiments show a significant improvement in attack success rate (+45.3 % ASR) over current leading methods even against state-of-the-art backdoor defenses highlighting our attack's effectiveness in various scenarios including downstream tasks. Our codes can be found at https://github.com/LiangSiyuan21/BadCLIP.
Characteristics Matching Based Hash Codes Generation for Efficient Fine-grained Image Retrieval
PDF ↗The rapidly growing scale of data in practice poses demands on the efficiency of retrieval models. However for fine-grained image retrieval task there are inherent contradictions in the design of hashing based efficient models. Firstly the limited information embedding capacity of low-dimensional binary hash codes coupled with the detailed information required to describe fine-grained categories results in a contradiction in feature learning. Secondly there is also a contradiction between the complexity of fine-grained feature extraction models and retrieval efficiency. To address these issues in this paper we propose the characteristics matching based hash codes generation method. Coupled with the cross-layer semantic information transfer module and the multi-region feature embedding module the proposed method can generate hash codes that effectively capture fine-grained differences among samples while ensuring efficient inference. Extensive experiments on widely used datasets demonstrate that our method can significantly outperform state-of-the-art methods.
Generating Audio Description (AD) for movies is a challenging task that requires fine-grained visual understanding and an awareness of the characters and their names. Currently visual language models for AD generation are limited by a lack of suitable training data and also their evaluation is hampered by using performance measures not specialized to the AD domain. In this paper we make three contributions: (i) We propose two approaches for constructing AD datasets with aligned video data and build training and evaluation datasets using these. These datasets will be publicly released; (ii) We develop a Q-former-based architecture which ingests raw video and generates AD using frozen pre-trained visual encoders and large language models; and (iii) We provide new evaluation metrics to benchmark AD quality that are well matched to human performance. Taken together we improve the state of the art on AD generation.
We present M&M VTO-a mix and match virtual try-on method that takes as input multiple garment images text description for garment layout and an image of a person. An example input includes: an image of a shirt an image of a pair of pants "rolled sleeves shirt tucked in" and an image of a person. The output is a visualization of how those garments (in the desired layout) would look like on the given person. Key contributions of our method are: 1) a single stage diffusion based model with no super resolution cascading that allows to mix and match multiple garments at 1024x512 resolution preserving and warping intricate garment details 2) architecture design (VTO UNet Diffusion Transformer) to disentangle denoising from person specific features allowing for a highly effective finetuning strategy for identity preservation (6MB model per individual vs 4GB achieved with e.g. dreambooth finetuning); solving a common identity loss problem in current virtual try-on methods 3) layout control for multiple garments via text inputs finetuned over PaLI-3 for virtual try-on task. Experimental results indicate that M&M VTO achieves state-of-the-art performance both qualitatively and quantitatively as well as opens up new opportunities for virtual try-on via language-guided and multi-garment try-on.
Most image-to-image translation models postulate that a unique correspondence exists between the semantic classes of the source and target domains. However this assumption does not always hold in real-world scenarios due to divergent distributions different class sets and asymmetrical information representation. As conventional GANs attempt to generate images that match the distribution of the target domain they may hallucinate spurious instances of classes absent from the source domain thereby diminishing the usefulness and reliability of translated images. CycleGAN-based methods are also known to hide the mismatched information in the generated images to bypass cycle consistency objectives a process known as steganography. In response to the challenge of non-bijective image translation we introduce StegoGAN a novel model that leverages steganography to prevent spurious features in generated images. Our approach enhances the semantic consistency of the translated images without requiring additional postprocessing or supervision. Our experimental evaluations demonstrate that StegoGAN outperforms existing GAN-based models across various non-bijective image-to-image translation tasks both qualitatively and quantitatively. Our code and pretrained models are accessible at https://github.com/sian-wusidi/StegoGAN.
Despite the emergence of successful NeRF inpainting methods built upon explicit RGB and depth 2D inpainting supervisions these methods are inherently constrained by the capabilities of their underlying 2D inpainters. This is due to two key reasons: (i) independently inpainting constituent images results in view-inconsistent imagery and (ii) 2D inpainters struggle to ensure high-quality geometry completion and alignment with inpainted RGB images. To overcome these limitations we propose a novel approach called MVIP-NeRF that harnesses the potential of diffusion priors for NeRF inpainting addressing both appearance and geometry aspects. MVIP-NeRF performs joint inpainting across multiple views to reach a consistent solution which is achieved via an iterative optimization process based on Score Distillation Sampling (SDS). Apart from recovering the rendered RGB images we also extract normal maps as a geometric representation and define a normal SDS loss that motivates accurate geometry inpainting and alignment with the appearance. Additionally we formulate a multi-view SDS score function to distill generative priors simultaneously from different view images ensuring consistent visual completion when dealing with large view variations. Our experimental results show better appearance and geometry recovery than previous NeRF inpainting methods.
While neural rendering has led to impressive advances in scene reconstruction and novel view synthesis it relies heavily on accurately pre-computed camera poses. To relax this constraint multiple efforts have been made to train Neural Radiance Fields (NeRFs) without pre-processed camera poses. However the implicit representations of NeRFs provide extra challenges to optimize the 3D structure and camera poses at the same time. On the other hand the recently proposed 3D Gaussian Splatting provides new opportunities given its explicit point cloud representations. This paper leverages both the explicit geometric representation and the continuity of the input video stream to perform novel view synthesis without any SfM preprocessing. We process the input frames in a sequential manner and progressively grow the 3D Gaussians set by taking one input frame at a time without the need to pre-compute the camera poses. Our method significantly improves over previous approaches in view synthesis and camera pose estimation under large motion changes. Our project page is: https: //oasisyang.github.io/colmap-free-3dgs.
Recently deep unfolding methods have achieved remarkable success in the realm of Snapshot Compressive Imaging (SCI) reconstruction. However the existing methods all follow the iterative framework of a single image prior which limits the efficiency of the unfolding methods and makes it a problem to use other priors simply and effectively. To break out of the box we derive an effective Dual Prior Unfolding (DPU) which achieves the joint utilization of multiple deep priors and greatly improves iteration efficiency. Our unfolding method is implemented through two parts i.e. Dual Prior Framework (DPF) and Focused Attention (FA). In brief in addition to the normal image prior DPF introduces a residual into the iteration formula and constructs a degraded prior for the residual by considering various degradations to establish the unfolding framework. To improve the effectiveness of the image prior based on self-attention FA adopts a novel mechanism inspired by PCA denoising to scale and filter attention which lets the attention focus more on effective features with little computation cost. Besides an asymmetric backbone is proposed to further improve the efficiency of hierarchical self-attention. Remarkably our 5-stage DPU achieves state-of-the-art (SOTA) performance with the least FLOPs and parameters compared to previous methods while our 9-stage DPU significantly outperforms other unfolding methods with less computational requirement.