CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Creating high-fidelity 3D head avatars has always been a research hotspot but there remains a great challenge under lightweight sparse view setups. In this paper we propose Gaussian Head Avatar represented by controllable 3D Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions. The two parts benefit each other thereby our method can model fine-grained dynamic details while ensuring expression accuracy. Furthermore we devise a well-designed geometry-guided initialization strategy based on implicit SDF and Deep Marching Tetrahedra for the stability and convergence of the training procedure. Experiments show our approach outperforms other state-of-the-art sparse-view methods achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions. Project page: https://yuelangx.github.io/gaussianheadavatar.
Estimating 3D full-body avatars from AR/VR devices is essential for creating immersive experiences in AR/VR applications. This task is challenging due to the limited input from Head Mounted Devices which capture only sparse observations from the head and hands. Predicting the full-body avatars particularly the lower body from these sparse observations presents significant difficulties. In this paper we are inspired by the inherent property of the kinematic tree defined in the Skinned Multi-Person Linear (SMPL) model where the upper body and lower body share only one common ancestor node bringing the potential of decoupled reconstruction. We propose a stratified approach to decouple the conventional full-body avatar reconstruction pipeline into two stages with the reconstruction of the upper body first and a subsequent reconstruction of the lower body conditioned on the previous stage. To implement this straightforward idea we leverage the latent diffusion model as a powerful probabilistic generator and train it to follow the latent distribution of decoupled motions explored by a VQ-VAE encoder-decoder model. Extensive experiments on AMASS mocap dataset demonstrate our state-of-the-art performance in the reconstruction of full-body motions.
Egocentric videos provide a first-person perspective of the wearer's activities involving simultaneous interactions with multiple objects. In this work we propose the task of weakly-supervised Narration-based Video Object Segmentation (NVOS). Given an egocentric video clip and a narration of the wearer's activities our aim is to segment object instances mentioned in the narration without using any spatial annotations during training. Existing weakly-supervised video object grounding methods typically yield bounding boxes for referred objects. In contrast we propose ROSA a weakly-supervised pixel-level grounding framework learning alignments between referred objects and segmentation mask proposals. Our model harnesses vision-language models pre-trained on image-text pairs to embed region masks and object phrases. During training we combine (a) a video-narration contrastive loss that implicitly supervises the alignment between regions and phrases and (b) a region-phrase contrastive loss based on inferred latent alignments. To address the lack of annotated NVOS datasets in egocentric videos we create a new evaluation benchmark VISOR-NVOS leveraging existing annotations of segmentation masks from VISOR alongside 14.6k newly-collected object-based video clip narrations.
To address the lack of annotated NVOS datasets in egocentric videos we create a new evaluation benchmark VISOR-NVOS leveraging existing annotations of segmentation masks from VISOR alongside 14.6k newly-collected object-based video clip narrations. Our approach achieves state-of-the-art zero-shot pixel-level grounding performance compared to strong baselines under similar supervision. Additionally we demonstrate generalization capabilities for zero-shot video object grounding on YouCook2 a third-person instructional video dataset.
Recent studies have drawn attention to the untapped potential of the "star operation" (element-wise multiplication) in network design. While intuitive explanations abound the foundational rationale behind its application remains largely unexplored. Our study attempts to reveal the star operation's ability of mapping inputs into high-dimensional non-linear feature spaces--akin to kernel tricks--without widening the network. We further introduce StarNet a simple yet powerful prototype demonstrating impressive performance and low latency under compact network structure and efficient budget. Like stars in the sky the star operation appears unremarkable but holds a vast universe of potential. Our work encourages further exploration across tasks with codes available at https://github.com/ma-xu/Rewrite-the-Stars.
Recent advancements in large-scale visual-language pre-trained models have led to significant progress in zero-/few-shot anomaly detection within natural image domains. However the substantial domain divergence between natural and medical images limits the effectiveness of these methodologies in medical anomaly detection. This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder enabling a stepwise enhancement of visual features across different levels. This multi-level adaptation is guided by multi-level pixel-wise visual-language feature alignment loss functions which recalibrate the model's focus from object semantics in natural imagery to anomaly identification in medical images. The adapted features exhibit improved generalization across various medical data types even in zero-shot scenarios where the model encounters unseen medical modalities and anatomical regions during training. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models with an average AUC improvement of 6.24% and 7.33% for anomaly classification 2.03% and 2.37% for anomaly segmentation under the zero-shot and few-shot settings respectively. Source code is available at: https://github.com/MediaBrain-SJTU/MVFA-AD
Accurate estimation of Room Impulse Response (RIR) which captures an environment's acoustic properties is important for speech processing and AR/VR applications. We propose AV-RIR a novel multi-modal multi-task learning approach to accurately estimate the RIR from a given reverberant speech signal and the visual cues of its corresponding environment. AV-RIR builds on a novel neural codec-based architecture that effectively captures environment geometry and materials properties and solves speech dereverberation as an auxiliary task by using multi-task learning. We also propose Geo-Mat features that augment material information into visual cues and CRIP that improves late reverberation components in the estimated RIR via image-to-RIR retrieval by 86%. Empirical results show that AV-RIR quantitatively outperforms previous audio-only and visual-only approaches by achieving 36% - 63% improvement across various acoustic metrics in RIR estimation. Additionally it also achieves higher preference scores in human evaluation. As an auxiliary benefit dereverbed speech from AV-RIR shows competitive performance with the state-of-the-art in various spoken language processing tasks and outperforms reverberation time error score in the real-world AVSpeech dataset.
Additionally it also achieves higher preference scores in human evaluation. As an auxiliary benefit dereverbed speech from AV-RIR shows competitive performance with the state-of-the-art in various spoken language processing tasks and outperforms reverberation time error score in the real-world AVSpeech dataset. Qualitative examples of both synthesized reverberant speech and enhanced speech are available online https://www.youtube.com/watch?v=tTsKhviukAE.
Zero-shot Video Object Segmentation (ZSVOS) aims at segmenting the primary moving object without any human annotations. Mainstream solutions mainly focus on learning a single model on large-scale video datasets which struggle to generalize to unseen videos. In this work we introduce a test-time training (TTT) strategy to address the problem. Our key insight is to enforce the model to predict consistent depth during the TTT process. In detail we first train a single network to perform both segmentation and depth prediction tasks. This can be effectively learned with our specifically designed depth modulation layer. Then for the TTT process the model is updated by predicting consistent depth maps for the same frame under different data augmentations. In addition we explore different TTT weight update strategies. Our empirical results suggest that the momentum-based weight initialization and looping-based training scheme lead to more stable improvements. Experiments show that the proposed method achieves clear improvements on ZSVOS. Our proposed video TTT strategy provides significant superiority over state-of-the-art TTT methods. Our code is available at: https://nifangbaage.github.io/DATTT/.
Non-exemplar class incremental learning (NECIL) aims to continuously assimilate new knowledge without forgetting previously acquired ones when historical data are unavailable. One of the generative NECIL methods is to invert the images of old classes for joint training. However these synthetic images suffer significant domain shifts compared with real data hampering the recognition of old classes. In this paper we present a novel method termed Dual-Consistency Model Inversion (DCMI) to generate better synthetic samples of old classes through two pivotal consistency alignments: (1) the semantic consistency between the synthetic images and the corresponding prototypes and (2) domain consistency between synthetic and real images of new classes. Besides we introduce Prototypical Routing (PR) to provide task-prior information and generate unbiased and accurate predictions. Our comprehensive experiments across diverse datasets consistently showcase the superiority of our method over previous state-of-the-art approaches.
With recent video object segmentation (VOS) benchmarks evolving to challenging scenarios we revisit a simple but overlooked strategy: restricting the size of memory banks. This diverges from the prevalent practice of expanding memory banks to accommodate extensive historical information. Our specially designed "memory deciphering" study offers a pivotal insight underpinning such a strategy: expanding memory banks while seemingly beneficial actually increases the difficulty for VOS modules to decode relevant features due to the confusion from redundant information. By restricting memory banks to a limited number of essential frames we achieve a notable improvement in VOS accuracy. This process balances the importance and freshness of frames to maintain an informative memory bank within a bounded capacity. Additionally restricted memory banks reduce the training-inference discrepancy in memory lengths compared with continuous expansion. This fosters new opportunities in temporal reasoning and enables us to introduce the previously overlooked "temporal positional embedding." Finally our insights are embodied in "RMem" ("R" for restricted) a simple yet effective VOS modification that excels at challenging VOS scenarios and establishes new state of the art for object state changes (VOST dataset) and long videos (the Long Videos dataset).
Finally our insights are embodied in "RMem" ("R" for restricted) a simple yet effective VOS modification that excels at challenging VOS scenarios and establishes new state of the art for object state changes (VOST dataset) and long videos (the Long Videos dataset). Our codes are available at https://github.com/Restricted-Memory/RMemand our demo can be watched on https://youtu.be/V3tCFQsJrrM.
Given the power of vision transformers a new learning paradigm pre-training and then prompting makes it more efficient and effective to address downstream visual recognition tasks. In this paper we identify a novel security threat towards such a paradigm from the perspective of backdoor attacks. Specifically an extra prompt token called the switch token in this work can turn the backdoor mode on i.e. converting a benign model into a backdoored one. Once under the backdoor mode a specific trigger can force the model to predict a target class. It poses a severe risk to the users of cloud API since the malicious behavior can not be activated and detected under the benign mode thus making the attack very stealthy. To attack a pre-trained model our proposed attack named SWARM learns a trigger and prompt tokens including a switch token. They are optimized with the clean loss which encourages the model always behaves normally even the trigger presents and the backdoor loss that ensures the backdoor can be activated by the trigger when the switch is on. Besides we utilize the cross-mode feature distillation to reduce the effect of the switch token on clean samples. The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack i.e.
Besides we utilize the cross-mode feature distillation to reduce the effect of the switch token on clean samples. The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack i.e. achieving 95%+ attack success rate and also being hard to be detected and removed. Our code is available at https://github.com/20000yshust/SWARM.
Image and video analysis requires not only accurate object but also the understanding of relationships among detected objects. Common solutions to relation modeling typically resort to stand-alone object detectors followed by non-differentiable post-processing techniques. Recently introduced detection transformers (DETR) perform end-to-end object detection based on a bipartite matching loss. Such methods however lack the ability to jointly detect objects and resolve object associations. In this paper we build on the DETR approach and extend it to the joint detection of objects and their relationships by introducing an approximated bipartite matching. While our method can generalize to an arbitrary number of objects we here focus on the modeling of object pairs and their relations. In particular we apply our method PairDETR to the problem of detecting human bodies and faces and associating them for the same person. Our approach not only eliminates the need for hand-designed post-processing but also achieves excellent results for body-face associations. We evaluate PairDETR on the challenging CrowdHuman and CityPersons datasets and demonstrate a large improvement over the state of the art. Our training code and pre-trained models are available online.
Recent advancements in personalized image generation using diffusion models have been noteworthy. However existing methods suffer from inefficiencies due to the requirement for subject-specific fine-tuning. This computationally intensive process hinders efficient deployment limiting practical usability. Moreover these methods often grapple with identity distortion and limited expression diversity. In light of these challenges we propose PortraitBooth an innovative approach designed for high efficiency robust identity preservation and expression-editable text-to-image generation without the need for fine-tuning. PortraitBooth leverages subject embeddings from a face recognition model for personalized image generation without fine-tuning. It eliminates computational overhead and mitigates identity distortion. The introduced dynamic identity preservation strategy further ensures close resemblance to the original image identity. Moreover PortraitBooth incorporates emotion-aware cross-attention control for diverse facial expressions in generated images supporting text-driven expression editing. Its scalability enables efficient and high-quality image creation including multi-subject generation. Extensive results demonstrate superior performance over other state-of-the-art methods in both single and multiple image generation scenarios.
In recent years anchor-based methods have achieved promising progress in multi-view clustering. The performances of these methods are significantly affected by the quality of the anchors. However the anchors generated by previous works solely rely on single-view information ignoring the correlation among different views. In particular we observe that similar patterns are more likely to exist between similar views so such correlation information can be leveraged to enhance the quality of the anchors which is also omitted. To this end we propose a novel plug-and-play anchor enhancement strategy through view correlation for multi-view clustering. Specifically we construct a view graph based on aligned initial anchor graphs to explore inter-view correlations. By learning from view correlation we enhance the anchors of the current view using the relationships between anchors and samples on neighboring views thereby narrowing the spatial distribution of anchors on similar views. Experimental results on seven datasets demonstrate the superiority of our proposed method over other existing methods. Furthermore extensive comparative experiments validate the effectiveness of the proposed anchor enhancement module when applied to various anchor-based methods.
Human-centric video frame interpolation has great potential for enhancing entertainment experiences and finding commercial applications in sports analysis industry e.g. synthesizing slow-motion videos. Although there are multiple benchmark datasets available for video frame interpolation in the community none of them is dedicated to human-centric scenarios. To bridge this gap we introduce SportsSloMo a benchmark featuring over 130K high-resolution slow-motion sports video clips totaling over 1M video frames sourced from YouTube. We re-train several state-of-the-art methods on our benchmark and we observed a noticeable decrease in their accuracy compared to other datasets. This highlights the difficulty of our benchmark and suggests that it poses significant challenges even for the best-performing methods as human bodies are highly deformable and occlusions are frequent in sports videos. To tackle these challenges we propose human-aware loss terms where we add auxiliary supervision for human segmentation in panoptic settings and keypoints detection. These loss terms are model-agnostic and can be easily plugged into any video frame interpolation approach. Experimental results validate the effectiveness of our proposed human-aware loss terms leading to consistent performance improvement over existing models. The dataset and code can be found at: https://neu-vi.github.io/SportsSlomo/  https://neu-vi.github.io/SportsSlomo/.
Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains and their performances degrade significantly while applied to a distinct domain. To this end we propose to leverage the cutting-edge foundation model the Segment Anything Model (SAM) for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data which primarily comprise natural scene images and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work we introduce APSeg a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS) which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes allowing features to be transformed into a more stable domain-agnostic space. Additionally a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning.
Additionally a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings respectively.
This paper introduces the first text-guided work for generating the sequence of hand-object interaction in 3D. The main challenge arises from the lack of labeled data where existing ground-truth datasets are nowhere near generalizable in interaction type and object category which inhibits the modeling of diverse 3D hand-object interaction with the correct physical implication (e.g. contacts and semantics) from text prompts. To address this challenge we propose to decompose the interaction generation task into two subtasks: hand-object contact generation; and hand-object motion generation. For contact generation a VAE-based network takes as input a text and an object mesh and generates the probability of contacts between the surfaces of hands and the object during the interaction. The network learns a variety of local geometry structure of diverse objects that is independent of the objects' category and thus it is applicable to general objects. For motion generation a Transformer-based diffusion model utilizes this 3D contact map as a strong prior for generating physically plausible hand-object motion as a function of text prompts by learning from the augmented labeled dataset; where we annotate text labels from many existing 3D hand and object motion data.
For motion generation a Transformer-based diffusion model utilizes this 3D contact map as a strong prior for generating physically plausible hand-object motion as a function of text prompts by learning from the augmented labeled dataset; where we annotate text labels from many existing 3D hand and object motion data. Finally we further introduce a hand refiner module that minimizes the distance between the object surface and hand joints to improve the temporal stability of the object-hand contacts and to suppress the penetration artifacts. In the experiments we demonstrate that our method can generate more realistic and diverse interactions compared to other baseline methods. We also show that our method is applicable to unseen objects. We will release our model and newly labeled data as a strong foundation for future research. Codes and data are available in: https://github.com/JunukCha/Text2HOI.
Deployment of Transformer models on edge devices is becoming increasingly challenging due to the exponentially growing inference cost that scales quadratically with the number of tokens in the input sequence. Token pruning is an emerging solution to address this challenge due to its ease of deployment on various Transformer backbones. However most token pruning methods require computationally expensive fine-tuning which is undesirable in many edge deployment cases. In this work we propose Zero-TPrune the first zero-shot method that considers both the importance and similarity of tokens in performing token pruning. It leverages the attention graph of pre-trained Transformer models to produce an importance distribution for tokens via our proposed Weighted Page Rank (WPR) algorithm. This distribution further guides token partitioning for efficient similarity-based pruning. Due to the elimination of the fine-tuning overhead Zero-TPrune can prune large models at negligible computational cost switch between different pruning configurations at no computational cost and perform hyperparameter tuning efficiently. We evaluate the performance of Zero-TPrune on vision tasks by applying it to various vision Transformer backbones and testing them on ImageNet.