CVPR 2024 论文列表

This work presents AnyDoor a diffusion-based image generator with the power to teleport target objects to new scenes at user-specified locations with desired shapes. Instead of tuning parameters for each object our model is trained only once and effortlessly generalizes to diverse object-scene combinations at the inference stage. Such a challenging zero-shot setting requires an adequate characterization of a certain object. To this end we complement the commonly used identity feature with detail features which are carefully designed to maintain appearance details yet allow versatile local variations(e.g. lighting orientation posture etc.) supporting the object in favorably blending with different surroundings. We further propose to borrow knowledge from video datasets where we can observe various forms (i.e. along the time axis) of a single object leading to stronger model generalizability and robustness. Extensive experiments demonstrate the superiority of our approach over existing alternatives as well as its great potential in real-world applications such as virtual try-on shape editing and object swapping.

Image-language models with prompt learning have shown remarkable advances in numerous downstream vision tasks. Nevertheless conventional prompt learning methods overfit the training distribution and lose the generalization ability on the test distributions. To improve the generalization across various distribution shifts we propose any-shift prompting: a general probabilistic inference framework that considers the relationship between training and test distributions during prompt learning. We explicitly connect training and test distributions in the latent space by constructing training and test prompts in a hierarchical architecture. Within this framework the test prompt exploits the distribution relationships to guide the generalization of the CLIP image-language model from training to any test distribution. To effectively encode the distribution information and their relationships we further introduce a transformer inference network with a pseudo-shift training mechanism. The network generates the tailored test prompt with both training and test information in a feedforward pass avoiding extra training costs at test time. Extensive experiments on twenty-three datasets demonstrate the effectiveness of any-shift prompting on the generalization over various distribution shifts.

With the advancement of generative models the assessment of generated images becomes increasingly more important. Previous methods measure distances between features of reference and generated images from trained vision models. In this paper we conduct an extensive investigation into the relationship between the representation space and input space around generated images. We first propose two measures related to the presence of unnatural elements within images: complexity which indicates how non-linear the representation space is and vulnerability which is related to how easily the extracted feature changes by adversarial input changes. Based on these we introduce a new metric to evaluating image-generative models called anomaly score (AS). Moreover we propose AS-i (anomaly score for individual images) that can effectively evaluate generated images individually. Experimental results demonstrate the validity of the proposed approach.

Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e. samples from open-set anomaly classes) while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies current OSAD methods can often largely reduce false positive errors. However these methods are trained in a closed-set setting and treat the anomaly examples as from a homogeneous distribution rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. This paper proposes to learn heterogeneous anomaly distributions using the limited anomaly examples to address this issue. To this end we introduce a novel approach namely Anomaly Heterogeneity Learning (AHL) that simulates a diverse set of heterogeneous anomaly distributions and then utilizes them to learn a unified heterogeneous abnormality model in surrogate open-set environments. Further AHL is a generic framework that existing OSAD models can plug and play for enhancing their abnormality modeling. Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art OSAD models in detecting seen and unseen anomalies and 2) effectively generalize to unseen anomalies in new domains. Code is available at https://github.com/mala-lab/AHL.

We present the pioneering Large Visual Motion Model (LVMM) meticulously engineered to analyze the intrinsic dynamics encapsulated within real-world imagery. Our model fortified with a wealth of prior knowledge extracted from billions of image pairs demonstrates promising results in predicting a diverse spectrum of scene dynamics. As a result it can infuse any generic image with authentic dynamic effects enhancing its visual allure.

Character Animation aims to generating character videos from still images through driving signals. Currently diffusion models have become the mainstream in visual generation research owing to their robust generative capabilities. However challenges persist in the realm of image-to-video especially in character animation where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data our approach can animate arbitrary characters yielding superior results in character animation compared to other image-to-video methods. Furthermore we evaluate our method on image animation benchmarks achieving state-of-the-art results.

Modeling animatable human avatars from RGB videos is a long-standing and challenging problem. Recent works usually adopt MLP-based neural radiance fields (NeRF) to represent 3D humans but it remains difficult for pure MLPs to regress pose-dependent garment details. To this end we introduce Animatable Gaussians a new avatar representation that leverages powerful 2D CNNs and 3D Gaussian splatting to create high-fidelity avatars. To associate 3D Gaussians with the animatable avatar we learn a parametric template from the input videos and then parameterize the template on two front & back canonical Gaussian maps where each pixel represents a 3D Gaussian. The learned template is adaptive to the wearing garments for modeling looser clothes like dresses. Such template-guided 2D parameterization enables us to employ a powerful StyleGAN-based CNN to learn the pose-dependent Gaussian maps for modeling detailed dynamic appearances. Furthermore we introduce a pose projection strategy for better generalization given novel poses. Overall our method can create lifelike avatars with dynamic realistic and generalized appearances. Experiments show that our method outperforms other state-of-the-art approaches. Code: https://github.com/lizhe00/AnimatableGaussians.

We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization i.e. i) domain shift such as natural to sketch images and ii) zero-shot capability to recognize the category that was not contained in the finetune data. Arguably the diminished OOD generalization after finetuning stems from the excessively simplified finetuning target which only provides the class information such as "a photo of a [CLASS]". This is distinct from the process in that CLIP was pretrained where there is abundant text supervision with rich semantic information. Therefore we propose to compensate for the finetune process using auxiliary supervision with rich semantic information which acts as anchors to preserve the OOD generalization. Specifically two types of anchors are elaborated in our methods including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner ii) image-text-pair anchor which is retrieved from the dataset similar to pretraining data of CLIP according to the downstream task associating with the original CLIP text with rich semantics. Those anchors are utilized as auxiliary semantic information to maintain the original feature space of CLIP thereby preserving the OOD generalization capabilities. Comprehensive experiments demonstrate that our method achieves in-distribution performance akin to conventional finetuning while attaining new state-of-the-art results on domain shift and zero-shot learning benchmarks.

Coordinate based implicit neural representations have gained rapid popularity in recent years as they have been successfully used in image geometry and scene modeling tasks. In this work we present a novel use case for such implicit representations in the context of learning anatomically constrained face models. Actor specific anatomically constrained face models are the state of the art in both facial performance capture and performance retargeting. Despite their practical success these anatomical models are slow to evaluate and often require extensive data capture to be built. We propose the anatomical implicit face model; an ensemble of implicit neural networks that jointly learn to model the facial anatomy and the skin surface with high-fidelity and can readily be used as a drop in replacement to conventional blendshape models. Given an arbitrary set of skin surface meshes of an actor and only a neutral shape with estimated skull and jaw bones our method can recover a dense anatomical substructure which constrains every point on the facial surface. We demonstrate the usefulness of our approach in several tasks ranging from shape fitting shape editing and performance retargeting.

Diffusion models currently dominate the field of data-driven image synthesis with their unparalleled scaling to large datasets. In this paper we identify and rectify several causes for uneven and ineffective training in the popular ADM diffusion model architecture without altering its high-level structure. Observing uncontrolled magnitude changes and imbalances in both the network activations and weights over the course of training we redesign the network layers to preserve activation weight and update magnitudes on expectation. We find that systematic application of this philosophy eliminates the observed drifts and imbalances resulting in considerably better networks at equal computational complexity. Our modifications improve the previous record FID of 2.41 in ImageNet-512 synthesis to 1.81 achieved using fast deterministic sampling. As an independent contribution we present a method for setting the exponential moving average (EMA) parameters post-hoc i.e. after completing the training run. This allows precise tuning of EMA length without the cost of performing several training runs and reveals its surprising interactions with network architecture training time and guidance.

Heterogeneous Federated Learning (HtFL) enables collaborative learning on multiple clients with different model architectures while preserving privacy. Despite recent research progress knowledge sharing in HtFL is still difficult due to data and model heterogeneity. To tackle this issue we leverage the knowledge stored in pre-trained generators and propose a new upload-efficient knowledge transfer scheme called Federated Knowledge-Transfer Loop (FedKTL). Our FedKTL can produce client-task-related prototypical image-vector pairs via the generator's inference on the server. With these pairs each client can transfer pre-existing knowledge from the generator to its local model through an additional supervised local task. We conduct extensive experiments on four datasets under two types of data heterogeneity with 14 kinds of models including CNNs and ViTs. Results show that our upload-efficient FedKTL surpasses seven state-of-the-art methods by up to 7.31% in accuracy. Moreover our knowledge transfer scheme is applicable in scenarios with only one edge client. Code: https://github.com/TsingZ0/FedKTL

Event cameras respond primarily to edges---formed by strong gradients---and are thus particularly well-suited for line-based motion estimation. Recent work has shown that events generated by a single line each satisfy a polynomial constraint which describes a manifold in the space-time volume. Multiple such constraints can be solved simultaneously to recover the partial linear velocity and line parameters. In this work we show that with a suitable line parametrization this system of constraints is actually linear in the unknowns which allows us to design a novel linear solver. Unlike existing solvers our linear solver (i) is fast and numerically stable since it does not rely on expensive root finding (ii) can solve both minimal and overdetermined systems with more than 5 events and (iii) admits the characterization of all degenerate cases and multiple solutions. The found line parameters are singularity-free and have a fixed scale which eliminates the need for auxiliary constraints typically encountered in previous work. To recover the full linear camera velocity we fuse observations from multiple lines with a novel velocity averaging scheme that relies on a geometrically-motivated residual and thus solves the problem more efficiently than previous schemes which minimize an algebraic residual. Extensive experiments in synthetic and real-world settings demonstrate that our method surpasses the previous work in numerical stability and operates over 600 times faster.

Visual navigation is to let the agent reach the target according to the continuous visual input. In most previous works visual navigation is usually assumed to be done in a static and ideal environment: the target is always reachable with no need to alter the environment. However the "messy" environments are more general and practical in our daily lives where the agent may get blocked by obstacles. Thus Interactive Navigation (InterNav) is introduced to navigate to the objects in more realistic "messy" environments according to the object interaction. Prior work on InterNav learns short-term interaction through extensive trials with reinforcement learning. However interaction does not guarantee efficient navigation that is planning obstacle interactions that make shorter paths and consume less effort is also crucial. In this paper we introduce an effect-oriented affordance map to enable long-term interactive navigation extending the existing map-based navigation framework to the domain of dynamic environment. We train a set of affordance functions predicting available interactions and the time cost of removing obstacles which informatively support an interactive modular system to address interaction and long-term planning. Experiments on the ProcTHOR simulator demonstrate the capability of our affordance-driven system in long-term navigation in complex dynamic environments.

3D Object Detectors (3D-OD) are crucial for understanding the environment in many robotic tasks especially autonomous driving. Including 3D information via Lidar sensors improves accuracy greatly. However such detectors perform poorly on domains they were not trained on i.e. different locations sensors weather etc. limiting their reliability in safety-critical applications. There exist methods to adapt 3D-ODs to these domains; however these methods treat 3D-ODs as a black box neglecting underlying architectural decisions and source-domain training strategies. Instead we dive deep into the details of 3D-ODs focusing our efforts on fundamental factors that influence robustness prior to domain adaptation. We systematically investigate four design choices (and the interplay between them) often overlooked in 3D-OD robustness and domain adaptation: architecture voxel encoding data augmentations and anchor strategies. We assess their impact on the robustness of nine state-of-the-art 3D-ODs across six benchmarks encompassing three types of domain gaps - sensor type weather and location. Our main findings are: (1) transformer backbones with local point features are more robust than 3D CNNs (2) test-time anchor size adjustment is crucial for adaptation across geographical locations significantly boosting scores without retraining (3) source-domain augmentations allow the model to generalize to low-resolution sensors and (4) surprisingly robustness to bad weather is improved when training directly on more clean weather data than on training with bad weather data. We outline our main conclusions and findings to provide practical guidance on developing more robust 3D-ODs.

The laws of model size data volume computation and model performance have been extensively studied in the field of Natural Language Processing (NLP). However the scaling laws in Scene Text Recognition (STR) have not yet been investigated. To address this we conducted comprehensive studies that involved examining the correlations between performance and the scale of models data volume and computation in the field of text recognition. Conclusively the study demonstrates smooth power laws between performance and model size as well as training data volume when other influencing factors are held constant. Additionally we have constructed a large-scale dataset called REBU-Syn which comprises 6 million real samples and 18 million synthetic samples. Based on our scaling law and new dataset we have successfully trained a scene text recognition model achieving a new state-of-the-art on 6 common test benchmarks with a top-1 average accuracy of 97.42%. The models and dataset are publicly available at \href https://github.com/large-ocr-model/large-ocr-model.github.io large-ocr-model.github.io .

Denoising diffusion probabilistic models (DDPMs) employ a sequence of white Gaussian noise samples to generate an image. In analogy with GANs those noise maps could be considered as the latent code associated with the generated image. However this native noise space does not possess a convenient structure and is thus challenging to work with in editing tasks. Here we propose an alternative latent noise space for DDPM that enables a wide range of editing operations via simple means and present an inversion method for extracting these edit-friendly noise maps for any given image (real or synthetically generated). As opposed to the native DDPM noise space the edit-friendly noise maps do not have a standard normal distribution and are not statistically independent across timesteps. However they allow perfect reconstruction of any desired image and simple transformations on them translate into meaningful manipulations of the output image (e.g. shifting color edits). Moreover in text-conditional models fixing those noise maps while changing the text prompt modifies semantics while retaining structure. We illustrate how this property enables text-based editing of real images via the diverse DDPM sampling scheme (in contrast to the popular non-diverse DDIM inversion). We also show how it can be used within existing diffusion-based editing methods to improve their quality and diversity. The code of the method is attached to this submission.

Unsupervised fine-grained image hashing aims to learn compact binary hash codes in unsupervised settings addressing challenges posed by large-scale datasets and dependence on supervision. In this paper we first identify a granularity gap between generic and fine-grained datasets for unsupervised hashing methods highlighting the inadequacy of conventional self-supervised learning for fine-grained visual objects. To bridge this gap we propose the Asymmetric Augmented Self-Supervised Learning (A^2-SSL) method comprising three modules. The asymmetric augmented SSL module employs suitable augmentation strategies for positive/negative views preventing fine-grained category confusion inherent in conventional SSL. Part-oriented dense contrastive learning utilizes the Fisher Vector framework to capture and model fine-grained object parts enhancing unsupervised representations through part-level dense contrastive learning. Self-consistent hash code learning introduces a reconstruction task aligned with the self-consistency principle guiding the model to emphasize comprehensive features particularly fine-grained patterns. Experimental results on five benchmark datasets demonstrate the superiority of A^2-SSL over existing methods affirming its efficacy in unsupervised fine-grained image hashing.

The performance of Federated Learning (FL) hinges on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework where clients update local models based on a global model aggregated by the server from the previous training round. This process can cause client drift especially with significant cross-client data heterogeneity impacting model performance and convergence of the FL algorithm. To address these challenges we introduce FedAF a novel aggregation-free FL algorithm. In this framework clients collaboratively learn condensed data by leveraging peer knowledge the server subsequently trains the global model using the condensed data and soft labels received from the clients. FedAF inherently avoids the issue of client drift enhances the quality of condensed data amid notable data heterogeneity and improves the global model performance. Extensive numerical studies on several popular benchmark datasets show FedAF surpasses various state-of-the-art FL algorithms in handling label-skew and feature-skew data heterogeneity leading to superior global model accuracy and faster convergence.

This paper studies amodal image segmentation: predicting entire object segmentation masks including both visible and invisible (occluded) parts. In previous work the amodal segmentation ground truth on real images is usually predicted by manual annotaton and thus is subjective. In contrast we use 3D data to establish an automatic pipeline to determine authentic ground truth amodal masks for partially occluded objects in real images. This pipeline is used to construct an amodal completion evaluation benchmark MP3D-Amodal consisting of a variety of object categories and labels. To better handle the amodal completion task in the wild we explore two architecture variants: a two-stage model that first infers the occluder followed by amodal mask completion; and a one-stage model that exploits the representation power of Stable Diffusion for amodal segmentation across many categories. Without bells and whistles our method achieves a new state-of-the-art performance on Amodal segmentation datasets that cover a large variety of objects including COCOA and our new MP3D-Amodal dataset. The dataset model and code are available at https://www. robots.ox.ac.uk/ vgg/research/amodal/.