CVPR 2024 Papers

Domain Generalization (DG) aims to resolve distribution shifts between source and target domains and current DG methods are default to the setting that data from source and target domains share identical categories. Nevertheless there exists unseen classes from target domains in practical scenarios. To address this issue Open Set Domain Generalization (OSDG) has emerged and several methods have been exclusively proposed. However most existing methods adopt complex architectures with slight improvement compared with DG methods. Recently vision-language models (VLMs) have been introduced in DG following the fine-tuning paradigm but consume huge training overhead with large vision models. Therefore in this paper we innovate to transfer knowledge from VLMs to lightweight vision models and improve the robustness by introducing Perturbation Distillation (PD) from three perspectives including Score Class and Instance (SCI) named SCI-PD. Moreover previous methods are oriented by the benchmarks with identical and fixed splits ignoring the divergence between source domains. These methods are revealed to suffer from sharp performance decay with our proposed new benchmark Hybrid Domain Generalization (HDG) and a novel metric H^ 2 -CV which construct various splits to comprehensively assess the robustness of algorithms. Extensive experiments demonstrate that our method outperforms state-of-the-art algorithms on multiple datasets especially improving the robustness when confronting data scarcity.

Material appearance is a key component of photorealism with a pronounced impact on human perception. Although there are many prior works targeting at measuring opaque materials using light-weight setups (e.g. consumer-level cameras) little attention is paid on acquiring the optical properties of translucent materials which are also quite common in nature. In this paper we present a practical method for acquiring scattering properties of translucent materials based solely on ordinary images captured with unknown lighting and camera parameters. The key to our method is an inter-pixel translucency prior which states that image pixels of a given homogeneous translucent material typically form curves (dubbed translucent curves) in the RGB space of which the shapes are determined by the parameters of the material. We leverage this prior in a specially-designed convolutional neural network comprising multiple encoders a translucency-aware feature fusion module and a cascaded decoder. We demonstrate through both visual comparisons and quantitative evaluations that high accuracy can be achieved on a wide range of real-world translucent materials.

Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics treatment evaluation and clinical research. The complex kidney system comprises various components across multiple levels including regions (cortex medulla) functional units (glomeruli tubules) and cells (podocytes mesangial cells in glomerulus). Prior studies have predominantly overlooked the intricate spatial interrelations among objects from clinical knowledge. In this research we introduce a novel universal proposition learning approach called panoramic renal pathology segmentation (PrPSeg) designed to segment comprehensively panoramic structures within kidney by integrating extensive knowledge of kidney anatomy. In this paper we propose (1) the design of a comprehensive universal proposition matrix for renal pathology facilitating the incorporation of classification and spatial relationships into the segmentation process; (2) a token-based dynamic head single network architecture with the improvement of the partial label image segmentation and capability for future data enlargement; and (3) an anatomy loss function quantifying the inter-object relationships across the kidney.

Human Mesh Recovery (HMR) aims to estimate the 3D human body from 2D images which is a challenging task due to inherent ambiguities in translating 2D observations to 3D space. A novel approach called PostureHMR is proposed to leverage a multi-step diffusion-style process which converts this task into a posture transformation from an SMPL T-pose mesh to the target mesh. To inject the learning process of posture transformation with the physical structure of the human body model a kinematics-based forward process is proposed to interpolate the intermediate state with pose and shape decomposition. Moreover a mesh-to-posture (M2P) decoder is designed by combining the input of 3D and 2D mesh constraints estimated from the image to model the posture changes in the reverse process. It mitigates the difficulties of posture change learning directly from RGB pixels. To overcome the limitation of pixel-level misalignment of modeling results with the input image a new trimap-based rendering loss is designed to highlight the areas with poor recognition. Experiments conducted on three widely used datasets demonstrate that the proposed approach outperforms the state-of-the-art methods.

We introduce Posterior Distillation Sampling (PDS) a novel optimization method for parametric image editing based on diffusion models. Existing optimization-based methods which leverage the powerful 2D prior of diffusion models to handle various parametric images have mainly focused on generation. Unlike generation editing requires a balance between conforming to the target attribute and preserving the identity of the source content. Recent 2D image editing methods have achieved this balance by leveraging the stochastic latent encoded in the generative process of diffusion models. To extend the editing capabilities of diffusion models shown in pixel space to parameter space we reformulate the 2D image editing method into an optimization form named PDS. PDS matches the stochastic latents of the source and the target enabling the sampling of targets in diverse parameter spaces that align with a desired attribute while maintaining the source's identity. We demonstrate that this optimization resembles running a generative process with the target attribute but aligning this process with the trajectory of the source's generative process. Extensive editing results in Neural Radiance Fields and Scalable Vector Graphics representations demonstrate that PDS is capable of sampling targets to fulfill the aforementioned balance across various parameter spaces.

Positive-Unlabeled (PU) learning aims to train a binary classifier using minimal positive data supplemented by a substantially larger pool of unlabeled data in the specific absence of explicitly annotated negatives. Despite its straightforward nature as a binary classification task the currently best-performing PU algorithms still largely lag behind the supervised counterpart. In this work we identify that the primary bottleneck lies in the difficulty of deriving discriminative representations under unreliable binary supervision with poor semantics which subsequently hinders the common label disambiguation procedures. To cope with this problem we propose a novel PU learning framework namely Latent Group-Aware Meta Disambiguation (LaGAM) which incorporates a hierarchical contrastive learning module to extract the underlying grouping semantics within PU data and produce compact representations. As a result LaGAM enables a more aggressive label disambiguation strategy where we enhance the robustness of training by iteratively distilling the true labels of unlabeled data directly through meta-learning. Extensive experiments show that LaGAM significantly outperforms the current state-of-the-art methods by an average of 6.8% accuracy on common benchmarks approaching the supervised baseline. We also provide comprehensive ablations as well as visualized analysis to verify the effectiveness of our LaGAM.

Camera-parameter-free multi-view pose estimation is an emerging technique for 3D human pose estimation (HPE). They can infer the camera settings implicitly or explicitly to mitigate the depth uncertainty impact showcasing significant potential in real applications. However due to the limited camera setting diversity in the available datasets the inferred camera parameters are always simply hardcoded into the model during training and not adaptable to the input in inference making the learned models cannot generalize well under unseen camera settings. A natural solution is to artificially synthesize some samples i.e. 2D-3D pose pairs under massive new camera settings. Unfortunately to prevent over-fitting the existing camera setting the number of synthesized samples for each new camera setting should be comparable with that for the existing one which multiplies the scale of training and even makes it computationally prohibitive. In this paper we propose a novel HPE approach under the invariant risk minimization (IRM) paradigm. Precisely we first synthesize 2D poses from myriad camera settings. We then train our model under the IRM paradigm which targets at learning a common optimal model across all camera settings and thus enforces the model to automatically learn the camera parameters based on the input data. This allows the model to accurately infer 3D poses on unseen data by training on only a handful of samples from each synthesized setting and thus avoid the unbearable training cost increment. Another appealing feature of our method is that benefited from the capability of IRM in identifying the invariant features its performance on the seen camera settings is enhanced as well. Comprehensive experiments verify the superiority of our approach.

Predicting 3D point trajectory is a fundamental learning task which commonly should be equivariant under Euclidean transformation e.g. SE(3). The existing equivariant models are commonly based on the group equivariant convolution equivariant message passing vector neuron frame averaging etc. In this paper we propose a novel pose-transformed equivariant network in which the points are firstly uniquely normalized and then transformed by the learned pose transformations upon which the points after motion are predicted and aggregated. Under each transformed pose we design the point position predictor consisting of multiple Pose-Transformed Points Prediction blocks in which the global and local motions are estimated and aggregated. This framework can be proven to be equivariant to SE(3) transformation over 3D points. We evaluate the pose-transformed equivariant network on extensive datasets including human motion capture molecular dynamics modeling and dynamics simulation. Extensive experimental comparisons demonstrated our SOTA performance compared with the existing equivariant networks for 3D point trajectory prediction.

Unsupervised landmarks discovery (ULD) for an object category is a challenging computer vision problem. In pursuit of developing a robust ULD framework we explore the potential of a recent paradigm of self-supervised learning algorithms known as diffusion models. Some recent works have shown that these models implicitly contain important correspondence cues. Towards harnessing the potential of diffusion models for ULD task we make the following core contributions. First we propose a ZeroShot ULD baseline based on simple clustering of random pixel locations with nearest neighbour matching. It delivers better results than the existing ULD methods. Second motivated by the ZeroShot performance we develop a ULD algorithm based on diffusion features using self-training and clustering which also outperforms prior methods by notable margins. Third we introduce a new proxy task based on generating latent pose codes and also propose a two-stage clustering mechanism to facilitate effective pseudo-labeling resulting in a significant performance improvement. Overall our approach consistently outperforms state-of-the-art methods on four challenging benchmarks AFLW MAFL CatHeads and LS3D by significant margins.

We propose the Pose Adapted Shape Learning (PASL) for large-pose face reenactment. The PASL framework consists of three modules namely the Pose-Adapted face Encoder (PAE) the Cycle-consistent Shape Generator (CSG) and the Attention-Embedded Generator (AEG). Different from previous approaches that use a single face encoder for identity preservation we propose multiple Pose-Adapted face Encodes (PAEs) to better preserve facial identity across large poses. Given a source face and a reference face the CSG generates a recomposed shape that fuses the source identity and reference action in the shape space and meets the cycle consistency requirement. Taking the shape code and the source as inputs the AEG learns the attention within the shape code and between the shape code and source style to enhance the generation of the desired target face. As existing benchmark datasets are inappropriate for evaluating large-pose face reenactment we propose a scheme to compose large-pose face pairs and introduce the MPIE-LP (Large Pose) and VoxCeleb2-LP datasets as the new large-pose benchmarks. We compared our approach with state-of-the-art methods on MPIE-LP and VoxCeleb2-LP for large-pose performance and on VoxCeleb1 for the common scope of pose variation.

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.

Existing one-shot 4D head synthesis methods usually learn from monocular videos with the aid of 3DMM reconstruction yet the latter is evenly challenging which restricts them from reasonable 4D head synthesis. We present a method to learn one-shot 4D head synthesis via large-scale synthetic data. The key is to first learn a part-wise 4D generative model from monocular images via adversarial learning to synthesize multi-view images of diverse identities and full motions as training data; then leverage a transformer-based animatable triplane reconstructor to learn 4D head reconstruction using the synthetic data. A novel learning strategy is enforced to enhance the generalizability to real images by disentangling the learning process of 3D reconstruction and reenactment. Experiments demonstrate our superiority over the prior art.

Object detection in remote sensing images (RSIs) often suffers from several increasing challenges including the large variation in object scales and the diverse-ranging context. Prior methods tried to address these challenges by expanding the spatial receptive field of the backbone either through large-kernel convolution or dilated convolution. However the former typically introduces considerable background noise while the latter risks generating overly sparse feature representations. In this paper we introduce the Poly Kernel Inception Network (PKINet) to handle the above challenges. PKINet employs multi-scale convolution kernels without dilation to extract object features of varying scales and capture local context. In addition a Context Anchor Attention (CAA) module is introduced in parallel to capture long-range contextual information. These two components work jointly to advance the performance of PKINet on four challenging remote sensing object detection benchmarks namely DOTA-v1.0 DOTA-v1.5 HRSC2016 and DIOR-R.

Establishing an automatic evaluation metric that closely aligns with human judgments is essential for effectively developing image captioning models. Recent data-driven metrics have demonstrated a stronger correlation with human judgments than classic metrics such as CIDEr; however they lack sufficient capabilities to handle hallucinations and generalize across diverse images and texts partially because they compute scalar similarities merely using embeddings learned from tasks unrelated to image captioning evaluation. In this study we propose Polos a supervised automatic evaluation metric for image captioning models. Polos computes scores from multimodal inputs using a parallel feature extraction mechanism that leverages embeddings trained through large-scale contrastive learning. To train Polos we introduce Multimodal Metric Learning from Human Feedback (M2LHF) a framework for developing metrics based on human feedback. We constructed the Polaris dataset which comprises 131K human judgments from 550 evaluators which is approximately ten times larger than standard datasets. Our approach achieved state-of-the-art performance on Composite Flickr8K-Expert Flickr8K-CF PASCAL-50S FOIL and the Polaris dataset thereby demonstrating its effectiveness and robustness.

Lidar has become a cornerstone sensing modality for 3D vision especially for large outdoor scenarios and autonomous driving. Conventional lidar sensors are capable of providing centimeter-accurate distance information by emitting laser pulses into a scene and measuring the time-of-flight (ToF) of the reflection. However the polarization of the received light that depends on the surface orientation and material properties is usually not considered. As such the polarization modality has the potential to improve scene reconstruction beyond distance measurements. In this work we introduce a novel long-range polarization wavefront lidar sensor (PolLidar) that modulates the polarization of the emitted and received light. Departing from conventional lidar sensors PolLidar allows access to the raw time-resolved polarimetric wavefronts. We leverage polarimetric wavefronts to estimate normals distance and material properties in outdoor scenarios with a novel learned reconstruction method. To train and evaluate the method we introduce a simulated and real-world long-range dataset with paired raw lidar data ground truth distance and normal maps. We find that the proposed method improves normal and distance reconstruction by 53% mean angular error and 41% mean absolute error compared to existing shape-from-polarization (SfP) and ToF methods. Code and data are open-sourced here.

In radio astronomy visibility data which are measurements of wave signals from radio telescopes are transformed into images for observation of distant celestial objects. However these resultant images usually contain both real sources and artifacts due to signal sparsity and other factors. One way to obtain cleaner images is to reconstruct samples into dense forms before imaging. Unfortunately existing reconstruction methods often miss some components of visibility in frequency domain so blurred object edges and persistent artifacts remain in the images. Furthermore the computation overhead is high on irregular visibility samples due to the data skew. To address these problems we propose PolarRec a transformer-encoder-conditioned reconstruction pipeline with visibility samples converted into the polar coordinate system. This coordinate system matches the way in which radio telescopes observe a celestial area as the Earth rotates. As a result visibility samples distribute in the polar system more uniformly than in the Cartesian space. Therefore we propose to use radial distance in the loss function to help reconstruct complete visibility effectively. Also we group visibility samples by their polar angles and propose a group-based encoding scheme to improve the efficiency. Our experiments demonstrate that PolarRec markedly improves imaging results by faithfully reconstructing all frequency components in the visibility domain while significantly reducing the computation cost in visibility data encoding. The code is available at https://github.com/RapidsAtHKUST/PolarRec.

The creation of high-quality alpha mattes as ground-truth data for video matting is typically a laborious task. The trade-off between accuracy manual corrections and capture constraints often produces erroneous results or is cost prohibitive. We propose PolarMatte a fully computational alpha matte extraction method for images and video without compromise between quality and practicality. A single polarization camera is used to capture dynamic scenes backlit by an off-the-shelf LCD monitor. PolarMatte exploits the polarization channel to compute the per-pixel opacity of the target scene including the transparency of fine-details translucent objects and optical/motion blur. We leverage polarization clues to robustly detect indistinguishable pixels and extract the alpha matte value at polarized foreground reflections with a polarimetric matting Laplacian. Quantitative and qualitative evaluation demonstrate our ability to computationally extract ground-truth-quality alpha mattes without human labour.

Single point-supervised object detection is gaining attention due to its cost-effectiveness. However existing approaches focus on generating horizontal bounding boxes (HBBs) while ignoring oriented bounding boxes (OBBs) commonly used for objects in aerial images. This paper proposes PointOBB the first single Point-based OBB generation method for oriented object detection. PointOBB operates through the collaborative utilization of three distinctive views: an original view a resized view and a rotated/flipped (rot/flp) view. Upon the original view we leverage the resized and rot/flp views to build a scale augmentation module and an angle acquisition module respectively. In the former module a Scale-Sensitive Consistency (SSC) loss is designed to enhance the deep network's ability to perceive the object scale. For accurate object angle predictions the latter module incorporates self-supervised learning to predict angles which is associated with a scale-guided Dense-to-Sparse (DS) matching strategy for aggregating dense angles corresponding to sparse objects. The resized and rot/flp views are switched using a progressive multi-view switching strategy during training to achieve coupled optimization of scale and angle. Experimental results on the DIOR-R and DOTA-v1.0 datasets demonstrate that PointOBB achieves promising performance and significantly outperforms potential point-supervised baselines.

We present PointInfinity an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size resolution-invariant latent representation. This enables efficient training with low-resolution point clouds while allowing high-resolution point clouds to be generated during inference. More importantly we show that scaling the test-time resolution beyond the training resolution improves the fidelity of generated point clouds and surfaces. We analyze this phenomenon and draw a link to classifier-free guidance commonly used in diffusion models demonstrating that both allow trading off fidelity and variability during inference. Experiments on CO3D show that PointInfinity can efficiently generate high-resolution point clouds (up to 131k points 31 times more than Point-E) with state-of-the-art quality.