Although diffusion models are rising as a powerful solution for blind face restoration they are criticized for two problems: 1) slow training and inference speed and 2) failure in preserving identity and recovering fine-grained facial details. In this work we propose WaveFace to solve the problems in the frequency domain where low- and high-frequency components decomposed by wavelet transformation are considered individually to maximize authenticity as well as efficiency. The diffusion model is applied to recover the low-frequency component only which presents general information of the original image but 1/16 in size. To preserve the original identity the generation is conditioned on the low-frequency component of low-quality images at each denoising step. Meanwhile high-frequency components at multiple decomposition levels are handled by a unified network which recovers complex facial details in a single step. Evaluations on four benchmark datasets show that: 1) WaveFace outperforms state-of-the-art methods in authenticity especially in terms of identity preservation and 2) authentic images are restored with the efficiency 10x faster than existing diffusion model-based BFR methods.
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We introduce a new task -- language-driven video inpainting which uses natural language instructions to guide the inpainting process. This approach overcomes the limitations of traditional video inpainting methods that depend on manually labeled binary masks a process often tedious and labor-intensive. We present the Remove Objects from Videos by Instructions (ROVI) dataset containing 5650 videos and 9091 inpainting results to support training and evaluation for this task. We also propose a novel diffusion-based language-driven video inpainting framework the first end-to-end baseline for this task integrating Multimodal Large Language Models to understand and execute complex language-based inpainting requests effectively. Our comprehensive results showcase the dataset's versatility and the model's effectiveness in various language-instructed inpainting scenarios. We have made datasets code and models publicly available at https://github.com/jianzongwu/Language-Driven-Video-Inpainting.
We address the problem of generalized category discovery (GCD) that aims to partition a partially labeled collection of images; only a small part of the collection is labeled and the total number of target classes is unknown. To address this generalized image clustering problem we revisit the mean-shift algorithm i.e. a classic powerful technique for mode seeking and incorporate it into a contrastive learning framework. The proposed method dubbed Contrastive Mean-Shift (CMS) learning trains an embedding network to produce representations with better clustering properties by an iterative process of mean shift and contrastive update. Experiments demonstrate that our method both in settings with and without the total number of clusters being known achieves state-of-the-art performance on six public GCD benchmarks without bells and whistles.
Real-time multi-person pose estimation presents significant challenges in balancing speed and precision. While two-stage top-down methods slow down as the number of people in the image increases existing one-stage methods often fail to simultaneously deliver high accuracy and real-time performance. This paper introduces RTMO a one-stage pose estimation framework that seamlessly integrates coordinate classification by representing keypoints using dual 1-D heatmaps within the YOLO architecture achieving accuracy comparable to top-down methods while maintaining high speed. We propose a dynamic coordinate classifier and a tailored loss function for heatmap learning specifically designed to address the incompatibilities between coordinate classification and dense prediction models. RTMO outperforms state-of-the-art one-stage pose estimators achieving 1.1% higher AP on COCO while operating about 9 times faster with the same backbone. Our largest model RTMO-l attains 74.8% AP on COCO val2017 and 141 FPS on a single V100 GPU demonstrating its efficiency and accuracy. The code and models are available at https://github.com/open-mmlab/mmpose/tree/main/projects/rtmo.
WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects Under Occlusion
PDF ↗Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments partly due to the lack of large-scale labeled ground-truth annotations for learning occlusion. In this work we introduce a novel framework for automatically generating a large realistic dataset of dynamic objects under occlusions using freely available time-lapse imagery. By leveraging off-the-shelf 2D (bounding box segmentation keypoint) and 3D (pose shape) predictions as pseudo-groundtruth unoccluded 3D objects are identified automatically and composited into the background in a clip-art style ensuring realistic appearances and physically accurate occlusion configurations. The resulting clip-art image with pseudo-groundtruth enables efficient training of object reconstruction methods that are robust to occlusions. Our method demonstrates significant improvements in both 2D and 3D reconstruction particularly in scenarios with heavily occluded objects like vehicles and people in urban scenes.
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation
PDF ↗Semi-supervised semantic segmentation allows model to mine effective supervision from unlabeled data to complement label-guided training. Recent research has primarily focused on consistency regularization techniques exploring perturbation-invariant training at both the image and feature levels. In this work we proposed a novel feature-level consistency learning framework named Density-Descending Feature Perturbation (DDFP). Inspired by the low-density separation assumption in semi-supervised learning our key insight is that feature density can shed a light on the most promising direction for the segmentation classifier to explore which is the regions with lower density. We propose to shift features with confident predictions towards lower-density regions by perturbation injection. The perturbed features are then supervised by the predictions on the original features thereby compelling the classifier to explore less dense regions to effectively regularize the decision boundary. Central to our method is the estimation of feature density. To this end we introduce a lightweight density estimator based on normalizing flow allowing for efficient capture of the feature density distribution in an online manner. By extracting gradients from the density estimator we can determine the direction towards less dense regions for each feature. The proposed DDFP outperforms other designs on feature-level perturbations and shows state of the art performances on both Pascal VOC and Cityscapes dataset under various partition protocols. The project is available at https://github.com/Gavinwxy/DDFP.
AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representation
PDF ↗This paper proposes a novel direct Audio-Visual Speech to Audio-Visual Speech Translation (AV2AV) framework where the input and output of the system are multimodal (i.e. audio and visual speech). With the proposed AV2AV two key advantages can be brought: 1) We can perform real-like conversations with individuals worldwide in a virtual meeting by utilizing our own primary languages. In contrast to Speech-to-Speech Translation (A2A) which solely translates between audio modalities the proposed AV2AV directly translates between audio-visual speech. This capability enhances the dialogue experience by presenting synchronized lip movements along with the translated speech. 2) We can improve the robustness of the spoken language translation system. By employing the complementary information of audio-visual speech the system can effectively translate spoken language even in the presence of acoustic noise showcasing robust performance. To mitigate the problem of the absence of a parallel AV2AV translation dataset we propose to train our spoken language translation system with the audio-only dataset of A2A. This is done by learning unified audio-visual speech representations through self-supervised learning in advance to train the translation system. Moreover we propose an AV-Renderer that can generate raw audio and video in parallel. It is designed with zero-shot speaker modeling thus the speaker in source audio-visual speech can be maintained at the target translated audio-visual speech. The effectiveness of AV2AV is evaluated with extensive experiments in a many-to-many language translation setting. Demo page is available on choijeongsoo.github.io/av2av.
SVG (Scalable Vector Graphics) is a widely used graphics format that possesses excellent scalability and editability. Image vectorization that aims to convert raster images to SVGs is an important yet challenging problem in computer vision and graphics. Existing image vectorization methods either suffer from low reconstruction accuracy for complex images or require long computation time. To address this issue we propose SuperSVG a superpixel-based vectorization model that achieves fast and high-precision image vectorization. Specifically we decompose the input image into superpixels to help the model focus on areas with similar colors and textures. Then we propose a two-stage self-training framework where a coarse-stage model is employed to reconstruct the main structure and a refinement-stage model is used for enriching the details. Moreover we propose a novel dynamic path warping loss to help the refinement-stage model to inherit knowledge from the coarse-stage model. Extensive qualitative and quantitative experiments demonstrate the superior performance of our method in terms of reconstruction accuracy and inference time compared to state-of-the-art approaches. The code is available in https://github.com/sjtuplayer/SuperSVG.
We introduce Free3D a simple accurate method for monocular open-set novel view synthesis (NVS). Similar to Zero-1-to-3 we start from a pre-trained 2D image generator for generalization and fine-tune it for NVS. Compared to other works that took a similar approach we obtain significant improvements without resorting to an explicit 3D representation which is slow and memory-consuming and without training an additional network for 3D reconstruction. Our key contribution is to improve the way the target camera pose is encoded in the network which we do by introducing a new ray conditioning normalization (RCN) layer. The latter injects pose information in the underlying 2D image generator by telling each pixel its viewing direction. We further improve multi-view consistency by using light-weight multi-view attention layers and by sharing generation noise between the different views. We train Free3D on the Objaverse dataset and demonstrate excellent generalization to new categories in new datasets including OmniObject3D and GSO. The project page is available at https://chuanxiaz.com/free3d/.
Implicit neural representation (INR) in combination with geometric rendering has recently been employed in real-time dense RGB-D SLAM. Despite active research endeavors being made there lacks a unified protocol for fair evaluation impeding the evolution of this area. In this work we establish to our knowledge the first open-source benchmark framework to evaluate the performance of a wide spectrum of commonly used INRs and rendering functions for mapping and localization. The goal of our benchmark is to 1) gain an intuition of how different INRs and rendering functions impact mapping and localization and 2) establish a unified evaluation protocol w.r.t. the design choices that may impact the mapping and localization. With the framework we conduct a large suite of experiments offering various insights in choosing the INRs and geometric rendering functions: for example the dense feature grid outperforms other INRs (e.g. tri-plane and hash grid) even when geometric and color features are jointly encoded for memory efficiency. To extend the findings into the practical scenario a hybrid encoding strategy is proposed to bring the best of the accuracy and completion from the grid-based and decomposition-based INRs. We further propose explicit hybrid encoding for high-fidelity dense grid mapping to comply with the RGB-D SLAM system that puts the premise on robustness and computation efficiency.
Optical flow estimation a process of predicting pixel-wise displacement between consecutive frames has commonly been approached as a regression task in the age of deep learning. Despite notable advancements this de facto paradigm unfortunately falls short in generalization performance when trained on synthetic or constrained data. Pioneering a paradigm shift we reformulate optical flow estimation as a conditional flow generation challenge unveiling FlowDiffuser --- a new family of optical flow models that could have stronger learning and generalization capabilities. FlowDiffuser estimates optical flow through a `noise-to-flow' strategy progressively eliminating noise from randomly generated flows conditioned on the provided pairs. To optimize accuracy and efficiency our FlowDiffuser incorporates a novel Conditional Recurrent Denoising Decoder (Conditional-RDD) streamlining the flow estimation process. It incorporates a unique Hidden State Denoising (HSD) paradigm effectively leveraging the information from previous time steps. Moreover FlowDiffuser can be easily integrated into existing flow networks leading to significant improvements in performance metrics compared to conventional implementations. Experiments on challenging benchmarks including Sintel and KITTI demonstrate the effectiveness of our FlowDiffuser with superior performance to existing state-of-the-art models. Code is available at https://github.com/LA30/FlowDiffuser.
Human matting is a foundation task in image and video processing where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional guidance or improve the temporal consistency of a single instance across frames. We propose a new framework MaGGIe Masked Guided Gradual Human Instance Matting which predicts alpha mattes progressively for each human instances while maintaining the computational cost precision and consistency. Our method leverages modern architectures including transformer attention and sparse convolution to output all instance mattes simultaneously without exploding memory and latency. Although keeping constant inference costs in the multiple-instance scenario our framework achieves robust and versatile performance on our proposed synthesized benchmarks. With the higher quality image and video matting benchmarks the novel multi-instance synthesis approach from publicly available sources is introduced to increase the generalization of models in real-world scenarios. Our code and datasets are available at https://maggie-matt.github.io
Continual learning can empower vision-language models to continuously acquire new knowledge without the need for access to the entire historical dataset. However mitigating the performance degradation in large-scale models is non-trivial due to (i) parameter shifts throughout lifelong learning and (ii) significant computational burdens associated with full-model tuning. In this work we present a parameter-efficient continual learning framework to alleviate long-term forgetting in incremental learning with vision-language models. Our approach involves the dynamic expansion of a pre-trained CLIP model through the integration of Mixture-of-Experts (MoE) adapters in response to new tasks. To preserve the zero-shot recognition capability of vision-language models we further introduce a Distribution Discriminative Auto-Selector (DDAS) that automatically routes in-distribution and out-of-distribution inputs to the MoE Adapter and the original CLIP respectively. Through extensive experiments across various settings our proposed method consistently outperforms previous state-of-the-art approaches while concurrently reducing parameter training burdens by 60%.
Panoptic segmentation combining semantic and instance segmentation stands as a cutting-edge computer vision task. Despite recent progress with deep learning models the dynamic nature of real-world applications necessitates continual learning where models adapt to new classes (plasticity) over time without forgetting old ones (catastrophic forgetting). Current continual segmentation methods often rely on distillation strategies like knowledge distillation and pseudo-labeling which are effective but result in increased training complexity and computational overhead. In this paper we introduce a novel and efficient method for continual panoptic segmentation based on Visual Prompt Tuning dubbed ECLIPSE. Our approach involves freezing the base model parameters and fine-tuning only a small set of prompt embeddings addressing both catastrophic forgetting and plasticity and significantly reducing the trainable parameters. To mitigate inherent challenges such as error propagation and semantic drift in continual segmentation we propose logit manipulation to effectively leverage common knowledge across the classes. Experiments on ADE20K continual panoptic segmentation benchmark demonstrate the superiority of ECLIPSE notably its robustness against catastrophic forgetting and its reasonable plasticity achieving a new state-of-the-art. The code is available at https://github.com/clovaai/ECLIPSE.
We propose Multiscale Correlation searching homography estimation Network namely MCNet an iterative deep homography estimation architecture. Different from previous approaches that achieve iterative refinement by correlation searching within a single scale MCNet combines the multiscale strategy with correlation searching incurring nearly ignored computational overhead. Moreover MCNet adopts a Fine-Grained Optimization loss function named FGO loss to further boost the network training at the convergent stage which can improve the estimation accuracy without additional computational overhead. According to our experiments using the above two simple strategies can produce significant homography estimation accuracy with considerable efficiency. We show that MCNet achieves state-of-the-art performance on a variety of datasets including common scene MSCOCO cross-modal scene GoogleEarth and GoogleMap and dynamic scene SPID. Compared to the previous SOTA method 2-scale RHWF our MCNet reduces inference time FLOPs parameter cost and memory cost by 78.9% 73.5% 34.1% and 33.2% respectively while achieving 20.5% (MSCOCO) 43.4% (GoogleEarth) and 41.1% (GoogleMap) mean average corner error (MACE) reduction. Source code is available at https://github.com/zjuzhk/MCNet.
We present As-Plausible-as-Possible (APAP) mesh deformation technique that leverages 2D diffusion priors to preserve the plausibility of a mesh under user-controlled deformation. Our framework uses per-face Jacobians to represent mesh deformations where mesh vertex coordinates are computed via a differentiable Poisson Solve. The deformed mesh is rendered and the resulting 2D image is used in the Score Distillation Sampling (SDS) process which enables extracting meaningful plausibility priors from a pretrained 2D diffusion model. To better preserve the identity of the edited mesh we fine-tune our 2D diffusion model with LoRA. Gradients extracted by SDS and a user-prescribed handle displacement are then backpropagated to the per-face Jacobians and we use iterative gradient descent to compute the final deformation that balances between the user edit and the output plausibility. We evaluate our method with 2D and 3D meshes and demonstrate qualitative and quantitative improvements when using plausibility priors over geometry-preservation or distortion-minimization priors used by previous techniques. Our project page is at: https://as-plausible-aspossible.github.io/
Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression
PDF ↗Recent Vision Transformer Compression (VTC) works mainly follow a two-stage scheme where the importance score of each model unit is first evaluated or preset in each submodule followed by the sparsity score evaluation according to the target sparsity constraint. Such a separate evaluation process induces the gap between importance and sparsity score distributions thus causing high search costs for VTC. In this work for the first time we investigate how to integrate the evaluations of importance and sparsity scores into a single stage searching the optimal subnets in an efficient manner. Specifically we present OFB a cost-efficient approach that simultaneously evaluates both importance and sparsity scores termed Once for Both (OFB) for VTC. First a bi-mask scheme is developed by entangling the importance score and the differentiable sparsity score to jointly determine the pruning potential (prunability) of each unit. Such a bi-mask search strategy is further used together with a proposed adaptive one-hot loss to realize the progressive-and-efficient search for the most important subnet. Finally Progressive Masked Image Modeling (PMIM) is proposed to regularize the feature space to be more representative during the search process which may be degraded by the dimension reduction. Extensive experiments demonstrate that OFB can achieve superior compression performance over state-of-the-art searching-based and pruning-based methods under various Vision Transformer architectures meanwhile promoting search efficiency significantly e.g. costing one GPU search day for the compression of DeiT-S on ImageNet-1K.
Superpixels play a crucial role in image processing by partitioning an image into clusters of pixels with similar visual attributes. This facilitates subsequent image processing tasks offering computational advantages over the manipulation of individual pixels. While numerous oversegmentation techniques have emerged in recent years many rely on predefined initialization and termination criteria. In this paper a novel top-down superpixel segmentation algorithm called Hierarchical Histogram Threshold Segmentation (HHTS) is introduced. It eliminates the need for initialization and implements auto-termination outperforming state-of-the-art methods w.r.t boundary recall. This is achieved by iteratively partitioning individual pixel segments into foreground and background and applying intensity thresholding across multiple color channels. The underlying iterative process constructs a superpixel hierarchy that adapts to local detail distributions until color information exhaustion. Experimental results demonstrate the superiority of the proposed approach in terms of boundary adherence while maintaining competitive runtime performance on the BSDS500 and NYUV2 datasets. Furthermore an application of HHTS in refining machine learning-based semantic segmentation masks produced by the Segment Anything Foundation Model (SAM) is presented.
We present DiffuScene for indoor 3D scene synthesis based on a novel scene configuration denoising diffusion model. It generates 3D instance properties stored in an unordered object set and retrieves the most similar geometry for each object configuration which is characterized as a concatenation of different attributes including location size orientation semantics and geometry features. We introduce a diffusion network to synthesize a collection of 3D indoor objects by denoising a set of unordered object attributes. Unordered parametrization simplifies and eases the joint distribution approximation. The shape feature diffusion facilitates natural object placements including symmetries. Our method enables many downstream applications including scene completion scene arrangement and text-conditioned scene synthesis. Experiments on the 3D-FRONT dataset show that our method can synthesize more physically plausible and diverse indoor scenes than state-of-the-art methods. Extensive ablation studies verify the effectiveness of our design choice in scene diffusion models.
A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception. This work presents M3Net a one-of-a-kind framework for fulfilling multi-task multi-dataset multi-modality LiDAR segmentation in a universal manner using just a single set of parameters. To better exploit data volume and diversity we first combine large-scale driving datasets acquired by different types of sensors from diverse scenes and then conduct alignments in three spaces namely data feature and label spaces during the training. As a result M3Net is capable of taming heterogeneous data for training state-of-the-art LiDAR segmentation models. Extensive experiments on twelve LiDAR segmentation datasets verify our effectiveness. Notably using a shared set of parameters M3Net achieves 75.1% 83.1% and 72.4% mIoU scores respectively on the official benchmarks of SemanticKITTI nuScenes and Waymo Open.