CVPR 2024 论文列表

3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal undertaking in autonomous driving aiming to predict the voxel occupancy within volumetric scenes. However prevailing methodologies primarily focus on voxel-wise feature aggregation while neglecting instance semantics and scene context. In this paper we present a novel paradigm termed Symphonies (Scene-from-Insts) that delves into the integration of instance queries to orchestrate 2D-to-3D reconstruction and 3D scene modeling. Leveraging our proposed Serial Instance-Propagated Attentions Symphonies dynamically encodes instance-centric semantics facilitating intricate interactions between the image and volumetric domains. Simultaneously Symphonies fosters holistic scene comprehension by capturing context through the efficient fusion of instance queries alleviating geometric ambiguities such as occlusion and perspective errors through contextual scene reasoning. Experimental results demonstrate that Symphonies achieves state-of-the-art performance on the challenging SemanticKITTI and SSCBench-KITTI-360 benchmarks yielding remarkable mIoU scores of 15.04 and 18.58 respectively. These results showcase the promising advancements of our paradigm. The code for our method is available at https://github.com/hustvl/Symphonies.

We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore to overcome the limitation of scarce high-quality lightstage data we have developed a self-supervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism.

Despite their ability to generate high-resolution and diverse images from text prompts text-to-image diffusion models often suffer from slow iterative sampling processes. Model distillation is one of the most effective directions to accelerate these models. However previous distillation methods fail to retain the generation quality while requiring a significant amount of images for training either from real data or synthetically generated by the teacher model. In response to this limitation we present a novel image-free distillation scheme named SwiftBrush. Drawing inspiration from text-to-3D synthesis in which a 3D neural radiance field that aligns with the input prompt can be obtained from a 2D text-to-image diffusion prior via a specialized loss without the use of any 3D data ground-truth our approach re-purposes that same loss for distilling a pretrained multi-step text-to-image model to a student network that can generate high-fidelity images with just a single inference step. In spite of its simplicity our model stands as one of the first one-step text-to-image generators that can produce images of comparable quality to Stable Diffusion without reliance on any training image data. Remarkably SwiftBrush achieves an FID score of 16.67 and a CLIP score of 0.29 on the COCO-30K benchmark achieving competitive results or even substantially surpassing existing state-of-the-art distillation techniques.

Vision-centric 3D environment understanding is both vital and challenging for autonomous driving systems. Recently object-free methods have attracted considerable attention. Such methods perceive the world by predicting the semantics of discrete voxel grids but fail to construct continuous and accurate obstacle surfaces. To this end in this paper we propose SurroundSDF to implicitly predict the signed distance field (SDF) and semantic field for the continuous perception from surround images. Specifically we introduce a query-based approach and utilize SDF constrained by the Eikonal formulation to accurately describe the surfaces of obstacles. Furthermore considering the absence of precise SDF ground truth we propose a novel weakly supervised paradigm for SDF referred to as the Sandwich Eikonal formulation which emphasizes applying correct and dense constraints on both sides of the surface thereby enhancing the perceptual accuracy of the surface. Experiments suggest that our method achieves SOTA for both occupancy prediction and 3D scene reconstruction tasks on the nuScenes dataset.

Dynamic human rendering from video sequences has achieved remarkable progress by formulating the rendering as a mapping from static poses to human images. However existing methods focus on the human appearance reconstruction of every single frame while the temporal motion relations are not fully explored. In this paper we propose a new 4D motion modeling paradigm SurMo that jointly models the temporal dynamics and human appearances in a unified framework with three key designs: 1) Surface-based motion encoding that models 4D human motions with an efficient compact surface-based triplane. It encodes both spatial and temporal motion relations on the dense surface manifold of a statistical body template which inherits body topology priors for generalizable novel view synthesis with sparse training observations. 2) Physical motion decoding that is designed to encourage physical motion learning by decoding the motion triplane features at timestep t to predict both spatial derivatives and temporal derivatives at the next timestep t+1 in the training stage. 3) 4D appearance decoding that renders the motion triplanes into images by an efficient volumetric surface-conditioned renderer that focuses on the rendering of body surfaces with motion learning conditioning. Extensive experiments validate the state-of-the-art performance of our new paradigm and illustrate the expressiveness of surface-based motion triplanes for rendering high-fidelity view-consistent humans with fast motions and even motion-dependent shadows. Our project page is at: https://taohuumd.github.io/projects/SurMo.

Automating visual inspection in industrial production lines is essential for increasing product quality across various industries. Anomaly detection (AD) methods serve as robust tools for this purpose. However existing public datasets primarily consist of images without anomalies limiting the practical application of AD methods in production settings. To address this challenge we present (1) the Valeo Anomaly Dataset (VAD) a novel real-world industrial dataset comprising 5000 images including 2000 instances of challenging real defects across more than 20 subclasses. Acknowledging that traditional AD methods struggle with this dataset we introduce (2) Segmentation-based Anomaly Detector (SegAD). First SegAD leverages anomaly maps as well as segmentation maps to compute local statistics. Next SegAD uses these statistics and an optional supervised classifier score as input features for a Boosted Random Forest (BRF) classifier yielding the final anomaly score. Our SegAD achieves state-of-the-art performance on both VAD (+2.1% AUROC) and the VisA dataset (+0.4% AUROC). The code and the models are publicly available.

Joint camera pose and dense geometry estimation from a set of images or a monocular video remains a challenging problem due to its computational complexity and inherent visual ambiguities. Most dense incremental reconstruction systems operate directly on image pixels and solve for their 3D positions using multi-view geometry cues. Such pixel-level approaches suffer from ambiguities or violations of multi-view consistency (e.g. caused by textureless or specular surfaces). We address this issue with a new image representation which we call a SuperPrimitive. SuperPrimitives are obtained by splitting images into semantically correlated local regions and enhancing them with estimated surface normal directions both of which are predicted by state-of-the-art single image neural networks. This provides a local geometry estimate per SuperPrimitive while their relative positions are adjusted based on multi-view observations. We demonstrate the versatility of our new representation by addressing three 3D reconstruction tasks: depth completion few-view structure from motion and monocular dense visual odometry. Project page: https://makezur.github.io/SuperPrimitive/

We present SuperNormal a fast high-fidelity approach to multi-view 3D reconstruction using surface normal maps. With a few minutes SuperNormal produces detailed surfaces on par with 3D scanners. We harness volume rendering to optimize a neural signed distance function (SDF) powered by multi-resolution hash encoding. To accelerate training we propose directional finite difference and patchbased ray marching to approximate the SDF gradients numerically. While not compromising reconstruction quality this strategy is nearly twice as efficient as analytical gradients and about three times faster than axis-aligned finite difference. Experiments on the benchmark dataset demonstrate the superiority of SuperNormal in efficiency and accuracy compared to existing multi-view photometric stereo methods. On our captured objects SuperNormal produces more fine-grained geometry than recent neural 3D reconstruction methods. Our code is available at https://github.com/CyberAgentAILab/SuperNormal.git.

Spike camera is a neuromorphic vision sensor that can capture highly dynamic scenes by generating a continuous stream of binary spikes to represent the arrival of photons at very high temporal resolution. Equipped with Bayer color filter array (CFA) color spike camera (CSC) has been invented to capture color information. Although spike camera has already demonstrated great potential for high-speed imaging its spatial resolution is limited compared with conventional digital cameras. This paper proposes a Color Spike Camera Super-Resolution (CSCSR) network to super-resolve higher-resolution color images from spike camera streams with Bayer CFA. To be specific we first propose a representation for Bayer-pattern spike streams exploring local temporal information with global perception to represent the binary data. Then we exploit the CFA layout and sub-pixel level motion to collect temporal pixels for the spatial super-resolution of each color channel. In particular a residual-based module for feature refinement is developed to reduce the impact of motion estimation errors. Considering color correlation we jointly utilize the multi-stage temporal-pixel features of color channels to reconstruct the high-resolution color image. Experimental results demonstrate that the proposed scheme can reconstruct satisfactory color images with both high temporal and spatial resolution from low-resolution Bayer-pattern spike streams. The source codes are available at https://github.com/csycdong/CSCSR.

We study object interaction anticipation in egocentric videos. This task requires an understanding of the spatio-temporal context formed by past actions on objects coined "action context". We propose TransFusion a multimodal transformer-based architecture for short-term object interaction anticipation. Our method exploits the representational power of language by summarizing the action context textually after leveraging pre-trained vision-language foundation models to extract the action context from past video frames. The summarized action context and the last observed video frame are processed by the multimodal fusion module to forecast the next object interaction. Experiments on the Ego4D next active object interaction dataset show the effectiveness of our multimodal fusion model and highlight the benefits of using the power of foundation models and language-based context summaries in a task where vision may appear to suffice. Our novel approach outperforms all state-of-the-art methods on both versions of the Ego4D dataset.

We propose a method to allow precise and extremely fast mesh extraction from 3D Gaussian Splatting. Gaussian Splatting has recently become very popular as it yields realistic rendering while being significantly faster to train than NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D Gaussians as these Gaussians tend to be unorganized after optimization and no method has been proposed so far. Our first key contribution is a regularization term that encourages the Gaussians to align well with the surface of the scene. We then introduce a method that exploits this alignment to extract a mesh from the Gaussians using Poisson reconstruction which is fast scalable and preserves details in contrast to the Marching Cubes algorithm usually applied to extract meshes from Neural SDFs. Finally we introduce an optional refinement strategy that binds Gaussians to the surface of the mesh and jointly optimizes these Gaussians and the mesh through Gaussian splatting rendering. This enables easy editing sculpting animating and relighting of the Gaussians by manipulating the mesh instead of the Gaussians themselves. Retrieving such an editable mesh for realistic rendering is done within minutes with our method compared to hours with the state-of-the-art method on SDFs while providing a better rendering quality.

We propose a method that can generate cinemagraphs automatically from a still landscape image using a pre-trained StyleGAN. Inspired by the success of recent unconditional video generation we leverage a powerful pre-trained image generator to synthesize high-quality cinemagraphs. Unlike previous approaches that mainly utilize the latent space of a pre-trained StyleGAN our approach utilizes its deep feature space for both GAN inversion and cinemagraph generation. Specifically we propose multi-scale deep feature warping (MSDFW) which warps the intermediate features of a pre-trained StyleGAN at different resolutions. By using MSDFW the generated cinemagraphs are of high resolution and exhibit plausible looping animation. We demonstrate the superiority of our method through user studies and quantitative comparisons with state-of-the-art cinemagraph generation methods and a video generation method that uses a pre-trained StyleGAN.

Despite the impressive generative capabilities of diffusion models existing diffusion model-based style transfer methods require inference-stage optimization (e.g. fine-tuning or textual inversion of style) which is time-consuming or fails to leverage the generative ability of large-scale diffusion models. To address these issues we introduce a novel artistic style transfer method based on a pre-trained large-scale diffusion model without any optimization. Specifically we manipulate the features of self-attention layers as the way the cross-attention mechanism works; in the generation process substituting the key and value of content with those of style image. This approach provides several desirable characteristics for style transfer including 1) preservation of content by transferring similar styles into similar image patches and 2) transfer of style based on similarity of local texture (e.g. edge) between content and style images. Furthermore we introduce query preservation and attention temperature scaling to mitigate the issue of disruption of original content and initial latent Adaptive Instance Normalization (AdaIN) to deal with the disharmonious color (failure to transfer the colors of style). Our experimental results demonstrate that our proposed method surpasses state-of-the-art methods in both conventional and diffusion-based style transfer baselines.

Deep learning models for semantic segmentation often experience performance degradation when deployed to unseen target domains unidentified during the training phase. This is mainly due to variations in image texture (i.e. style) from different data sources. To tackle this challenge existing domain generalized semantic segmentation (DGSS) methods attempt to remove style variations from the feature. However these approaches struggle with the entanglement of style and content which may lead to the unintentional removal of crucial content information causing performance degradation. This study addresses this limitation by proposing BlindNet a novel DGSS approach that blinds the style without external modules or datasets. The main idea behind our proposed approach is to alleviate the effect of style in the encoder whilst facilitating robust segmentation in the decoder. To achieve this BlindNet comprises two key components: covariance alignment and semantic consistency contrastive learning. Specifically the covariance alignment trains the encoder to uniformly recognize various styles and preserve the content information of the feature rather than removing the style-sensitive factor. Meanwhile semantic consistency contrastive learning enables the decoder to construct discriminative class embedding space and disentangles features that are vulnerable to misclassification. Through extensive experiments our approach outperforms existing DGSS methods exhibiting robustness and superior performance for semantic segmentation on unseen target domains.

Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields generating visually compelling outputs from textual prompts. However controlling these models to ensure consistent style remains challenging with existing methods necessitating fine-tuning and manual intervention to disentangle content and style. In this paper we introduce StyleAligned a novel technique designed to establish style alignment among a series of generated images. By employing minimal `attention sharing' during the diffusion process our method maintains style consistency across images within T2I models. This approach allows for the creation of style-consistent images using a reference style through a straightforward inversion operation. Our method's evaluation across diverse styles and text prompts demonstrates high-quality synthesis and fidelity underscoring its efficacy in achieving consistent style across various inputs.

We describe a novel method StyLitGAN for relighting and resurfacing images in the absence of labeled data. StyLitGAN generates images with realistic lighting effects including cast shadows soft shadows inter-reflections and glossy effects without the need for paired or CGI data. StyLitGAN uses an intrinsic image method to decompose an image followed by a search of the latent space of a pretrained StyleGAN to identify a set of directions. By prompting the model to fix one component (e.g. albedo) and vary another (e.g. shading) we generate relighted images by adding the identified directions to the latent style codes. Quantitative metrics of change in albedo and lighting diversity allow us to choose effective directions using a forward selection process. Qualitative evaluation confirms the effectiveness of our method.