CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
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.
Tone mapping techniques aiming to convert high dynamic range (HDR) images to high-quality low dynamic range (LDR) images for display play a more crucial role in real-world vision systems with the increasing application of HDR images. However obtaining paired HDR and high-quality LDR images is difficult posing a challenge to deep learning based tone mapping methods. To overcome this challenge we propose a novel zero-shot tone mapping framework that utilizes shared structure knowledge allowing us to transfer a pre-trained mapping model from the LDR domain to HDR fields without paired training data. Our approach involves decomposing both the LDR and HDR images into two components: structural information and tonal information. To preserve the original image's structure we modify the reverse sampling process of a diffusion model and explicitly incorporate the structure information into the intermediate results. Additionally for improved image details we introduce a dual-control network architecture that enables different types of conditional inputs to control different scales of the output. Experimental results demonstrate the effectiveness of our approach surpassing previous state-of-the-art methods both qualitatively and quantitatively. Moreover our model exhibits versatility and can be applied to other low-level vision tasks without retraining. The code is available at https://github.com/ZSDM-HDR/Zero-Shot-Diffusion-HDR.
In this paper we propose VidLA an approach for video-language alignment at scale. There are two major limitations of previous video-language alignment approaches. First they do not capture both short-range and long-range temporal dependencies and typically employ complex hierarchical deep network architectures that are hard to integrate with existing pretrained image-text foundation models. To effectively address this limitation we instead keep the network architecture simple and use a set of data tokens that operate at different temporal resolutions in a hierarchical manner accounting for the temporally hierarchical nature of videos. By employing a simple two-tower architecture we are able to initialize our video-language model with pretrained image-text foundation models thereby boosting the final performance. Second existing video-language alignment works struggle due to the lack of semantically aligned large-scale training data. To overcome it we leverage recent LLMs to curate the largest video-language dataset to date with better visual grounding. Furthermore unlike existing video-text datasets which only contain short clips our dataset is enriched with video clips of varying durations to aid our temporally hierarchical data tokens in extracting better representations at varying temporal scales. Overall empirical results show that our proposed approach surpasses state-of-the-art methods on multiple retrieval benchmarks especially on longer videos and performs competitively on classification benchmarks.
Self-Supervised Learning (SSL) has demonstrated promising results in 3D medical image analysis. However the lack of high-level semantics in pre-training still heavily hinders the performance of downstream tasks. We observe that 3D medical images contain relatively consistent contextual position information i.e. consistent geometric relations between different organs which leads to a potential way for us to learn consistent semantic representations in pre-training. In this paper we propose a simple-yet-effective Volume Contrast (VoCo) framework to leverage the contextual position priors for pre-training. Specifically we first generate a group of base crops from different regions while enforcing feature discrepancy among them where we employ them as class assignments of different regions. Then we randomly crop sub-volumes and predict them belonging to which class (located at which region) by contrasting their similarity to different base crops which can be seen as predicting contextual positions of different sub-volumes. Through this pretext task VoCo implicitly encodes the contextual position priors into model representations without the guidance of annotations enabling us to effectively improve the performance of downstream tasks that require high-level semantics. Extensive experimental results on six downstream tasks demonstrate the superior effectiveness of VoCo. Code will be available at https://github.com/Luffy03/VoCo.
In this paper we present CCEdit a versatile generative video editing framework based on diffusion models. Our approach employs a novel trident network structure that separates structure and appearance control ensuring precise and creative editing capabilities. Utilizing the foundational ControlNet architecture we maintain the structural integrity of the video during editing. The incorporation of an additional appearance branch enables users to exert fine-grained control over the edited key frame. These two side branches seamlessly integrate into the main branch which is constructed upon existing text-to-image (T2I) generation models through learnable temporal layers. The versatility of our framework is demonstrated through a diverse range of choices in both structure representations and personalized T2I models as well as the option to provide the edited key frame. To facilitate comprehensive evaluation we introduce the BalanceCC benchmark dataset comprising 100 videos and 4 target prompts for each video. Our extensive user studies compare CCEdit with eight state-of-the-art video editing methods. The outcomes demonstrate CCEdit's substantial superiority over all other methods.
Generalizable 3D object reconstruction from single-view RGB-D images remains a challenging task particularly with real-world data. Current state-of-the-art methods develop Transformer-based implicit field learning necessitating an intensive learning paradigm that requires dense query-supervision uniformly sampled throughout the entire space. We propose a novel approach IPoD which harmonizes implicit field learning with point diffusion. This approach treats the query points for implicit field learning as a noisy point cloud for iterative denoising allowing for their dynamic adaptation to the target object shape. Such adaptive query points harness diffusion learning's capability for coarse shape recovery and also enhances the implicit representation's ability to delineate finer details. Besides an additional self-conditioning mechanism is designed to use implicit predictions as the guidance of diffusion learning leading to a cooperative system. Experiments conducted on the CO3D-v2 dataset affirm the superiority of IPoD achieving 7.8% improvement in F-score and 28.6% in Chamfer distance over existing methods. The generalizability of IPoD is also demonstrated on the MVImgNet dataset. Our project page is at https://yushuang-wu.github.io/IPoD.
As for human avatar reconstruction contemporary techniques commonly necessitate the acquisition of costly data and struggle to achieve satisfactory results from a small number of casual images. In this paper we investigate this task from a few-shot unconstrained photo album. The reconstruction of human avatars from such data sources is challenging because of limited data amount and dynamic articulated poses. For handling dynamic data we integrate a skinning mechanism with deep marching tetrahedra (DMTet) to form a drivable tetrahedral representation which drives arbitrary mesh topologies generated by the DMTet for the adaptation of unconstrained images. To effectively mine instructive information from few-shot data we devise a two-phase optimization method with few-shot reference and few-shot guidance. The former focuses on aligning avatar identity with reference images while the latter aims to generate plausible appearances for unseen regions. Overall our framework called HaveFun can undertake avatar reconstruction rendering and animation. Extensive experiments on our developed benchmarks demonstrate that HaveFun exhibits substantially superior performance in reconstructing the human body and hand.
Collaborative perception enhances perception performance by enabling autonomous vehicles to exchange complementary information. Despite its potential to revolutionize the mobile industry challenges in various environments such as communication bandwidth limitations localization errors and information aggregation inefficiencies hinder its implementation in practical applications. In this work we propose ERMVP a communication-Efficient and collaboration-Robust Multi-Vehicle Perception method in challenging environments. Specifically ERMVP has three distinct strengths: i) It utilizes the hierarchical feature sampling strategy to abstract a representative set of feature vectors using less communication overhead for efficient communication; ii) It employs the sparse consensus features to execute precise spatial location calibrations effectively mitigating the implications of vehicle localization errors; iii) A pioneering feature fusion and interaction paradigm is introduced to integrate holistic spatial semantics among different vehicles and data sources. To thoroughly validate our method we conduct extensive experiments on real-world and simulated datasets. The results demonstrate that the proposed ERMVP is significantly superior to the state-of-the-art collaborative perception methods.
Diffusion models have achieved remarkable image generation quality surpassing previous generative models. However a notable limitation of diffusion models in comparison to GANs is their difficulty in smoothly interpolating between two image samples due to their highly unstructured latent space. Such a smooth interpolation is intriguing as it naturally serves as a solution for the image morphing task with many applications. In this work we address this limitation via DiffMorpher an approach that enables smooth and natural image interpolation by harnessing the prior knowledge of a pre-trained diffusion model. Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition where correspondence automatically emerges without the need for annotation. In addition we propose an attention interpolation and injection technique an adaptive normalization adjustment method and a new sampling schedule to further enhance the smoothness between consecutive images. Extensive experiments demonstrate that DiffMorpher achieves starkly better image morphing effects than previous methods across a variety of object categories bridging a critical functional gap that distinguished diffusion models from GANs.
As an important and practical way to obtain high dynamic range (HDR) video HDR video reconstruction from sequences with alternating exposures is still less explored mainly due to the lack of large-scale real-world datasets. Existing methods are mostly trained on synthetic datasets which perform poorly in real scenes. In this work to facilitate the development of real-world HDR video reconstruction we present Real-HDRV a large-scale real-world benchmark dataset for HDR video reconstruction featuring various scenes diverse motion patterns and high-quality labels. Specifically our dataset contains 500 LDRs-HDRs video pairs comprising about 28000 LDR frames and 4000 HDR labels covering daytime nighttime indoor and outdoor scenes. To our best knowledge our dataset is the largest real-world HDR video reconstruction dataset. Correspondingly we propose an end-to-end network for HDR video reconstruction where a novel two-stage strategy is designed to perform alignment sequentially. Specifically the first stage performs global alignment with the adaptively estimated global offsets reducing the difficulty of subsequent alignment. The second stage implicitly performs local alignment in a coarse-to-fine manner at the feature level using the adaptive separable convolution.
Specifically the first stage performs global alignment with the adaptively estimated global offsets reducing the difficulty of subsequent alignment. The second stage implicitly performs local alignment in a coarse-to-fine manner at the feature level using the adaptive separable convolution. Extensive experiments demonstrate that: (1) models trained on our dataset can achieve better performance on real scenes than those trained on synthetic datasets; (2) our method outperforms previous state-of-the-art methods. Our dataset is available at https://github.com/yungsyu99/Real-HDRV.
3D head avatars built with neural implicit volumetric representations have achieved unprecedented levels of photorealism. However the computational cost of these methods remains a significant barrier to their widespread adoption particularly in real-time applications such as virtual reality and teleconferencing. While attempts have been made to develop fast neural rendering approaches for static scenes these methods cannot be simply employed to support realistic facial expressions such as in the case of a dynamic facial performance. To address these challenges we propose a novel fast 3D neural implicit head avatar model that achieves real-time rendering while maintaining fine-grained controllability and high rendering quality. Our key idea lies in the introduction of local hash table blendshapes which are learned and attached to the vertices of an underlying face parametric model. These per-vertex hash-tables are linearly merged with weights predicted via a CNN resulting in expression dependent embeddings. Our novel representation enables efficient density and color predictions using a lightweight MLP which is further accelerated by a hierarchical nearest neighbor search method. Extensive experiments show that our approach runs in real-time while achieving comparable rendering quality to state-of-the-arts and decent results on challenging expressions.
Low-precision quantization is recognized for its efficacy in neural network optimization. Our analysis reveals that non-quantized elementwise operations which are prevalent in layers such as parameterized activation functions batch normalization and quantization scaling dominate the inference cost of low-precision models. These non-quantized elementwise operations are commonly overlooked in SOTA efficiency metrics such as Arithmetic Computation Effort (ACE). In this paper we propose ACEv2 - an extended version of ACE which offers a better alignment with the inference cost of quantized models and their energy consumption on ML hardware. Moreover we introduce PikeLPN a model that addresses these efficiency issues by applying quantization to both elementwise operations and multiply-accumulate operations. In particular we present a novel quantization technique for batch normalization layers named QuantNorm which allows for quantizing the batch normalization parameters without compromising the model performance. Additionally we propose applying Double Quantization where the quantization scaling parameters are quantized. Furthermore we recognize and resolve the issue of distribution mismatch in Separable Convolution layers by introducing Distribution-Heterogeneous Quantization which enables quantizing them to low-precision. PikeLPN achieves Pareto-optimality in efficiency-accuracy trade-off with up to 3X efficiency improvement compared to SOTA low-precision models.
Modern depth sensors such as LiDAR operate by sweeping laser-beams across the scene resulting in a point cloud with notable 1D curve-like structures. In this work we introduce a new point cloud processing scheme and backbone called CurveCloudNet which takes advantage of the curve-like structure inherent to these sensors. While existing backbones discard the rich 1D traversal patterns and rely on generic 3D operations CurveCloudNet parameterizes the point cloud as a collection of polylines (dubbed a "curve cloud") establishing a local surface-aware ordering on the points. By reasoning along curves CurveCloudNet captures lightweight curve-aware priors to efficiently and accurately reason in several diverse 3D environments. We evaluate CurveCloudNet on multiple synthetic and real datasets that exhibit distinct 3D size and structure. We demonstrate that CurveCloudNet outperforms both point-based and sparse-voxel backbones in various segmentation settings notably scaling to large scenes better than point-based alternatives while exhibiting improved single-object performance over sparse-voxel alternatives. In all CurveCloudNet is an efficient and accurate backbone that can handle a larger variety of 3D environments than past works.
We address the challenge of generating 3D articulated objects in a controllable fashion. Currently modeling articulated 3D objects is either achieved through laborious manual authoring or using methods from prior work that are hard to scale and control directly. We leverage the interplay between part shape connectivity and motion using a denoising diffusion-based method with attention modules designed to extract correlations between part attributes. Our method takes an object category label and a part connectivity graph as input and generates an object's geometry and motion parameters. The generated objects conform to user-specified constraints on the object category part shape and part articulation. Our experiments show that our method outperforms the state-of-the-art in articulated object generation producing more realistic objects while conforming better to user constraints.
To reduce the reliance on large-scale datasets recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot segmentation methods first pre-train models on 'seen' classes and then evaluate their generalization performance on 'unseen' classes. However the prior pre-training stage not only introduces excessive time overhead but also incurs a significant domain gap on 'unseen' classes. To tackle these issues we propose a Non-parametric Network for few-shot 3D Segmentation Seg-NN and its Parametric variant Seg-PN. Without training Seg-NN extracts dense representations by hand-crafted filters and achieves comparable performance to existing parameterized models. Due to the elimination of pre-training Seg-NN can alleviate the domain gap issue and save a substantial amount of time. Based on Seg-NN Seg-PN only requires training a lightweight QUEry-Support Transferring (QUEST) module which enhances the interaction between the support set and query set. Experiments suggest that Seg-PN outperforms previous state-of-the-art method by +4.19% and +7.71% mIoU on S3DIS and ScanNet datasets respectively while reducing training time by -90% indicating its effectiveness and efficiency.
Experiments suggest that Seg-PN outperforms previous state-of-the-art method by +4.19% and +7.71% mIoU on S3DIS and ScanNet datasets respectively while reducing training time by -90% indicating its effectiveness and efficiency. Code is available https://github.com/yangyangyang127/Seg-NN.
We introduce PhysGaussian a new method that seamlessly integrates physically grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthesis. Employing a customized Material Point Method (MPM) our approach enriches 3D Gaussian kernels with physically meaningful kinematic deformation and mechanical stress attributes all evolved in line with continuum mechanics principles. A defining characteristic of our method is the seamless integration between physical simulation and visual rendering: both components utilize the same 3D Gaussian kernels as their discrete representations. This negates the necessity for triangle/tetrahedron meshing marching cubes cage meshes or any other geometry embedding highlighting the principle of "what you see is what you simulate (WS^2)". Our method demonstrates exceptional versatility across a wide variety of materials--including elastic entities plastic metals non-Newtonian fluids and granular materials--showcasing its strong capabilities in creating diverse visual content with novel viewpoints and movements.
Recovering images distorted by atmospheric turbulence is a challenging inverse problem due to the stochastic nature of turbulence. Although numerous turbulence mitigation (TM) algorithms have been proposed their efficiency and generalization to real-world dynamic scenarios remain severely limited. Building upon the intuitions of classical TM algorithms we present the Deep Atmospheric TUrbulence Mitigation network (DATUM). DATUM aims to overcome major challenges when transitioning from classical to deep learning approaches. By carefully integrating the merits of classical multi-frame TM methods into a deep network structure we demonstrate that DATUM can efficiently perform long-range temporal aggregation using a recurrent fashion while deformable attention and temporal-channel attention seamlessly facilitate pixel registration and lucky imaging. With additional supervision tilt and blur degradation can be jointly mitigated. These inductive biases empower DATUM to significantly outperform existing methods while delivering a tenfold increase in processing speed. A large-scale training dataset ATSyn is presented as a co-invention to enable the generalization to real turbulence. Our code and datasets are available at https://xg416.github.io/DATUM/
In recent years automated Gallbladder Cancer (GBC) detection has gained the attention of researchers. Current state-of-the-art (SOTA) methodologies relying on ultrasound sonography (US) images exhibit limited generalization emphasizing the need for transformative approaches. We observe that individual US frames may lack sufficient information to capture disease manifestation. This study advocates for a paradigm shift towards video-based GBC detection leveraging the inherent advantages of spatiotemporal representations. Employing the Masked Autoencoder (MAE) for representation learning we address shortcomings in conventional image-based methods. We propose a novel design called FocusMAE to systematically bias the selection of masking tokens from high-information regions fostering a more refined representation of malignancy. Additionally we contribute the most extensive US video dataset for GBC detection. We also note that this is the first study on US video-based GBC detection. We validate the proposed methods on the curated dataset and report a new SOTA accuracy of 96.4% for the GBC detection problem against an accuracy of 84% by current Image-based SOTA - GBCNet and RadFormer and 94.7% by Video-based SOTA - AdaMAE.
We further demonstrate the generality of the proposed FocusMAE on a public CT-based Covid detection dataset reporting an improvement in accuracy by 3.3% over current baselines. Project page with source code trained models and data is available at: https://gbc-iitd.github.io/focusmae.