mtybilly/Instruct2DS huggingface dataset:
XScale-NVS: Cross-Scale Novel View Synthesis with Hash Featurized Manifold
Authors: Jinzhi Zhang, Fan Wang, Ruqi Huang, Lu Fang Guangyu Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose XScale-NVS for high-fidelity cross-scale novel view synthesis of real-world large-scale scenes. Existing representations based on explicit surface suffer from discretization resolution or UV distortion while implicit volumetric representations lack scalability for large scenes due to the dispersed weight distribution and surface ambiguity. In light of the above challenges we introduce hash featurized manifold a novel hash-based featurization coupled with a deferred neural rendering framework. This approach fully unlocks the expressivity of the representation by explicitly concentrating the hash entries on the 2D manifold thus effectively representing highly detailed contents independent of the discretization resolution. We also introduce a novel dataset namely GigaNVS to benchmark cross-scale high-resolution novel view synthesis of real-world large-scale scenes. Our method significantly outperforms competing baselines on various real-world scenes yielding an average LPIPS that is ?40% lower than prior state-of-the-art on the challenging GigaNVS benchmark. Please see our project page at: xscalenvs.github.io.
Ink Dot-Oriented Differentiable Optimization for Neural Image Halftoning
Authors: Bingfeng Zhou, Yadong Mu Hao Jiang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Halftoning is a time-honored printing technique that simulates continuous tones using ink dots (halftone dots). The resurgence of deep learning has catalyzed the emergence of innovative technologies in the printing industry fostering the advancement of data-driven halftoning methods. Nevertheless current deep learning-based approaches produce halftones through image-to-image black box transformations lacking direct control over the movement of individual halftone dots. In this paper we propose an innovative halftoning method termed "neural dot-controllable halftoning". This method allows dot-level image dithering by providing direct control over the motion of each ink dot. We conceptualize halftoning as the process of sprinkling dots on a canvas. Initially a specific quantity of dots are randomly dispersed on the canvas and subsequently adjusted based on the surrounding grayscale and gradient. To establish differentiable transformations between discrete ink dot positions and halftone matrices we devise a lightweight dot encoding network to spread dense gradients to sparse dots. Dot control offers several advantages to our approach including the capability to regulate the quantity of halftone dots and enhance specific areas with artifacts in the generated halftones by adjusting the placement of the dots. Our proposed method exhibits superior performance than previous approaches in extensive quantitative and qualitative experiments.
The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement
Authors: Carlo Masone, Barbara Caputo, Torsten Sattler Gabriele Trivigno
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Pose refinement is an interesting and practically relevant research direction. Pose refinement can be used to (1) obtain a more accurate pose estimate from an initial prior (e.g. from retrieval) (2) as pre-processing i.e. to provide a better starting point to a more expensive pose estimator (3) as post-processing of a more accurate localizer. Existing approaches focus on learning features / scene representations for the pose refinement task. This involves training an implicit scene representation or learning features while optimizing a camera pose-based loss. A natural question is whether training specific features / representations is truly necessary or whether similar results can be already achieved with more generic features. In this work we present a simple approach that combines pre-trained features with a particle filter and a renderable representation of the scene. Despite its simplicity it achieves state-of-the-art results demonstrating that one can easily build a pose refiner without the need for specific training. The code will be released upon acceptance.
Scalable 3D Registration via Truncated Entry-wise Absolute Residuals
Authors: Liangzu Peng, Rene Vidal, Yun-Hui Liu Tianyu Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Given an input set of 3D point pairs the goal of outlier-robust 3D registration is to compute some rotation and translation that align as many point pairs as possible. This is an important problem in computer vision for which many highly accurate approaches have been recently proposed. Despite their impressive performance these approaches lack scalability often overflowing the 16GB of memory of a standard laptop to handle roughly 30000 point pairs. In this paper we propose a 3D registration approach that can process more than ten million (10^7) point pairs with over 99% random outliers. Moreover our method is efficient entails low memory costs and maintains high accuracy at the same time. We call our method TEAR as it involves minimizing an outlier-robust loss that computes Truncated Entry-wise Absolute Residuals. To minimize this loss we decompose the original 6-dimensional problem into two subproblems of dimensions 3 and 2 respectively solved in succession to global optimality via a customized branch-and-bound method. While branch-and-bound is often slow and unscalable this does not apply to TEAR as we propose novel bounding functions that are tight and computationally efficient. Experiments on various datasets are conducted to validate the scalability and efficiency of our method.
ExtraNeRF: Visibility-Aware View Extrapolation of Neural Radiance Fields with Diffusion Models
Authors: Wei-Chiu Ma, Lorenzo Boyice, Aleksander Holynski, Forrester Cole, Brian Curless, Janne Kontkanen Meng-Li Shih
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose ExtraNeRF a novel method for extrapolating the range of views handled by a Neural Radiance Field (NeRF). Our main idea is to leverage NeRFs to model scene-specific fine-grained details while capitalizing on diffusion models to extrapolate beyond our observed data. A key ingredient is to track visibility to determine what portions of the scene have not been observed and focus on reconstructing those regions consistently with diffusion models. Our primary contributions include a visibility-aware diffusion-based inpainting module that is fine-tuned on the input imagery yielding an initial NeRF with moderate quality (often blurry) inpainted regions followed by a second diffusion model trained on the input imagery to consistently enhance notably sharpen the inpainted imagery from the first pass. We demonstrate high-quality results extrapolating beyond a small number of (typically six or fewer) input views effectively outpainting the NeRF as well as inpainting newly disoccluded regions inside the original viewing volume. We compare with related work both quantitatively and qualitatively and show significant gains over prior art.
Equivariant Plug-and-Play Image Reconstruction
Authors: Thomas Moreau, Nelly Pustelnik, Julian Tachella Matthieu Terris
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Plug-and-play algorithms constitute a popular framework for solving inverse imaging problems that rely on the implicit definition of an image prior via a denoiser. These algorithms can leverage powerful pre-trained denoisers to solve a wide range of imaging tasks circumventing the necessity to train models on a per-task basis. Unfortunately plug-and-play methods often show unstable behaviors hampering their promise of versatility and leading to suboptimal quality of reconstructed images. In this work we show that enforcing equivariance to certain groups of transformations (rotations reflections and/or translations) on the denoiser strongly improves the stability of the algorithm as well as its reconstruction quality. We provide a theoretical analysis that illustrates the role of equivariance on better performance and stability. We present a simple algorithm that enforces equivariance on any existing denoiser by simply applying a random transformation to the input of the denoiser and the inverse transformation to the output at each iteration of the algorithm. Experiments on multiple imaging modalities and denoising networks show that the equivariant plug-and-play algorithm improves both the reconstruction performance and the stability compared to their non-equivariant counterparts.
CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor
Authors: Runjia Li, Philip Torr, Xiuye Gu, Siyang Li Shuyang Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing open-vocabulary image segmentation methods require a fine-tuning step on mask labels and/or image-text datasets. Mask labels are labor-intensive which limits the number of categories in segmentation datasets. Consequently the vocabulary capacity of pre-trained VLMs is severely reduced after fine-tuning. However without fine-tuning VLMs trained under weak image-text supervision tend to make suboptimal mask predictions. To alleviate these issues we introduce a novel recurrent framework that progressively filters out irrelevant texts and enhances mask quality without training efforts. The recurrent unit is a two-stage segmenter built upon a frozen VLM. Thus our model retains the VLM's broad vocabulary space and equips it with segmentation ability. Experiments show that our method outperforms not only the training-free counterparts but also those fine-tuned with millions of data samples and sets the new state-of-the-art records for both zero-shot semantic and referring segmentation. Concretely we improve the current record by 28.8 16.0 and 6.9 mIoU on Pascal VOC COCO Object and Pascal Context.
LP++: A Surprisingly Strong Linear Probe for Few-Shot CLIP
Authors: Fereshteh Shakeri, Jose Dolz, Malik Boudiaf, Houda Bahig, Ismail Ben Ayed Yunshi Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In a recent strongly emergent literature on few-shot CLIP adaptation Linear Probe (LP) has been often reported as a weak baseline. This has motivated intensive research building convoluted prompt learning or feature adaptation strategies. In this work we propose and examine from convex-optimization perspectives a generalization of the standard LP baseline in which the linear classifier weights are learnable functions of the text embedding with class-wise multipliers blending image and text knowledge. As our objective function depends on two types of variables i.e. the class visual prototypes and the learnable blending parameters we propose a computationally efficient block coordinate Majorize-Minimize (MM) descent algorithm. In our full-batch MM optimizer which we coin LP++ step sizes are implicit unlike standard gradient descent practices where learning rates are intensively searched over validation sets. By examining the mathematical properties of our loss (e.g. Lipschitz gradient continuity) we build majorizing functions yielding data-driven learning rates and derive approximations of the loss's minima which provide data-informed initialization of the variables. Our image-language objective function along with these non-trivial optimization insights and ingredients yields surprisingly highly competitive few-shot CLIP performances. Furthermore LP++ operates in black-box relaxes intensive validation searches for the optimization hyper-parameters and runs orders-of-magnitudes faster than state-of-the-art few-shot CLIP adaptation methods. Our code is available at: https://github.com/FereshteShakeri/FewShot-CLIP-Strong-Baseline.git.
Active Generalized Category Discovery
Authors: Fei Zhu, Zhun Zhong, Xu-Yao Zhang, Cheng-Lin Liu Shijie Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task which endeavors to cluster unlabeled samples from both novel and old classes leveraging some labeled data of old classes. Given that knowledge learned from old classes is not fully transferable to new classes and that novel categories are fully unlabeled GCD inherently faces intractable problems including imbalanced classification performance and inconsistent confidence between old and new classes especially in the low-labeling regime. Hence some annotations of new classes are deemed necessary. However labeling new classes is extremely costly. To address this issue we take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD). The goal is to improve the performance of GCD by actively selecting a limited amount of valuable samples for labeling from the oracle. To solve this problem we devise an adaptive sampling strategy which jointly considers novelty informativeness and diversity to adaptively select novel samples with proper uncertainty. However owing to the varied orderings of label indices caused by the clustering of novel classes the queried labels are not directly applicable to subsequent training. To overcome this issue we further propose a stable label mapping algorithm that transforms ground truth labels to the label space of the classifier thereby ensuring consistent training across different active selection stages. Our method achieves state-of-the-art performance on both generic and fine-grained datasets. Our code is available at https://github.com/mashijie1028/ActiveGCD
HIVE: Harnessing Human Feedback for Instructional Visual Editing
Authors: Xinyi Yang, Yihao Feng, Can Qin, Chia-Chih Chen, Ning Yu, Zeyuan Chen, Huan Wang, Silvio Savarese, Stefano Ermon, Caiming Xiong, Ran Xu Shu Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences. We hypothesize that state-of-the-art instructional image editing models where outputs are generated based on an input image and an editing instruction could similarly benefit from human feedback as their outputs may not adhere to the correct instructions and preferences of users. In this paper we present a novel framework to harness human feedback for instructional visual editing (HIVE). Specifically we collect human feedback on the edited images and learn a reward function to capture the underlying user preferences. We then introduce scalable diffusion model fine-tuning methods that can incorporate human preferences based on the estimated reward. Besides to mitigate the bias brought by the limitation of data we contribute a new 1.1M training dataset a 3.6K reward dataset for rewards learning and a 1K evaluation dataset to boost the performance of instructional image editing. We conduct extensive empirical experiments quantitatively and qualitatively showing that HIVE is favored over previous state-of-the-art instructional image editing approaches by a large margin.
StrokeFaceNeRF: Stroke-based Facial Appearance Editing in Neural Radiance Field
Authors: Dingxi Zhang, Shu-Yu Chen, Feng-Lin Liu Xiao-Juan Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Current 3D-aware facial NeRF generation approaches control the facial appearance by text lighting conditions or reference images limiting precise manipulation of local facial regions and interactivity. Color stroke a user-friendly and effective tool to depict appearance is challenging to edit 3D faces because of the lack of texture coarse geometry representation and detailed editing operations. To solve the above problems we introduce StrokeFaceNeRF a novel stroke-based method for editing facial NeRF appearance. In order to infer the missing texture and 3D geometry information 2D edited stroke maps are firstly encoded into the EG3D's latent space followed by a transformer-based editing module to achieve effective appearance changes while preserving the original geometry in editing regions. Notably we design a novel geometry loss function to ensure surface density remains consistent during training. To further enhance the local manipulation accuracy we propose a stereo fusion approach which lifts the 2D mask (inferred from strokes or drawn by users) into 3D mask volume allowing explicit blending of the original and edited faces. Extensive experiments validate that the proposed method outperforms existing 2D and 3D methods in both editing reality and geometry retention.
FlowVQTalker: High-Quality Emotional Talking Face Generation through Normalizing Flow and Quantization
Authors: Bin Ji, Ye Pan Shuai Tan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Generating emotional talking faces is a practical yet challenging endeavor. To create a lifelike avatar we draw upon two critical insights from a human perspective: 1) The connection between audio and the non-deterministic facial dynamics encompassing expressions blinks poses should exhibit synchronous and one-to-many mapping. 2) Vibrant expressions are often accompanied by emotion-aware high-definition (HD) textures and finely detailed teeth. However both aspects are frequently overlooked by existing methods. To this end this paper proposes using normalizing Flow and Vector-Quantization modeling to produce emotional talking faces that satisfy both insights concurrently (FlowVQTalker). Specifically we develop a flowbased coefficient generator that encodes the dynamics of facial emotion into a multi-emotion-class latent space represented as a mixture distribution. The generation process commences with random sampling from the modeled distribution guided by the accompanying audio enabling both lip-synchronization and the uncertain nonverbal facial cues generation. Furthermore our designed vector-quantization image generator treats the creation of expressive facial images as a code query task utilizing a learned codebook to provide rich high-quality textures that enhance the emotional perception of the results. Extensive experiments are conducted to showcase the effectiveness of our approach.
Learning from Observer Gaze: Zero-Shot Attention Prediction Oriented by Human-Object Interaction Recognition
Authors: Linkai Liu, Chao Gou Yuchen Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Most existing attention prediction research focuses on salient instances like humans and objects. However the more complex interaction-oriented attention arising from the comprehension of interactions between instances by human observers remains largely unexplored. This is equally crucial for advancing human-machine interaction and human-centered artificial intelligence. To bridge this gap we first collect a novel gaze fixation dataset named IG comprising 530000 fixation points across 740 diverse interaction categories capturing visual attention during human observers' cognitive processes of interactions. Subsequently we introduce the zero-shot interaction-oriented attention prediction task (ZeroIA) which challenges models to predict visual cues for interactions not encountered during training. Thirdly we present the Interactive Attention model (IA) designed to emulate human observers' cognitive processes to tackle the ZeroIA problem. Extensive experiments demonstrate that the proposed IA outperforms other state-of-the-art approaches in both ZeroIA and fully supervised settings. Lastly we endeavor to apply interaction-oriented attention to the interaction recognition task itself. Further experimental results demonstrate the promising potential to enhance the performance and interpretability of existing state-of-the-art HOI models by incorporating real human attention data from IG and attention labels generated by IA.
ProxyCap: Real-time Monocular Full-body Capture in World Space via Human-Centric Proxy-to-Motion Learning
Authors: Hongwen Zhang, Liangxiao Hu, Jiajun Zhang, Hongwei Yi, Shengping Zhang, Yebin Liu Yuxiang Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Learning-based approaches to monocular motion capture have recently shown promising results by learning to regress in a data-driven manner. However due to the challenges in data collection and network designs it remains challenging to achieve real-time full-body capture while being accurate in world space. In this work we introduce ProxyCap a human-centric proxy-to-motion learning scheme to learn world-space motions from a proxy dataset of 2D skeleton sequences and 3D rotational motions. Such proxy data enables us to build a learning-based network with accurate world-space supervision while also mitigating the generalization issues. For more accurate and physically plausible predictions in world space our network is designed to learn human motions from a human-centric perspective which enables the understanding of the same motion captured with different camera trajectories. Moreover a contact-aware neural motion descent module is proposed to improve foot-ground contact and motion misalignment with the proxy observations. With the proposed learning-based solution we demonstrate the first real-time monocular full-body capture system with plausible foot-ground contact in world space even using hand-held cameras.
OpenBias: Open-set Bias Detection in Text-to-Image Generative Models
Authors: Elia Peruzzo, Massimiliano Mancini, Dejia Xu, Vidit Goel, Xingqian Xu, Zhangyang Wang, Humphrey Shi, Nicu Sebe Moreno D'Incà
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-to-image generative models are becoming increasingly popular and accessible to the general public. As these models see large-scale deployments it is necessary to deeply investigate their safety and fairness to not disseminate and perpetuate any kind of biases. However existing works focus on detecting closed sets of biases defined a priori limiting the studies to well-known concepts. In this paper we tackle the challenge of open-set bias detection in text-to-image generative models presenting OpenBias a new pipeline that identifies and quantifies the severity of biases agnostically without access to any precompiled set. OpenBias has three stages. In the first phase we leverage a Large Language Model (LLM) to propose biases given a set of captions. Secondly the target generative model produces images using the same set of captions. Lastly a Vision Question Answering model recognizes the presence and extent of the previously proposed biases. We study the behavior of Stable Diffusion 1.5 2 and XL emphasizing new biases never investigated before. Via quantitative experiments we demonstrate that OpenBias agrees with current closed-set bias detection methods and human judgement.
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation
Authors: Tejas Gokhale, Chitta Baral, Yezhou Yang Agneet Chatterjee
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advances in monocular depth estimation have been made by incorporating natural language as additional guidance. Although yielding impressive results the impact of the language prior particularly in terms of generalization and robustness remains unexplored. In this paper we address this gap by quantifying the impact of this prior and introduce methods to benchmark its effectiveness across various settings. We generate "low-level" sentences that convey object-centric three-dimensional spatial relationships incorporate them as additional language priors and evaluate their downstream impact on depth estimation. Our key finding is that current language-guided depth estimators perform optimally only with scene-level descriptions and counter-intuitively fare worse with low level descriptions. Despite leveraging additional data these methods are not robust to directed adversarial attacks and decline in performance with an increase in distribution shift. Finally to provide a foundation for future research we identify points of failures and offer insights to better understand these shortcomings. With an increasing number of methods using language for depth estimation our findings highlight the opportunities and pitfalls that require careful consideration for effective deployment in real-world settings.
UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs
Authors: Yang Zhao, Zhisheng Xiao, Tingbo Hou Yanwu Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-to-image diffusion models have demonstrated remarkable capabilities in transforming text prompts into coherent images yet the computational cost of the multi-step inference remains a persistent challenge. To address this issue we present UFOGen a novel generative model designed for ultra-fast one-step text-to-image generation. In contrast to conventional approaches that focus on improving samplers or employing distillation techniques for diffusion models UFOGen adopts a hybrid methodology integrating diffusion models with a GAN objective. Leveraging a newly introduced diffusion-GAN objective and initialization with pre-trained diffusion models UFOGen excels in efficiently generating high-quality images conditioned on textual descriptions in a single step. Beyond traditional text-to-image generation UFOGen showcases versatility in applications. Notably UFOGen stands among the pioneering models enabling one-step text-to-image generation and diverse downstream tasks presenting a significant advancement in the landscape of efficient generative models.
3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features
Authors: Huan Ling, Sanja Fidler, Or Litany Chenfeng Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 3DiffTection introduces a novel method for 3D object detection from single images utilizing a 3D-aware diffusion model for feature extraction. Addressing the resource-intensive nature of annotating large-scale 3D image data our approach leverages pretrained diffusion models traditionally used for 2D tasks and adapts them for 3D detection through geometric and semantic tuning. Geometrically we enhance the model to perform view synthesis from single images incorporating an epipolar warp operator. This process utilizes easily accessible posed image data eliminating the need for manual annotation. Semantically the model is further refined on target detection data. Both stages utilize ControlNet ensuring the preservation of original feature capabilities. Through our methodology we obtain 3D-aware features that excel in identifying cross-view point correspondences. In 3D detection 3DiffTection substantially surpasses previous benchmarks e.g. Cube-RCNN by 9.43% in AP3D on the Omni3D-ARkitscene dataset. Furthermore 3DiffTection demonstrates robust label efficiency and generalizes well to cross-domain data nearly matching fully-supervised models in zero-shot scenarios.
Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D
Authors: Peihao Wang, Zhiwen Fan, Zhangyang Wang, Hao Su, Ravi Ramamoorthi Mukund Varma T
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In recent years there has been an explosion of 2D vision models for numerous tasks such as semantic segmentation style transfer or scene editing enabled by large-scale 2D image datasets. At the same time there has been renewed interest in 3D scene representations such as neural radiance fields from multi-view images. However the availability of 3D or multiview data is still substantially limited compared to 2D image datasets making extending 2D vision models to 3D data highly desirable but also very challenging. Indeed extending a single 2D vision operator like scene editing to 3D typically requires a highly creative method specialized to that task and often requires per-scene optimization. In this paper we ask the question of whether any 2D vision model can be lifted to make 3D consistent predictions. We answer this question in the affirmative; our new Lift3D method trains to predict unseen views on feature spaces generated by a few visual models (i.e. DINO and CLIP) but then generalizes to novel vision operators and tasks such as style transfer super-resolution open vocabulary segmentation and image colorization; for some of these tasks there is no comparable previous 3D method. In many cases we even outperform state-of-the-art methods specialized for the task in question. Moreover Lift3D is a zero-shot method in the sense that it requires no task-specific training nor scene-specific optimization.
LowRankOcc: Tensor Decomposition and Low-Rank Recovery for Vision-based 3D Semantic Occupancy Prediction
Authors: Xiuwei Xu, Ziwei Wang, Yunpeng Zhang, Borui Zhang, Wenzhao Zheng, Dalong Du, Jie Zhou, Jiwen Lu Linqing Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we present a tensor decomposition and low-rank recovery approach (LowRankOcc) for vision-based 3D semantic occupancy prediction. Conventional methods model outdoor scenes with fine-grained 3D grids but the sparsity of non-empty voxels introduces considerable spatial redundancy leading to potential overfitting risks. In contrast our approach leverages the intrinsic low-rank property of 3D occupancy data factorizing voxel representations into low-rank components to efficiently mitigate spatial redundancy without sacrificing performance. Specifically we present the Vertical-Horizontal (VH) decomposition block factorizes 3D tensors into vertical vectors and horizontal matrices. With our "decomposition-encoding-recovery" framework we encode 3D contexts with only 1/2D convolutions and poolings and subsequently recover the encoded compact yet informative context features back to voxel representations. Experimental results demonstrate that LowRankOcc achieves state-of-the-art performances in semantic scene completion on the SemanticKITTI dataset and 3D occupancy prediction on the nuScenes dataset.
Multiway Point Cloud Mosaicking with Diffusion and Global Optimization
Authors: Iro Armeni, Marc Pollefeys, Daniel Barath Shengze Jin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce a novel framework for multiway point cloud mosaicking (named Wednesday) designed to co-align sets of partially overlapping point clouds -- typically obtained from 3D scanners or moving RGB-D cameras -- into a unified coordinate system. At the core of our approach is ODIN a learned pairwise registration algorithm that iteratively identifies overlaps and refines attention scores employing a diffusion-based process for denoising pairwise correlation matrices to enhance matching accuracy. Further steps include constructing a pose graph from all point clouds performing rotation averaging a novel robust algorithm for re-estimating translations optimally in terms of consensus maximization and translation optimization. Finally the point cloud rotations and positions are optimized jointly by a diffusion-based approach. Tested on four diverse large-scale datasets our method achieves state-of-the-art pairwise and multiway registration results by a large margin on all benchmarks. Our code and models are available at https://github.com/jinsz/Multiway-Point-Cloud-Mosaicking-with-Diffusion-and-Global-Optimization.
Novel View Synthesis with View-Dependent Effects from a Single Image
Authors: Munchurl Kim Juan Luis Gonzalez Bello
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we address single image-based novel view synthesis (NVS) by firstly integrating view-dependent effects (VDE) into the process. Our approach leverages camera motion priors to model VDE treating negative disparity as the representation of these effects in the scene. By identifying that specularities align with camera motion we infuse VDEs into input images by aggregating pixel colors along the negative depth region of epipolar lines. Additionally we introduce a relaxed volumetric rendering approximation enhancing efficiency by computing densities in a single pass for NVS from single images. Notably our method learns single-image NVS from image sequences alone making it a fully self-supervised learning approach that requires no depth or camera pose annotations. We present extensive experimental results and show that our proposed method can learn NVS with VDEs outperforming the SOTA single-view NVS methods on the RealEstate10k and MannequinChallenge datasets. Visit our project site https://kaist-viclab.github.io/monovde-site.
Point2RBox: Combine Knowledge from Synthetic Visual Patterns for End-to-end Oriented Object Detection with Single Point Supervision
Authors: Xue Yang, Qingyun Li, Feipeng Da, Jifeng Dai, Yu Qiao, Junchi Yan Yi Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: With the rapidly increasing demand for oriented object detection (OOD) recent research involving weakly-supervised detectors for learning rotated box (RBox) from the horizontal box (HBox) has attracted more and more attention. In this paper we explore a more challenging yet label-efficient setting namely single point-supervised OOD and present our approach called Point2RBox. Specifically we propose to leverage two principles: 1) Synthetic pattern knowledge combination: By sampling around each labeled point on the image we spread the object feature to synthetic visual patterns with known boxes to provide the knowledge for box regression. 2) Transform self-supervision: With a transformed input image (e.g. scaled/rotated) the output RBoxes are trained to follow the same transformation so that the network can perceive the relative size/rotation between objects. The detector is further enhanced by a few devised techniques to cope with peripheral issues e.g. the anchor/layer assignment as the size of the object is not available in our point supervision setting. To our best knowledge Point2RBox is the first end-to-end solution for point-supervised OOD. In particular our method uses a lightweight paradigm yet it achieves a competitive performance among point-supervised alternatives 41.05%/27.62%/80.01% on DOTA/DIOR/HRSC datasets.
PBWR: Parametric-Building-Wireframe Reconstruction from Aerial LiDAR Point Clouds
Authors: Ruisheng Wang, Bo Guo, Hongxin Yang Shangfeng Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we present an end-to-end 3D-building-wireframe reconstruction method to regress edges directly from aerial light-detection-and-ranging (LiDAR) point clouds. Our method named parametric-building-wireframe reconstruction (PBWR) takes aerial LiDAR point clouds and initial edge entities as input and fully uses the self-attention mechanism of transformers to regress edge parameters without any intermediate steps such as corner prediction. We propose an edge non-maximum suppression (E-NMS) module based on edge similarity to remove redundant edges. Additionally a dedicated edge loss function is utilized to guide the PBWR in regressing edges parameters when the simple use of the edge distance loss is not suitable. In our experiments our proposed method demonstrated state-of-the-art results on the Building3D dataset achieving an improvement of approximately 36% in Entry-level dataset edge accuracy and around a 42% improvement in the Tallinn dataset.
Spectrum AUC Difference (SAUCD): Human-aligned 3D Shape Evaluation
Authors: Zhong Li, Lele Chen, Xuan Gong, Lichang Chen, Yi Xu, Junsong Yuan Tianyu Luan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing 3D mesh shape evaluation metrics mainly focus on the overall shape but are usually less sensitive to local details. This makes them inconsistent with human evaluation as human perception cares about both overall and detailed shape. In this paper we propose an analytic metric named Spectrum Area Under the Curve Difference (SAUCD) that demonstrates better consistency with human evaluation. To compare the difference between two shapes we first transform the 3D mesh to the spectrum domain using the discrete Laplace-Beltrami operator and Fourier transform. Then we calculate the Area Under the Curve (AUC) difference between the two spectrums so that each frequency band that captures either the overall or detailed shape is equitably considered. Taking human sensitivity across frequency bands into account we further extend our metric by learning suitable weights for each frequency band which better aligns with human perception. To measure the performance of SAUCD we build a 3D mesh evaluation dataset called Shape Grading along with manual annotations from more than 800 subjects. By measuring the correlation between our metric and human evaluation we demonstrate that SAUCD is well aligned with human evaluation and outperforms previous 3D mesh metrics.
HRVDA: High-Resolution Visual Document Assistant
Authors: Kun Yin, Haoyu Cao, Xinghua Jiang, Xin Li, Yinsong Liu, Deqiang Jiang, Xing Sun, Linli Xu Chaohu Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Leveraging vast training data multimodal large language models (MLLMs) have demonstrated formidable general visual comprehension capabilities and achieved remarkable performance across various tasks. However their performance in visual document understanding still leaves much room for improvement. This discrepancy is primarily attributed to the fact that visual document understanding is a fine-grained prediction task. In natural scenes MLLMs typically use low-resolution images leading to a substantial loss of visual information. Furthermore general-purpose MLLMs do not excel in handling document-oriented instructions. In this paper we propose a High-Resolution Visual Document Assistant (HRVDA) which bridges the gap between MLLMs and visual document understanding. This model employs a content filtering mechanism and an instruction filtering module to separately filter out the content-agnostic visual tokens and instruction-agnostic visual tokens thereby achieving efficient model training and inference for high-resolution images. In addition we construct a document-oriented visual instruction tuning dataset and apply a multi-stage training strategy to enhance the model's document modeling capabilities. Extensive experiments demonstrate that our model achieves state-of-the-art performance across multiple document understanding datasets while maintaining training efficiency and inference speed comparable to low-resolution models.
Learning for Transductive Threshold Calibration in Open-World Recognition
Authors: Dongsheng An, Tianjun Xiao, Tong He, Qingming Tang, Ying Nian Wu, Joseph Tighe, Yifan Xing Qin Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In deep metric learning for visual recognition the calibration of distance thresholds is crucial for achieving desired model performance in the true positive rates (TPR) or true negative rates (TNR). However calibrating this thresh- old presents challenges in open-world scenarios where the test classes can be entirely disjoint from those encountered during training. We define the problem of finding distance thresholds for a trained embedding model to achieve target performance metrics over unseen open-world test classes as open-world threshold calibration. Existing posthoc threshold calibration methods reliant on inductive inference and requiring a calibration dataset with a similar distance distribution as the test data often prove ineffective in open- world scenarios. To address this we introduce OpenGCN a Graph Neural Network-based transductive threshold calibration method with enhanced adaptability and robustness. OpenGCN learns to predict pairwise connectivity for the unlabeled test instances embedded in a graph to determine its TPR and TNR at various distance thresholds allowing for transductive inference of the distance thresholds which also incorporates test-time information. Extensive experiments across open-world visual recognition benchmarks validate OpenGCN's superiority over existing posthoc calibration methods for open-world threshold calibration.
Weakly-Supervised Emotion Transition Learning for Diverse 3D Co-speech Gesture Generation
Authors: Jiahao Pan, Peng Li, Ruibin Yuan, Xiaowei Chi, Mengfei Li, Wenhan Luo, Wei Xue, Shanghang Zhang, Qifeng Liu, Yike Guo Xingqun Qi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Generating vivid and emotional 3D co-speech gestures is crucial for virtual avatar animation in human-machine interaction applications. While the existing methods enable generating the gestures to follow a single emotion label they overlook that long gesture sequence modeling with emotion transition is more practical in real scenes. In addition the lack of large-scale available datasets with emotional transition speech and corresponding 3D human gestures also limits the addressing of this task. To fulfill this goal we first incorporate the ChatGPT-4 and an audio inpainting approach to construct the high-fidelity emotion transition human speeches. Considering obtaining the realistic 3D pose annotations corresponding to the dynamically inpainted emotion transition audio is extremely difficult we propose a novel weakly supervised training strategy to encourage authority gesture transitions. Specifically to enhance the coordination of transition gestures w.r.t. different emotional ones we model the temporal association representation between two different emotional gesture sequences as style guidance and infuse it into the transition generation. We further devise an emotion mixture mechanism that provides weak supervision based on a learnable mixed emotion label for transition gestures. Last we present a keyframe sampler to supply effective initial posture cues in long sequences enabling us to generate diverse gestures. Extensive experiments demonstrate that our method outperforms the state-of-the-art models constructed by adapting single emotion-conditioned counterparts on our newly defined emotion transition task and datasets. Our code and dataset will be released on the project page: https://xingqunqi-lab.github.io/Emo-Transition-Gesture/
Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization
Authors: Jia Deng Lahav Lipson
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce a new system for Multi-Session SLAM which tracks camera motion across multiple disjoint videos under a single global reference. Our approach couples the prediction of optical flow with solver layers to estimate camera pose. The backbone is trained end-to-end using a novel differentiable solver for wide-baseline two-view pose. The full system can connect disjoint sequences perform visual odometry and global optimization. Compared to existing approaches our design is accurate and robust to catastrophic failures.
A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation
Authors: Ce Zheng, Chen Chen Qucheng Peng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 3D human pose data collected in controlled laboratory settings present challenges for pose estimators that generalize across diverse scenarios. To address this domain generalization is employed. Current methodologies in domain generalization for 3D human pose estimation typically utilize adversarial training to generate synthetic poses for training. Nonetheless these approaches exhibit several limitations. First the lack of prior information about the target domain complicates the application of suitable augmentation through a single pose augmentor affecting generalization on target domains. Moreover adversarial training's discriminator tends to enforce similarity between source and synthesized poses impeding the exploration of out-of-source distributions. Furthermore the pose estimator's optimization is not exposed to domain shifts limiting its overall generalization ability. To address these limitations we propose a novel framework featuring two pose augmentors: the weak and the strong augmentors. Our framework employs differential strategies for generation and discrimination processes facilitating the preservation of knowledge related to source poses and the exploration of out-of-source distributions without prior information about target poses. Besides we leverage meta-optimization to simulate domain shifts in the optimization process of the pose estimator thereby improving its generalization ability. Our proposed approach significantly outperforms existing methods as demonstrated through comprehensive experiments on various benchmark datasets.
Improving Out-of-Distribution Generalization in Graphs via Hierarchical Semantic Environments
Authors: Sangseon Lee, Yijingxiu Lu, Sun Kim Yinhua Piao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts. Recent methods attempt to enhance graph OOD generalization by generating flat environments. However such flat environments come with inherent limitations to capture more complex data distributions. Considering the DrugOOD dataset which contains diverse training environments (e.g. scaffold size etc.) flat contexts cannot sufficiently address its high heterogeneity. Thus a new challenge is posed to generate more semantically enriched environments to enhance graph invariant learning for handling distribution shifts. In this paper we propose a novel approach to generate hierarchical semantic environments for each graph. Firstly given an input graph we explicitly extract variant subgraphs from the input graph to generate proxy predictions on local environments. Then stochastic attention mechanisms are employed to re-extract the subgraphs for regenerating global environments in a hierarchical manner. In addition we introduce a new learning objective that guides our model to learn the diversity of environments within the same hierarchy while maintaining consistency across different hierarchies. This approach enables our model to consider the relationships between environments and facilitates robust graph invariant learning. Extensive experiments on real-world graph data have demonstrated the effectiveness of our framework. Particularly in the challenging dataset DrugOOD our method achieves up to 1.29% and 2.83% improvement over the best baselines on IC50 and EC50 prediction tasks respectively.