mtybilly/Instruct2DS huggingface dataset:
PostureHMR: Posture Transformation for 3D Human Mesh Recovery
Authors: Xiao Wu, Zhaoquan Yuan, Jian-Jun Qiao, Qiang Peng Yu-Pei Song
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Human Mesh Recovery (HMR) aims to estimate the 3D human body from 2D images which is a challenging task due to inherent ambiguities in translating 2D observations to 3D space. A novel approach called PostureHMR is proposed to leverage a multi-step diffusion-style process which converts this task into a posture transformation from an SMPL T-pose mesh to the target mesh. To inject the learning process of posture transformation with the physical structure of the human body model a kinematics-based forward process is proposed to interpolate the intermediate state with pose and shape decomposition. Moreover a mesh-to-posture (M2P) decoder is designed by combining the input of 3D and 2D mesh constraints estimated from the image to model the posture changes in the reverse process. It mitigates the difficulties of posture change learning directly from RGB pixels. To overcome the limitation of pixel-level misalignment of modeling results with the input image a new trimap-based rendering loss is designed to highlight the areas with poor recognition. Experiments conducted on three widely used datasets demonstrate that the proposed approach outperforms the state-of-the-art methods.
Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains
Authors: Phong Tran, Anh Tran, Cuong Pham, Rang Nguyen, Minh Hoai Bang-Dang Pham
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper presents an innovative framework designed to train an image deblurring algorithm tailored to a specific camera device. This algorithm works by transforming a blurry input image which is challenging to deblur into another blurry image that is more amenable to deblurring. The transformation process from one blurry state to another leverages unpaired data consisting of sharp and blurry images captured by the target camera device. Learning this blur-to-blur transformation is inherently simpler than direct blur-to-sharp conversion as it primarily involves modifying blur patterns rather than the intricate task of reconstructing fine image details. The efficacy of the proposed approach has been demonstrated through comprehensive experiments on various benchmarks where it significantly outperforms state-of-the-art methods both quantitatively and qualitatively. Our code and data are available at https://github.com/VinAIResearch/Blur2Blur
Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis
Authors: Dingkang Liang, Wei Xu, Xingkui Zhu, Yihan Xu, Zhikang Zou, Xiang Bai Xin Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Point cloud analysis has achieved outstanding performance by transferring point cloud pre-trained models. However existing methods for model adaptation usually update all model parameters i.e. full fine-tuning paradigm which is inefficient as it relies on high computational costs (e.g. training GPU memory) and massive storage space. In this paper we aim to study parameter-efficient transfer learning for point cloud analysis with an ideal trade-off between task performance and parameter efficiency. To achieve this goal we freeze the parameters of the default pre-trained models and then propose the Dynamic Adapter which generates a dynamic scale for each token considering the token significance to the downstream task. We further seamlessly integrate Dynamic Adapter with Prompt Tuning (DAPT) by constructing Internal Prompts capturing the instance-specific features for interaction. Extensive experiments conducted on five challenging datasets demonstrate that the proposed DAPT achieves superior performance compared to the full fine-tuning counterparts while significantly reducing the trainable parameters and training GPU memory by 95% and 35% respectively. Code is available at https://github.com/LMD0311/DAPT.
Exploring Vision Transformers for 3D Human Motion-Language Models with Motion Patches
Authors: Mikihiro Tanaka, Kent Fujiwara Qing Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: To build a cross-modal latent space between 3D human motion and language acquiring large-scale and high-quality human motion data is crucial. However unlike the abundance of image data the scarcity of motion data has limited the performance of existing motion-language models. To counter this we introduce "motion patches" a new representation of motion sequences and propose using Vision Transformers (ViT) as motion encoders via transfer learning aiming to extract useful knowledge from the image domain and apply it to the motion domain. These motion patches created by dividing and sorting skeleton joints based on body parts in motion sequences are robust to varying skeleton structures and can be regarded as color image patches in ViT. We find that transfer learning with pre-trained weights of ViT obtained through training with 2D image data can boost the performance of motion analysis presenting a promising direction for addressing the issue of limited motion data. Our extensive experiments show that the proposed motion patches used jointly with ViT achieve state-of-the-art performance in the benchmarks of text-to-motion retrieval and other novel challenging tasks such as cross-skeleton recognition zero-shot motion classification and human interaction recognition which are currently impeded by the lack of data.
Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring
Authors: Xuan Wang, Xiangyu Xu, Ruhao Tian, Shuai Li, Xueming Qian, Ming-Hsuan Yang Chengxu Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Eliminating image blur produced by various kinds of motion has been a challenging problem. Dominant approaches rely heavily on model capacity to remove blurring by reconstructing residual from blurry observation in feature space. These practices not only prevent the capture of spatially variable motion in the real world but also ignore the tailored handling of various motions in image space. In this paper we propose a novel real-world deblurring filtering model called the Motion-adaptive Separable Collaborative (MISC) Filter. In particular we use a motion estimation network to capture motion information from neighborhoods thereby adaptively estimating spatially-variant motion flow mask kernels weights and offsets to obtain the MISC Filter. The MISC Filter first aligns the motion-induced blurring patterns to the motion middle along the predicted flow direction and then collaboratively filters the aligned image through the predicted kernels weights and offsets to generate the output. This design can handle more generalized and complex motion in a spatially differentiated manner. Furthermore we analyze the relationships between the motion estimation network and the residual reconstruction network. Extensive experiments on four widely used benchmarks demonstrate that our method provides an effective solution for real-world motion blur removal and achieves state-of-the-art performance. Code is available at https://github.com/ChengxuLiu/MISCFilter.
DART: Implicit Doppler Tomography for Radar Novel View Synthesis
Authors: John Miller, Akarsh Prabhakara, Tao Jin, Tarana Laroia, Zico Kolter, Anthony Rowe Tianshu Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Simulation is an invaluable tool for radio-frequency system designers that enables rapid prototyping of various algorithms for imaging target detection classification and tracking. However simulating realistic radar scans is a challenging task that requires an accurate model of the scene radio frequency material properties and a corresponding radar synthesis function. Rather than specifying these models explicitly we propose DART - Doppler Aided Radar Tomography a Neural Radiance Field-inspired method which uses radar-specific physics to create a reflectance and transmittance-based rendering pipeline for range-Doppler images. We then evaluate DART by constructing a custom data collection platform and collecting a novel radar dataset together with accurate position and instantaneous velocity measurements from lidar-based localization. In comparison to state-of-the-art baselines DART synthesizes superior radar range-Doppler images from novel views across all datasets and additionally can be used to generate high quality tomographic images.
Wonder3D: Single Image to 3D using Cross-Domain Diffusion
Authors: Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, Wenping Wang Xiaoxiao Long
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this work we introduce Wonder3D a novel method for generating high-fidelity textured meshes from single-view images with remarkable efficiency. Recent methods based on the Score Distillation Sampling (SDS) loss methods have shown the potential to recover 3D geometry from 2D diffusion priors but they typically suffer from time-consuming per-shape optimization and inconsistent geometry. In contrast certain works directly produce 3D information via fast network inferences but their results are often of low quality and lack geometric details. To holistically improve the quality consistency and efficiency of image-to-3D tasks we propose a cross-domain diffusion model that generates multi-view normal maps and the corresponding color images. To ensure consistency we employ a multi-view cross-domain attention mechanism that facilitates information exchange across views and modalities. Lastly we introduce a geometry-aware normal fusion algorithm that extracts high-quality surfaces from the multi-view 2D representations in only 2 3 minutes. Our extensive evaluations demonstrate that our method achieves high-quality reconstruction results robust generalization and remarkable efficiency compared to prior works.
Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal
Authors: Hongtao Wu, Angelica I. Aviles-Rivero, Yulun Zhang, Jing Qin, Lei Zhu Yijun Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Real-world vision tasks frequently suffer from the appearance of unexpected adverse weather conditions including rain haze snow and raindrops. In the last decade convolutional neural networks and vision transformers have yielded outstanding results in single-weather video removal. However due to the absence of appropriate adaptation most of them fail to generalize to other weather conditions. Although ViWS-Net is proposed to remove adverse weather conditions in videos with a single set of pre-trained weights it is seriously blinded by seen weather at train-time and degenerates when coming to unseen weather during test-time. In this work we introduce test-time adaptation into adverse weather removal in videos and propose the first framework that integrates test-time adaptation into the iterative diffusion reverse process. Specifically we devise a diffusion-based network with a novel temporal noise model to efficiently explore frame-correlated information in degraded video clips at training stage. During inference stage we introduce a proxy task named Diffusion Tubelet Self-Calibration to learn the primer distribution of test video stream and optimize the model by approximating the temporal noise model for online adaptation. Experimental results on benchmark datasets demonstrate that our Test-Time Adaptation method with Diffusion-based network(Diff-TTA) outperforms state-of-the-art methods in terms of restoring videos degraded by seen weather conditions. Its generalizable capability is validated with unseen weather conditions in synthesized and real-world videos.
Gradient-based Parameter Selection for Efficient Fine-Tuning
Authors: Qizhe Zhang, Zijun Gao, Renrui Zhang, Ekaterina Shutova, Shiji Zhou, Shanghang Zhang Zhi Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: With the growing size of pre-trained models full fine-tuning and storing all the parameters for various downstream tasks is costly and infeasible. In this paper we propose a new parameter-efficient fine-tuning method Gradient-based Parameter Selection (GPS) demonstrating that only tuning a few selected parameters from the pre-trained model while keeping the remainder of the model frozen can generate similar or better performance compared with the full model fine-tuning method. Different from the existing popular and state-of-the-art parameter-efficient fine-tuning approaches our method does not introduce any additional parameters and computational costs during both the training and inference stages. Another advantage is the model-agnostic and non-destructive property which eliminates the need for any other design specific to a particular model. Compared with the full fine-tuning GPS achieves 3.33% (91.78% vs. 88.45% FGVC) and 9.61% (73.1% vs. 65.57% VTAB) improvement of the accuracy with tuning only 0.36% parameters of the pre-trained model on average over 24 image classification tasks; it also demonstrates a significant improvement of 17% and 16.8% in mDice and mIoU respectively on medical image segmentation task. Moreover GPS achieves state-of-the-art performance compared with existing PEFT methods. The code will be available in https://github.com/FightingFighting/GPS.git.
Clustering for Protein Representation Learning
Authors: Wenguan Wang, Fan Ma, Hehe Fan, Yi Yang Ruijie Quan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Protein representation learning is a challenging task that aims to capture the structure and function of proteins from their amino acid sequences. Previous methods largely ignored the fact that not all amino acids are equally important for protein folding and activity. In this article we propose a neural clustering framework that can automatically discover the critical components of a protein by considering both its primary and tertiary structure information. Our framework treats a protein as a graph where each node represents an amino acid and each edge represents a spatial or sequential connection between amino acids. We then apply an iterative clustering strategy to group the nodes into clusters based on their 1D and 3D positions and assign scores to each cluster. We select the highest-scoring clusters and use their medoid nodes for the next iteration of clustering until we obtain a hierarchical and informative representation of the protein. We evaluate on four protein-related tasks: protein fold classification enzyme reaction classification gene ontology term prediction and enzyme commission number prediction. Experimental results demonstrate that our method achieves state-of-the-art performance.
CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
Authors: Yuqi Yang, Le Zhang, Ming-Ming Cheng, Qibin Hou Boyuan Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper presents a simple but performant semi-supervised semantic segmentation approach called CorrMatch. Previous approaches mostly employ complicated training strategies to leverage unlabeled data but overlook the role of correlation maps in modeling the relationships between pairs of locations. We observe that the correlation maps not only enable clustering pixels of the same category easily but also contain good shape information which previous works have omitted. Motivated by these we aim to improve the use efficiency of unlabeled data by designing two novel label propagation strategies. First we propose to conduct pixel propagation by modeling the pairwise similarities of pixels to spread the high-confidence pixels and dig out more. Then we perform region propagation to enhance the pseudo labels with accurate class-agnostic masks extracted from the correlation maps. CorrMatch achieves great performance on popular segmentation benchmarks. Taking the DeepLabV3+ with ResNet-101 backbone as our segmentation model we receive a 76%+ mIoU score on the Pascal VOC 2012 dataset with only 92 annotated images. Code is available at https://github.com/BBBBchan/CorrMatch .
Estimating Extreme 3D Image Rotations using Cascaded Attention
Authors: Yosi Keller, Martin Cadik Shay Dekel
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Estimating large extreme inter-image rotations is critical for numerous computer vision domains involving images related by limited or non-overlapping fields of view. In this work we propose an attention-based approach with a pipeline of novel algorithmic components. First as rotation estimation pertains to image pairs we introduce an inter-image distillation scheme using Decoders to improve embeddings. Second whereas contemporary methods compute a 4D correlation volume (4DCV) encoding inter-image relationships we propose an Encoder-based cross-attention approach between activation maps to compute an enhanced equivalent of the 4DCV. Finally we present a cascaded Decoder-based technique for alternately refining the cross-attention and the rotation query. Our approach outperforms current state-of-the-art methods on extreme rotation estimation. We make our code publicly available.
RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D
Authors: Guanying Chen, Xiaodong Gu, Qi Zuo, Mutian Xu, Yushuang Wu, Weihao Yuan, Zilong Dong, Liefeng Bo, Xiaoguang Han Lingteng Qiu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Lifting 2D diffusion for 3D generation is a challenging problem due to the lack of geometric prior and the complex entanglement of materials and lighting in natural images. Existing methods have shown promise by first creating the geometry through score-distillation sampling (SDS) applied to rendered surface normals followed by appearance modeling. However relying on a 2D RGB diffusion model to optimize surface normals is suboptimal due to the distribution discrepancy between natural images and normals maps leading to instability in optimization. In this paper recognizing that the normal and depth information effectively describe scene geometry and be automatically estimated from images we propose to learn a generalizable Normal-Depth diffusion model for 3D generation. We achieve this by training on the large-scale LAION dataset together with the generalizable image-to-depth and normal prior models. In an attempt to alleviate the mixed illumination effects in the generated materials we introduce an albedo diffusion model to impose data-driven constraints on the albedo component. Our experiments show that when integrated into existing text-to-3D pipelines our models significantly enhance the detail richness achieving state-of-the-art results. Our project page is at https://aigc3d.github.io/richdreamer/.
Adapt or Perish: Adaptive Sparse Transformer with Attentive Feature Refinement for Image Restoration
Authors: Duosheng Chen, Jinshan Pan, Jinglei Shi, Jufeng Yang Shihao Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Transformer-based approaches have achieved promising performance in image restoration tasks given their ability to model long-range dependencies which is crucial for recovering clear images. Though diverse efficient attention mechanism designs have addressed the intensive computations associated with using transformers they often involve redundant information and noisy interactions from irrelevant regions by considering all available tokens. In this work we propose an Adaptive Sparse Transformer (AST) to mitigate the noisy interactions of irrelevant areas and remove feature redundancy in both spatial and channel domains. AST comprises two core designs i.e. an Adaptive Sparse Self-Attention (ASSA) block and a Feature Refinement Feed-forward Network (FRFN). Specifically ASSA is adaptively computed using a two-branch paradigm where the sparse branch is introduced to filter out the negative impacts of low query-key matching scores for aggregating features while the dense one ensures sufficient information flow through the network for learning discriminative representations. Meanwhile FRFN employs an enhance-and-ease scheme to eliminate feature redundancy in channels enhancing the restoration of clear latent images. Experimental results on commonly used benchmarks have demonstrated the versatility and competitive performance of our method in several tasks including rain streak removal real haze removal and raindrop removal. The code and pre-trained models are available at https://github.com/joshyZhou/AST.
VINECS: Video-based Neural Character Skinning
Authors: Vladislav Golyanik, Marc Habermann, Christian Theobalt Zhouyingcheng Liao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Rigging and skinning clothed human avatars is a challenging task and traditionally requires a lot of manual work and expertise. Recent methods addressing it either generalize across different characters or focus on capturing the dynamics of a single character observed under different pose configurations. However the former methods typically predict solely static skinning weights which perform poorly for highly articulated poses and the latter ones either require dense 3D character scans in different poses or cannot generate an explicit mesh with vertex correspondence over time. To address these challenges we propose a fully automated approach for creating a fully rigged character with pose-dependent skinning weights which can be solely learned from multi-view video. Therefore we first acquire a rigged template which is then statically skinned. Next a coordinate-based MLP learns a skinning weights field parameterized over the position in a canonical pose space and the respective pose. Moreover we introduce our pose- and view-dependent appearance field allowing us to differentiably render and supervise the posed mesh using multi-view imagery. We show that our approach outperforms state-of-the-art while not relying on dense 4D scans. More details can be found on our project page.
Zero-shot Referring Expression Comprehension via Structural Similarity Between Images and Captions
Authors: Fangrui Zhu, Qianru Lao, Huaizu Jiang Zeyu Han
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Zero-shot referring expression comprehension aims at localizing bounding boxes in an image corresponding to provided textual prompts which requires: (i) a fine-grained disentanglement of complex visual scene and textual context and (ii) a capacity to understand relationships among disentangled entities. Unfortunately existing large vision-language alignment (VLA) models e.g. CLIP struggle with both aspects so cannot be directly used for this task. To mitigate this gap we leverage large foundation models to disentangle both images and texts into triplets in the format of (subject predicate object). After that grounding is accomplished by calculating the structural similarity matrix between visual and textual triplets with a VLA model and subsequently propagate it to an instance-level similarity matrix. Furthermore to equip VLA models with the ability of relationship understanding we design a triplet-matching objective to fine-tune the VLA models on a collection of curated dataset containing abundant entity relationships. Experiments demonstrate that our visual grounding performance increase of up to 19.5% over the SOTA zero-shot model on RefCOCO/+/g. On the more challenging Who's Waldo dataset our zero-shot approach achieves comparable accuracy to the fully supervised model. Code is available at https://github.com/Show-han/Zeroshot_REC.
Domain Prompt Learning with Quaternion Networks
Authors: Zhengqin Xu, Yuntian Chen, Chao Ma, Xiaokang Yang Qinglong Cao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Prompt learning has emerged as an effective and data-efficient technique in large Vision-Language Models (VLMs). However when adapting VLMs to specialized domains such as remote sensing and medical imaging domain prompt learning remains underexplored. While large-scale domain-specific foundation models can help tackle this challenge their concentration on a single vision level makes it challenging to prompt both vision and language modalities. To overcome this we propose to leverage domain-specific knowledge from domain-specific foundation models to transfer the robust recognition ability of VLMs from generalized to specialized domains using quaternion networks. Specifically the proposed method involves using domain-specific vision features from domain-specific foundation models to guide the transformation of generalized contextual embeddings from the language branch into a specialized space within the quaternion networks. Moreover we present a hierarchical approach that generates vision prompt features by analyzing intermodal relationships between hierarchical language prompt features and domain-specific vision features. In this way quaternion networks can effectively mine the intermodal relationships in the specific domain facilitating domain-specific vision-language contrastive learning. Extensive experiments on domain-specific datasets show that our proposed method achieves new state-of-the-art results in prompt learning.
BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation
Authors: Yihe Tang, Jiashu Xu, Cem Gokmen, Chengshu Li, Wensi Ai, Benjamin Jose Martinez, Arman Aydin, Mona Anvari, Ayush K Chakravarthy, Hong-Xing Yu, Josiah Wong, Sanjana Srivastava, Sharon Lee, Shengxin Zha, Laurent Itti, Yunzhu Li, Roberto Martín-Martín, Miao Liu, Pengchuan Zhang, Ruohan Zhang, Li Fei-Fei, Jiajun Wu Yunhao Ge
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels which real-world vision datasets rarely satisfy. While current synthetic data generators offer a promising alternative particularly for embodied AI tasks they often fall short for computer vision tasks due to low asset and rendering quality limited diversity and unrealistic physical properties. We introduce the BEHAVIOR Vision Suite (BVS) a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models based on the newly developed embodied AI benchmark BEHAVIOR-1K. BVS supports a large number of adjustable parameters at the scene level (e.g. lighting object placement) the object level (e.g. joint configuration attributes such as "filled" and "folded") and the camera level (e.g. field of view focal length). Researchers can arbitrarily vary these parameters during data generation to perform controlled experiments. We showcase three example application scenarios: systematically evaluating the robustness of models across different continuous axes of domain shift evaluating scene understanding models on the same set of images and training and evaluating simulation-to-real transfer for a novel vision task: unary and binary state prediction. Project website: https://behavior-vision-suite.github.io/
Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers
Authors: Zhipeng Yu, Yuan-Chen Guo, Yangguang Li, Ding Liang, Yan-Pei Cao, Song-Hai Zhang Zi-Xin Zou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advancements in 3D reconstruction from single images have been driven by the evolution of generative models. Prominent among these are methods based on Score Distillation Sampling (SDS) and the adaptation of diffusion models in the 3D domain. Despite their progress these techniques often face limitations due to slow optimization or rendering processes leading to extensive training and optimization times. In this paper we introduce a novel approach for single-view reconstruction that efficiently generates a 3D model from a single image via feed-forward inference. Our method utilizes two transformer-based networks namely a point decoder and a triplane decoder to reconstruct 3D objects using a hybrid Triplane-Gaussian intermediate representation. This hybrid representation strikes a balance achieving a faster rendering speed compared to implicit representations while simultaneously delivering superior rendering quality than explicit representations. The point decoder is designed for generating point clouds from single images offering an explicit representation which is then utilized by the triplane decoder to query Gaussian features for each point. This design choice addresses the challenges associated with directly regressing explicit 3D Gaussian attributes characterized by their non-structural nature. Subsequently the 3D Gaussians are decoded by an MLP to enable rapid rendering through splatting. Both decoders are built upon a scalable transformer-based architecture and have been efficiently trained on large-scale 3D datasets. The evaluations conducted on both synthetic datasets and real-world images demonstrate that our method not only achieves higher quality but also ensures a faster runtime in comparison to previous state-of-the-art techniques. Please see our project page at https://zouzx.github.io/TriplaneGaussian/
WateRF: Robust Watermarks in Radiance Fields for Protection of Copyrights
Authors: Dong In Lee, MinHyuk Jang, Jong Wook Kim, Feng Yang, Sangpil Kim Youngdong Jang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The advances in the Neural Radiance Fields (NeRF) research offer extensive applications in diverse domains but protecting their copyrights has not yet been researched in depth. Recently NeRF watermarking has been considered one of the pivotal solutions for safely deploying NeRF-based 3D representations. However existing methods are designed to apply only to implicit or explicit NeRF representations. In this work we introduce an innovative watermarking method that can be employed in both representations of NeRF. This is achieved by fine-tuning NeRF to embed binary messages in the rendering process. In detail we propose utilizing the discrete wavelet transform in the NeRF space for watermarking. Furthermore we adopt a deferred back-propagation technique and introduce a combination with the patch-wise loss to improve rendering quality and bit accuracy with minimum trade-offs. We evaluate our method in three different aspects: capacity invisibility and robustness of the embedded watermarks in the 2D-rendered images. Our method achieves state-of-the-art performance with faster training speed over the compared state-of-the-art methods. Project page: https://kuai-lab.github.io/cvpr2024waterf/
Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle
Authors: Zuozhuo Dai, Siyu Zhu, Yao Yao Youtian Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce Gaussian-Flow a novel point-based approach for fast dynamic scene reconstruction and real-time rendering from both multi-view and monocular videos. In contrast to the prevalent NeRF-based approaches hampered by slow training and rendering speeds our approach harnesses recent advancements in point-based 3D Gaussian Splatting (3DGS). Specifically a novel Dual-Domain Deformation Model (DDDM) is proposed to explicitly model attribute deformations of each Gaussian point where the time-dependent residual of each attribute is captured by a polynomial fitting in the time domain and a Fourier series fitting in the frequency domain. The proposed DDDM is capable of modeling complex scene deformations across long video footage eliminating the need for training separate 3DGS for each frame or introducing an additional implicit neural field to model 3D dynamics. Moreover the explicit deformation modeling for discretized Gaussian points ensures ultra-fast training and rendering of a 4D scene which is comparable to the original 3DGS designed for static 3D reconstruction. Our proposed approach showcases a substantial efficiency improvement achieving a 5xfaster training speed compared to the per-frame 3DGS modeling. In addition quantitative results demonstrate that the proposed Gaussian-Flow significantly outperforms previous leading methods in novel view rendering quality.
Your Student is Better Than Expected: Adaptive Teacher-Student Collaboration for Text-Conditional Diffusion Models
Authors: Dmitry Baranchuk, Artem Fedorov, Artem Babenko Nikita Starodubcev
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Knowledge distillation methods have recently shown to be a promising direction to speedup the synthesis of large-scale diffusion models by requiring only a few inference steps. While several powerful distillation methods were recently proposed the overall quality of student samples is typically lower compared to the teacher ones which hinders their practical usage. In this work we investigate the relative quality of samples produced by the teacher text-to-image diffusion model and its distilled student version. As our main empirical finding we discover that a noticeable portion of student samples exhibit superior fidelity compared to the teacher ones despite the approximate nature of the student. Based on this finding we propose an adaptive collaboration between student and teacher diffusion models for effective text-to-image synthesis. Specifically the distilled model produces an initial image sample and then an oracle decides whether it needs further improvements with the teacher model. Extensive experiments demonstrate that the designed pipeline surpasses state-of-the-art text-to-image alternatives for various inference budgets in terms of human preference. Furthermore the proposed approach can be naturally used in popular applications such as text-guided image editing and controllable generation.
DiVAS: Video and Audio Synchronization with Dynamic Frame Rates
Authors: Mertcan Akçay, Eitan Abecassis, Joan Massich, Christopher Schroers Clara Fernandez-Labrador
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Synchronization issues between audio and video are one of the most disturbing quality defects in film production and live broadcasting. Even a discrepancy as short as 45 millisecond can degrade the viewer's experience enough to warrant manual quality checks over entire movies. In this paper we study the automatic discovery of such issues. Specifically we focus on the alignment of lip movements with spoken words targeting realistic production scenarios which can include background noise and music intricate head poses excessive makeup or scenes with multiple individuals where the speaker is unknown. Our model's robustness also extends to various media specifications including different video frame rates and audio sample rates. To address these challenges we present a model fully based on transformers that encodes face crops or full video frames and raw audio using timestamp information identifies the speaker and provides highly accurate synchronization predictions much faster than previous methods.
SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design
Authors: Youngmin Ro Seokju Yun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently efficient Vision Transformers have shown great performance with low latency on resource-constrained devices. Conventionally they use 4x4 patch embeddings and a 4-stage structure at the macro level while utilizing sophisticated attention with multi-head configuration at the micro level. This paper aims to address computational redundancy at all design levels in a memory-efficient manner. We discover that using larger-stride patchify stem not only reduces memory access costs but also achieves competitive performance by leveraging token representations with reduced spatial redundancy from the early stages. Furthermore our preliminary analyses suggest that attention layers in the early stages can be substituted with convolutions and several attention heads in the latter stages are computationally redundant. To handle this we introduce a single-head attention module that inherently prevents head redundancy and simultaneously boosts accuracy by parallelly combining global and local information. Building upon our solutions we introduce SHViT a Single-Head Vision Transformer that obtains the state-of-the-art speed-accuracy tradeoff. For example on ImageNet-1k our SHViT-S4 is 3.3x 8.1x and 2.4x faster than MobileViTv2x1.0 on GPU CPU and iPhone12 mobile device respectively while being 1.3% more accurate. For object detection and instance segmentation on MS COCO using Mask-RCNN head our model achieves performance comparable to FastViT-SA12 while exhibiting 3.8x and 2.0x lower backbone latency on GPU and mobile device respectively.
HDRFlow: Real-Time HDR Video Reconstruction with Large Motions
Authors: Yujin Wang, Jinwei Gu, Tianfan Xue, Xin Yang Gangwei Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Reconstructing High Dynamic Range (HDR) video from image sequences captured with alternating exposures is challenging especially in the presence of large camera or object motion. Existing methods typically align low dynamic range sequences using optical flow or attention mechanism for deghosting. However they often struggle to handle large complex motions and are computationally expensive. To address these challenges we propose a robust and efficient flow estimator tailored for real-time HDR video reconstruction named HDRFlow. HDRFlow has three novel designs: an HDR-domain alignment loss (HALoss) an efficient flow network with a multi-size large kernel (MLK) and a new HDR flow training scheme. The HALoss supervises our flow network to learn an HDR-oriented flow for accurate alignment in saturated and dark regions. The MLK can effectively model large motions at a negligible cost. In addition we incorporate synthetic data Sintel into our training dataset utilizing both its provided forward flow and backward flow generated by us to supervise our flow network enhancing our performance in large motion regions. Extensive experiments demonstrate that our HDRFlow outperforms previous methods on standard benchmarks. To the best of our knowledge HDRFlow is the first real-time HDR video reconstruction method for video sequences captured with alternating exposures capable of processing 720p resolution inputs at 25ms.
SPIDeRS: Structured Polarization for Invisible Depth and Reflectance Sensing
Authors: Shohei Nobuhara, Ko Nishino Tomoki Ichikawa
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Can we capture shape and reflectance in stealth? Such capability would be valuable for many application domains in vision xR robotics and HCI. We introduce structured polarization for invisible depth and reflectance sensing (SPIDeRS) the first depth and reflectance sensing method using patterns of polarized light. The key idea is to modulate the angle of linear polarization (AoLP) of projected light at each pixel. The use of polarization makes it invisible and lets us recover not only depth but also directly surface normals and even reflectance. We implement SPIDeRS with a liquid crystal spatial light modulator (SLM) and a polarimetric camera. We derive a novel method for robustly extracting the projected structured polarization pattern from the polarimetric object appearance. We evaluate the effectiveness of SPIDeRS by applying it to a number of real-world objects. The results show that our method successfully reconstructs object shapes of various materials and is robust to diffuse reflection and ambient light. We also demonstrate relighting using recovered surface normals and reflectance. We believe SPIDeRS opens a new avenue of polarization use in visual sensing.
SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration
Authors: Takafumi Taketomi Xu Cao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Instance-aware Contrastive Learning for Occluded Human Mesh Reconstruction
Authors: Gi-Mun Um, Won-Sik Cheong, Wonjun Kim Mi-Gyeong Gwon
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: A simple yet effective method for occlusion-robust 3D human mesh reconstruction from a single image is presented in this paper. Although many recent studies have shown the remarkable improvement in human mesh reconstruction it is still difficult to generate accurate meshes when person-to-person occlusion occurs due to the ambiguity of who a body part belongs to. To address this problem we propose an instance-aware contrastive learning scheme. Specifically joint features belonging to the target human are trained to be proximate with the anchor feature (i.e. feature extracted from the body center position). On the other hand anchor features of different human instances are forced to be far apart so that joint features of each person can be clearly distinguished from others. By interpreting the joint possession based on such contrastive learning scheme the proposed method easily understands the spatial occupancy of body parts for each person in a given image thus can reconstruct reliable human meshes even with severely overlapped cases between multiple persons. Experimental results on benchmark datasets demonstrate the robustness of the proposed method compared to previous approaches under person-to-person occlusions. The code and model are publicly available at: https://github.com/DCVL-3D/InstanceHMR_release.
ADFactory: An Effective Framework for Generalizing Optical Flow with NeRF
Authors: Quansen Sun, Yinghui Sun, Xian Xu, Xinfeng Li Han Ling
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: A significant challenge facing current optical flow methods is the difficulty in generalizing them well to the real world. This is mainly due to the lack of large-scale real-world datasets and existing self-supervised methods are limited by indirect loss and occlusions resulting in fuzzy outcomes. To address this challenge we introduce a novel optical flow training framework: automatic data factory (ADF). ADF only requires RGB images as input to effectively train the optical flow network on the target data domain. Specifically we use advanced NeRF technology to reconstruct scenes from photo groups collected by a monocular camera and then calculate optical flow labels between camera pose pairs based on the rendering results. To eliminate erroneous labels caused by defects in the scene reconstructed by NeRF we screened the generated labels from multiple aspects such as optical flow matching accuracy radiation field confidence and depth consistency. The filtered labels can be directly used for network supervision. Experimentally the generalization ability of ADF on KITTI surpasses existing self-supervised optical flow and monocular scene flow algorithms. In addition ADF achieves impressive results in real-world zero-point generalization evaluations and surpasses most supervised methods.
Robust Noisy Correspondence Learning with Equivariant Similarity Consistency
Authors: Likai Wang, Erkun Yang, Cheng Deng Yuchen Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The surge in multi-modal data has propelled cross-modal matching to the forefront of research interest. However the challenge lies in the laborious and expensive process of curating a large and accurately matched multimodal dataset. Commonly sourced from the Internet these datasets often suffer from a significant presence of mismatched data impairing the performance of matching models. To address this problem we introduce a novel regularization approach named Equivariant Similarity Consistency (ESC) which can facilitate robust clean and noisy data separation and improve the training for cross-modal matching. Intuitively our method posits that the semantic variations caused by image changes should be proportional to those caused by text changes for any two matched samples. Accordingly we first calculate the ESC by comparing image and text semantic variations between a set of elaborated anchor points and other undivided training data. Then pairs with high ESC are filtered out as noisy correspondence pairs. We implement our method by combining the ESC with a traditional hinge-based triplet loss. Extensive experiments on three widely used datasets including Flickr30K MS-COCO and Conceptual Captions verify the effectiveness of our method.