GenAI4ELab/papercli-papers huggingface dataset:
VS: Reconstructing Clothed 3D Human from Single Image via Vertex Shift
Authors: Leyuan Liu; Yuhan Li; Yunqi Gao; Changxin Gao; Yuanyuan Liu; Jingying Chen
Venue: CVPR 2024 (main)

Abstract: Various applications require high-fidelity and artifact-free 3D human reconstructions. However current implicit function-based methods inevitably produce artifacts while existing deformation methods are difficult to reconstruct high-fidelity humans wearing loose clothing. In this paper we propose a two-stage deformation method named Vertex Shift (VS) for reconstructing clothed 3D humans from single images. Specifically VS first stretches the estimated SMPL-X mesh into a coarse 3D human model using shift fields inferred from normal maps then refines the coarse 3D human model into a detailed 3D human model via a graph convolutional network embedded with implicit-function-learned features. This "stretch-refine" strategy addresses large deformations required for reconstructing loose clothing and delicate deformations for recovering intricate and detailed surfaces achieving high-fidelity reconstructions that faithfully convey the pose clothing and surface details from the input images. The graph convolutional network's ability to exploit neighborhood vertices coupled with the advantages inherited from the deformation methods ensure VS rarely produces artifacts like distortions and non-human shapes and never produces artifacts like holes broken parts and dismembered limbs. As a result VS can reconstruct high-fidelity and artifact-less clothed 3D humans from single images even under scenarios of challenging poses and loose clothing. Experimental results on three benchmarks and two in-the-wild datasets demonstrate that VS significantly outperforms current state-of-the-art methods. The code and models of VS are available for research purposes at https://github.com/starVisionTeam/VS.
PARA-Drive: Parallelized Architecture for Real-time Autonomous Driving
Authors: Xinshuo Weng; Boris Ivanovic; Yan Wang; Yue Wang; Marco Pavone
Venue: CVPR 2024 (main)

Abstract: Recent works have proposed end-to-end autonomous vehicle (AV) architectures comprised of differentiable modules achieving state-of-the-art driving performance. While they provide advantages over the traditional perception-prediction-planning pipeline (e.g. removing information bottlenecks between components and alleviating integration challenges) they do so using a diverse combination of tasks modules and their interconnectivity. As of yet however there has been no systematic analysis of the necessity of these modules or the impact of their connectivity placement and internal representations on overall driving performance. Addressing this gap our work conducts a comprehensive exploration of the design space of end-to-end modular AV stacks. Our findings culminate in the development of PARA-Drive: a fully parallel end-to-end AV architecture. PARA-Drive not only achieves state-of-the-art performance in perception prediction and planning but also significantly enhances runtime speed by nearly 3x without compromising on interpretability or safety.
TEA: Test-time Energy Adaptation
Authors: Yige Yuan; Bingbing Xu; Liang Hou; Fei Sun; Huawei Shen; Xueqi Cheng
Venue: CVPR 2024 (main)

Abstract: Test Time Adaptation (TTA) aims to improve model generalizability when test data diverges from training distribution with the distinct advantage of not requiring access to training data and processes especially valuable in the context of pre-trained models. However current TTA methods fail to address the fundamental issue: covariate shift i.e. the decreased generalizability can be attributed to the model's reliance on the marginal distribution of the training data which may impair model calibration and introduce confirmation bias. To address this we propose a novel energy-based perspective enhancing the model's perception of target data distributions without requiring access to training data or processes. Building on this perspective we introduce Test-time Energy Adaptation (TEA) which transforms the trained classifier into an energy-based model and aligns the model's distribution with the test data's enhancing its ability to perceive test distributions and thus improving overall generalizability. Extensive experiments across multiple tasks benchmarks and architectures demonstrate TEA's superior generalization performance against state-of-the-art methods. Further in-depth analyses reveal that TEA can equip the model with a comprehensive perception of test distribution ultimately paving the way toward improved generalization and calibration. Code is available at https://github.com/yuanyige/tea.
NEAT: Distilling 3D Wireframes from Neural Attraction Fields
Authors: Nan Xue; Bin Tan; Yuxi Xiao; Liang Dong; Gui-Song Xia; Tianfu Wu; Yujun Shen
Venue: CVPR 2024 (main)

Abstract: This paper studies the problem of structured 3D recon- struction using wireframes that consist of line segments and junctions focusing on the computation of structured boundary geometries of scenes. Instead of leveraging matching-based solutions from 2D wireframes (or line segments) for 3D wireframe reconstruction as done in prior arts we present NEAT a rendering-distilling formulation using neural fields to represent 3D line segments with 2D observations and bipartite matching for perceiving and dis- tilling of a sparse set of 3D global junctions. The proposed NEAT enjoys the joint optimization of the neural fields and the global junctions from scratch using view-dependent 2D observations without precomputed cross-view feature matching. Comprehensive experiments on the DTU and BlendedMVS datasets demonstrate our NEAT's superiority over state-of-the-art alternatives for 3D wireframe recon- struction. Moreover the distilled 3D global junctions by NEAT are a better initialization than SfM points for the recently-emerged 3D Gaussian Splatting for high-fidelity novel view synthesis using about 20 times fewer initial 3D points. Project page: https://xuenan.net/neat
Prompt Augmentation for Self-supervised Text-guided Image Manipulation
Authors: Rumeysa Bodur; Binod Bhattarai; Tae-Kyun Kim
Venue: CVPR 2024 (main)

Abstract: Text-guided image editing finds applications in various creative and practical fields. While recent studies in image generation have advanced the field they often struggle with the dual challenges of coherent image transformation and context preservation. In response our work introduces prompt augmentation a method amplifying a single input prompt into several target prompts strengthening textual context and enabling localised image editing. Specifically we use the augmented prompts to delineate the intended manipulation area. We propose a Contrastive Loss tailored to driving effective image editing by displacing edited areas and drawing preserved regions closer. Acknowledging the continuous nature of image manipulations we further refine our approach by incorporating the similarity concept creating a Soft Contrastive Loss. The new losses are incorporated to the diffusion model demonstrating improved or competitive image editing results on public datasets and generated images over state-of-the-art approaches.
Pink: Unveiling the Power of Referential Comprehension for Multi-modal LLMs
Authors: Shiyu Xuan; Qingpei Guo; Ming Yang; Shiliang Zhang
Venue: CVPR 2024 (main)

Abstract: Multi-modal Large Language Models (MLLMs) have shown remarkable capabilities in various multi-modal tasks. Nevertheless their performance in fine-grained image understanding tasks is still limited. To address this issue this paper proposes a new framework to enhance the fine-grained image understanding abilities of MLLMs. Specifically we present a new method for constructing the instruction tuning dataset at a low cost by leveraging annotations in existing datasets. A self-consistent bootstrapping method is also introduced to extend existing dense object annotations into high-quality referring-expression-bounding-box pairs. These methods enable the generation of high-quality instruction data which includes a wide range of fundamental abilities essential for fine-grained image perception. Moreover we argue that the visual encoder should be tuned during instruction tuning to mitigate the gap between full image perception and fine-grained image perception. Experimental results demonstrate the superior performance of our method. For instance our model exhibits a 5.2% accuracy improvement over Qwen-VL on GQA and surpasses the accuracy of Kosmos-2 by 24.7% on RefCOCO_val. We have also attained the top rank on the leaderboard of MMBench. This promising performance is achieved by training on only publicly available data making it easily reproducible. The models datasets and codes are publicly available at https://github.com/SY-Xuan/Pink.
LDP: Language-driven Dual-Pixel Image Defocus Deblurring Network
Authors: Hao Yang; Liyuan Pan; Yan Yang; Richard Hartley; Miaomiao Liu
Venue: CVPR 2024 (main)

Abstract: Recovering sharp images from dual-pixel (DP) pairs with disparity-dependent blur is a challenging task. Existing blur map-based deblurring methods have demonstrated promising results. In this paper we propose to the best of our knowledge the first framework to introduce the contrastive language-image pre-training framework (CLIP) to achieve accurate blur map estimation from DP pairs unsupervisedly. To this end we first carefully design text prompts to enable CLIP to understand blur-related geometric prior knowledge from the DP pair. Then we propose a format to input stereo DP pair to the CLIP without any fine-tuning where the CLIP is pre-trained on monocular images. Given the estimated blur map we introduce a blur-prior attention block a blur-weighting loss and a blur-aware loss to recover the all-in-focus image. Our method achieves state-of-the-art performance in extensive experiments (see Fig. 1).
MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos
Authors: Jielin Qiu; Jiacheng Zhu; William Han; Aditesh Kumar; Karthik Mittal; Claire Jin; Zhengyuan Yang; Linjie Li; Jianfeng Wang; Ding Zhao; Bo Li; Lijuan Wang
Venue: CVPR 2024 (main)

Abstract: Multimodal summarization with multimodal output (MSMO) has emerged as a promising research direction. Nonetheless numerous limitations exist within existing public MSMO datasets including insufficient maintenance data inaccessibility limited size and the absence of proper categorization which pose significant challenges. To address these challenges and provide a comprehensive dataset for this new direction we have meticulously curated the MMSum dataset. Our new dataset features (1) Human-validated summaries for both video and textual content providing superior human instruction and labels for multimodal learning. (2) Comprehensively and meticulously arranged categorization spanning 17 principal categories and 170 subcategories to encapsulate a diverse array of real-world scenarios. (3) Benchmark tests performed on the proposed dataset to assess various tasks and methods including video summarization text summarization and multimodal summarization. To champion accessibility and collaboration we released the MMSum dataset and the data collection tool as fully open-source resources fostering transparency and accelerating future developments.
HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data
Authors: Qifan Yu; Juncheng Li; Longhui Wei; Liang Pang; Wentao Ye; Bosheng Qin; Siliang Tang; Qi Tian; Yueting Zhuang
Venue: CVPR 2024 (main)

Abstract: Multi-modal Large Language Models (MLLMs) tuned on machine-generated instruction-following data have demonstrated remarkable performance in various multimodal understanding and generation tasks. However the hallucinations inherent in machine-generated data which could lead to hallucinatory outputs in MLLMs remain under-explored. This work aims to investigate various hallucinations (i.e. object relation attribute hallucinations) and mitigate those hallucinatory toxicities in large-scale machine-generated visual instruction datasets. Drawing on the human ability to identify factual errors we present a novel hallucination detection and elimination framework HalluciDoctor based on the cross-checking paradigm. We use our framework to identify and eliminate hallucinations in the training data automatically. Interestingly HalluciDoctor also indicates that spurious correlations arising from long-tail object co-occurrences contribute to hallucinations. Based on that we execute counterfactual visual instruction expansion to balance data distribution thereby enhancing MLLMs' resistance to hallucinations. Comprehensive experiments on hallucination evaluation benchmarks show that our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA. The data and code for this paper are publicly available.
Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners
Authors: Keon-Hee Park; Kyungwoo Song; Gyeong-Moon Park
Venue: CVPR 2024 (main)

Abstract: Few-Shot Class Incremental Learning (FSCIL) is a task that requires a model to learn new classes incrementally without forgetting when only a few samples for each class are given. FSCIL encounters two significant challenges: catastrophic forgetting and overfitting and these challenges have driven prior studies to primarily rely on shallow models such as ResNet-18. Even though their limited capacity can mitigate both forgetting and overfitting issues it leads to inadequate knowledge transfer during few-shot incremental sessions. In this paper we argue that large models such as vision and language transformers pre-trained on large datasets can be excellent few-shot incremental learners. To this end we propose a novel FSCIL framework called PriViLege Pre-trained Vision and Language transformers with prompting functions and knowledge distillation. Our framework effectively addresses the challenges of catastrophic forgetting and overfitting in large models through new pre-trained knowledge tuning (PKT) and two losses: entropy-based divergence loss and semantic knowledge distillation loss. Experimental results show that the proposed PriViLege significantly outperforms the existing state-of-the-art methods with a large margin e.g. +9.38% in CUB200 +20.58% in CIFAR-100 and +13.36% in miniImageNet. Our implementation code is available at https://github.com/KHU-AGI/PriViLege.
Guess The Unseen: Dynamic 3D Scene Reconstruction from Partial 2D Glimpses
Authors: Inhee Lee; Byungjun Kim; Hanbyul Joo
Venue: CVPR 2024 (main)

Abstract: In this paper we present a method to reconstruct the world and multiple dynamic humans in 3D from a monocular video input. As a key idea we represent both the world and multiple humans via the recently emerging 3D Gaussian Splatting (3D-GS) representation enabling to conveniently and efficiently compose and render them together. In particular we address the scenarios with severely limited and sparse observations in 3D human reconstruction a common challenge encountered in the real world. To tackle this challenge we introduce a novel approach to optimize the 3D-GS representation in a canonical space by fusing the sparse cues in the common space where we leverage a pre-trained 2D diffusion model to synthesize unseen views while keeping the consistency with the observed 2D appearances. We demonstrate our method can reconstruct high-quality animatable 3D humans in various challenging examples in the presence of occlusion image crops few-shot and extremely sparse observations. After reconstruction our method is capable of not only rendering the scene in any novel views at arbitrary time instances but also editing the 3D scene by removing individual humans or applying different motions for each human. Through various experiments we demonstrate the quality and efficiency of our methods over alternative existing approaches.
C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction
Authors: Yiqun Lin; Jiewen Yang; Hualiang Wang; Xinpeng Ding; Wei Zhao; Xiaomeng Li
Venue: CVPR 2024 (main)

Abstract: Cone beam computed tomography (CBCT) is an important imaging technology widely used in medical scenarios such as diagnosis and preoperative planning. Using fewer projection views to reconstruct CT also known as sparse-view reconstruction can reduce ionizing radiation and further benefit interventional radiology. Compared with sparse-view reconstruction for traditional parallel/fan-beam CT CBCT reconstruction is more challenging due to the increased dimensionality caused by the measurement process based on cone-shaped X-ray beams. As a 2D-to-3D reconstruction problem although implicit neural representations have been introduced to enable efficient training only local features are considered and different views are processed equally in previous works resulting in spatial inconsistency and poor performance on complicated anatomies. To this end we propose C^2RV by leveraging explicit multi-scale volumetric representations to enable cross-regional learning in the 3D space. Additionally the scale-view cross-attention module is introduced to adaptively aggregate multi-scale and multi-view features. Extensive experiments demonstrate that our C^2RV achieves consistent and significant improvement over previous state-of-the-art methods on datasets with diverse anatomy. Code is available at https://github.com/xmed-lab/C2RV-CBCT.
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
Authors: Nataniel Ruiz; Yuanzhen Li; Varun Jampani; Wei Wei; Tingbo Hou; Yael Pritch; Neal Wadhwa; Michael Rubinstein; Kfir Aberman
Venue: CVPR 2024 (main)

Abstract: Personalization has emerged as a prominent aspect within the field of generative AI enabling the synthesis of individuals in diverse contexts and styles while retaining high-fidelity to their identities. However the process of personalization presents inherent challenges in terms of time and memory requirements. Fine-tuning each personalized model needs considerable GPU time investment and storing a personalized model per subject can be demanding in terms of storage capacity. To overcome these challenges we propose HyperDreamBooth - a hypernetwork capable of efficiently generating a small set of personalized weights from a single image of a person. By composing these weights into the diffusion model coupled with fast finetuning HyperDreamBooth can generate a person's face in various contexts and styles with high subject details while also preserving the model's crucial knowledge of diverse styles and semantic modifications. Our method achieves personalization on faces in roughly 20 seconds 25x faster than DreamBooth and 125x faster than Textual Inversion using as few as one reference image with the same quality and style diversity as DreamBooth. Also our method yields a model that is 10000x smaller than a normal DreamBooth model.
Language-guided Image Reflection Separation
Authors: Haofeng Zhong; Yuchen Hong; Shuchen Weng; Jinxiu Liang; Boxin Shi
Venue: CVPR 2024 (main)

Abstract: This paper studies the problem of language-guided reflection separation which aims at addressing the ill-posed reflection separation problem by introducing language descriptions to provide layer content. We propose a unified framework to solve this problem which leverages the cross-attention mechanism with contrastive learning strategies to construct the correspondence between language descriptions and image layers. A gated network design and a randomized training strategy are employed to tackle the recognizable layer ambiguity. The effectiveness of the proposed method is validated by the significant performance advantage over existing reflection separation methods on both quantitative and qualitative comparisons.
HardMo: A Large-Scale Hardcase Dataset for Motion Capture
Authors: Jiaqi Liao; Chuanchen Luo; Yinuo Du; Yuxi Wang; Xucheng Yin; Man Zhang; Zhaoxiang Zhang; Junran Peng
Venue: CVPR 2024 (main)

Abstract: Recent years have witnessed rapid progress in monocular human mesh recovery. Despite their impressive performance on public benchmarks existing methods are vulnerable to unusual poses which prevents them from deploying to challenging scenarios such as dance and martial arts. This issue is mainly attributed to the domain gap induced by the data scarcity in relevant cases. Most existing datasets are captured in constrained scenarios and lack samples of such complex movements. For this reason we propose a data collection pipeline comprising automatic crawling precise annotation and hardcase mining. Based on this pipeline we establish a large dataset in a short time. The dataset named HardMo contains 7M images along with precise annotations covering 15 categories of dance and 14 categories of martial arts. Empirically we find that the prediction failure in dance and martial arts is mainly characterized by the misalignment of hand-wrist and foot-ankle. To dig deeper into the two hardcases we leverage the proposed automatic pipeline to filter collected data and construct two subsets named HardMo-Hand and HardMo-Foot. Extensive experiments demonstrate the effectiveness of the annotation pipeline and the data-driven solution to failure cases. Specifically after being trained on HardMo HMR an early pioneering method can even outperform the current state of the art 4DHumans on our benchmarks.
View-Category Interactive Sharing Transformer for Incomplete Multi-View Multi-Label Learning
Authors: Shilong Ou; Zhe Xue; Yawen Li; Meiyu Liang; Yuanqiang Cai; Junjiang Wu
Venue: CVPR 2024 (main)

Abstract: As a problem often encountered in real-world scenarios multi-view multi-label learning has attracted considerable research attention. However due to oversights in data collection and uncertainties in manual annotation real-world data often suffer from incompleteness. Regrettably most existing multi-view multi-label learning methods sidestep missing views and labels. Furthermore they often neglect the potential of harnessing complementary information between views and labels thus constraining their classification capabilities. To address these challenges we propose a view-category interactive sharing transformer tailored for incomplete multi-view multi-label learning. Within this network we incorporate a two-layer transformer module to characterize the interplay between views and labels. Additionally to address view incompleteness a KNN-style missing view generation module is employed. Finally we introduce a view-category consistency guided embedding enhancement module to align different views and improve the discriminating power of the embeddings. Collectively these modules synergistically integrate to classify the incomplete multi-view multi-label data effectively. Extensive experiments substantiate that our approach outperforms the existing state-of-the-art methods.
The More You See in 2D the More You Perceive in 3D
Authors: Xinyang Han; Zelin Gao; Angjoo Kanazawa; Shubham Goel; Yossi Gandelsman
Venue: CVPR 2024 (main)

Abstract: Humans can infer 3D structure from 2D images of an object based on past experience and improve their 3D understanding as they see more images. Inspired by this behavior we introduce SAP3D a system for 3D reconstruction and novel view synthesis from an arbitrary number of unposed images. Given a few unposed images of an object we adapt a pre-trained view-conditioned diffusion model together with the camera poses of the images via test-time fine-tuning. The adapted diffusion model and the obtained camera poses are then utilized as instance-specific priors for 3D reconstruction and novel view synthesis. We show that as the number of input images increases the performance of our approach improves bridging the gap between optimization-based prior-less 3D reconstruction methods and single-image-to-3D diffusion-based methods. We demonstrate our system on real images as well as standard synthetic benchmarks. Our ablation studies confirm that this adaption behavior is key for more accurate 3D understanding.