pwc-archive/papers-with-abstracts 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
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1

    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.
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action
Authors: Jiasen Lu, Christopher Clark, Sangho Lee, Zichen Zhang, Savya Khosla, Ryan Marten, Derek Hoiem, Aniruddha Kembhavi
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Generation, Instruction Following, object-detection, Object Detection, Video Understanding, Visual Question Answering (VQA)

    We present Unified-IO 2 a multimodal and multi-skill unified model capable of following novel instructions. Unified-IO 2 can use text images audio and/or videos as input and can generate text image or audio outputs which is accomplished in a unified way by tokenizing these different inputs and outputs into a shared semantic space that can then be processed by a single encoder-decoder transformer model. Unified-IO 2 is trained from scratch on a custom-built multimodal pre-training corpus and then learns an expansive set of skills through fine-tuning on over 120 datasets including datasets for segmentation object detection image editing audio localization video tracking embodied AI and 3D detection. To facilitate instruction-following we add prompts and other data augmentations to these tasks to allow Unified-IO 2 to generalize these skills to new tasks zero-shot. Unified-IO 2 is the first model to be trained on such a diverse and wide-reaching set of skills and unify three separate generation capabilities. Unified-IO 2 achieves state-of-the-art performance on the multi-task GRIT benchmark and achieves strong results on 30 diverse datasets including SEED-Bench image and video understanding TIFA image generation VQA 2.0 ScienceQA VIMA robotic manipulation VGG-Sound and Kinetics-Sounds and can perform unseen tasks and generate free-form responses. We release our model and code to facilitate future work.
Device-Wise Federated Network Pruning
Authors: Shangqian Gao, Junyi Li, Zeyu Zhang, yanfu Zhang, Weidong Cai, Heng Huang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Edge-computing, Federated Learning, Network Pruning

    Neural network pruning particularly channel pruning is a widely used technique for compressing deep learning models to enable their deployment on edge devices with limited resources. Typically redundant weights or structures are removed to achieve the target resource budget. Although data-driven pruning approaches have proven to be more effective they cannot be directly applied to federated learning (FL) which has emerged as a popular technique in edge computing applications because of distributed and confidential datasets. In response to this challenge we design a new network pruning method for FL. We propose device-wise sub-networks for each device assuming that the data distribution is similar within each device. These sub-networks are generated through sub-network embeddings and a hypernetwork. To further minimize memory usage and communication costs we permanently prune the full model to remove weights that are not useful for all devices. During the FL process we simultaneously train the device-wise sub-networks and the base sub-network to facilitate the pruning process. We then finetune the pruned model with device-wise sub-networks to regain performance. Moreover we provided the theoretical guarantee of convergence for our method. Our method achieves better performance and resource trade-off than other well-established network pruning baselines as demonstrated through extensive experiments on CIFAR-10 CIFAR-100 and TinyImageNet.
SPIN: Simultaneous Perception Interaction and Navigation
Authors: Shagun Uppal, Ananye Agarwal, Haoyu Xiong, Kenneth Shaw, Deepak Pathak
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Navigate

    While there has been remarkable progress recently in the fields of manipulation and locomotion mobile manipulation remains a long-standing challenge. Compared to locomotion or static manipulation a mobile system must make a diverse range of long-horizon tasks feasible in unstructured and dynamic environments. While the applications are broad and interesting there are a plethora of challenges in developing these systems such as coordination between the base and arm reliance on onboard perception for perceiving and interacting with the environment and most importantly simultaneously integrating all these parts together. Prior works approach the problem using disentangled modular skills for mobility and manipulation that are trivially tied together. This causes several limitations such as compounding errors delays in decision-making and no whole-body coordination. In this work we present a reactive mobile manipulation framework that uses an active visual system to consciously perceive and react to its environment. Similar to how humans leverage whole-body and hand-eye coordination we develop a mobile manipulator that exploits its ability to move and see more specifically -- to move in order to see and to see in order to move. This allows it to not only move around and interact with its environment but also choose "when" to perceive "what" using an active visual system. We observe that such an agent learns to navigate around complex cluttered scenarios while displaying agile whole-body coordination using only ego-vision without needing to create environment maps. Videos are available at https://spin-robot.github.io
Instance-aware Contrastive Learning for Occluded Human Mesh Reconstruction
Authors: Mi-Gyeong Gwon, Gi-Mun Um, Won-Sik Cheong, Wonjun Kim
Date: 2024-01-01 00:00:00
Proceeding: Conference on Computer Vision and Pattern Recognition (CVPR) 2024 6
Tasks: 3D Human Pose Estimation, 3D Multi-Person Mesh Recovery, 3D Multi-Person Pose Estimation, Contrastive Learning, Human Mesh Recovery

    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.
EventPS: Real-Time Photometric Stereo Using an Event Camera
Authors: Bohan Yu, Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, Boxin Shi
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1

    Photometric stereo is a well-established technique to estimate the surface normal of an object. However the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency. EventPS seamlessly integrates with both optimization-based and deep-learning-based photometric stereo techniques to offer a robust solution for non-Lambertian surfaces. Extensive experiments validate the effectiveness and efficiency of EventPS compared to frame-based counterparts. Our algorithm runs at over 30 fps in real-world scenarios unleashing the potential of EventPS in time-sensitive and high-speed downstream applications.
Language-aware Visual Semantic Distillation for Video Question Answering
Authors: Bo Zou, Chao Yang, Yu Qiao, Chengbin Quan, Youjian Zhao
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Answer Generation, Question Answering, Video Question Answering

    Significant advancements in video question answering (VideoQA) have been made thanks to thriving large image-language pretraining frameworks. Although these image-language models can efficiently represent both video and language branches they typically employ a goal-free vision perception process and do not interact vision with language well during the answer generation thus omitting crucial visual cues. In this paper we are inspired by the human recognition and learning pattern and propose VideoDistill a framework with language-aware (i.e. goal-driven) behavior in both vision perception and answer generation process. VideoDistill generates answers only from question-related visual embeddings and follows a thinking-observing-answering approach that closely resembles human behavior distinguishing it from previous research. Specifically we develop a language-aware gating mechanism to replace the standard cross-attention avoiding language's direct fusion into visual representations. We incorporate this mechanism into two key components of the entire framework. The first component is a differentiable sparse sampling module which selects frames containing the necessary dynamics and semantics relevant to the questions. The second component is a vision refinement module that merges existing spatial-temporal attention layers to ensure the extraction of multi-grained visual semantics associated with the questions. We conduct experimental evaluations on various challenging video question-answering benchmarks and VideoDistill achieves state-of-the-art performance in both general and long-form VideoQA datasets. In Addition we verify that VideoDistill can effectively alleviate the utilization of language shortcut solutions in the EgoTaskQA dataset.
Taming Stable Diffusion for Text to 360 Panorama Image Generation
Authors: Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella, Xiaoshui Huang, Dinh Phung, Wanli Ouyang, Jianfei Cai
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Denoising, Image Generation

    Generative models e.g. Stable Diffusion have enabled the creation of photorealistic images from text prompts. Yet the generation of 360-degree panorama images from text remains a challenge particularly due to the dearth of paired text-panorama data and the domain gap between panorama and perspective images. In this paper we introduce a novel dual-branch diffusion model named PanFusion to generate a 360-degree image from a text prompt. We leverage the stable diffusion model as one branch to provide prior knowledge in natural image generation and register it to another panorama branch for holistic image generation. We propose a unique cross-attention mechanism with projection awareness to minimize distortion during the collaborative denoising process. Our experiments validate that PanFusion surpasses existing methods and thanks to its dual-branch structure can integrate additional constraints like room layout for customized panorama outputs.
Self-Supervised Representation Learning from Arbitrary Scenarios
Authors: Zhaowen Li, Yousong Zhu, Zhiyang Chen, Zongxin Gao, Rui Zhao, Chaoyang Zhao, Ming Tang, Jinqiao Wang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Contrastive Learning, Data Augmentation, Decoder, Representation Learning

    Current self-supervised methods can primarily be categorized into contrastive learning and masked image modeling. Extensive studies have demonstrated that combining these two approaches can achieve state-of-the-art performance. However these methods essentially reinforce the global consistency of contrastive learning without taking into account the conflicts between these two approaches which hinders their generalizability to arbitrary scenarios. In this paper we theoretically prove that MAE serves as a patch-level contrastive learning where each patch within an image is considered as a distinct category. This presents a significant conflict with global-level contrastive learning which treats all patches in an image as an identical category. To address this conflict this work abandons the non-generalizable global-level constraints and proposes explicit patch-level contrastive learning as a solution. Specifically this work employs the encoder of MAE to generate dual-branch features which then perform patch-level learning through a decoder. In contrast to global-level data augmentation in contrastive learning our approach leverages patch-level feature augmentation to mitigate interference from global-level learning. Consequently our approach can learn heterogeneous representations from a single image while avoiding the conflicts encountered by previous methods. Massive experiments affirm the potential of our method for learning from arbitrary scenarios.
Towards CLIP-driven Language-free 3D Visual Grounding via 2D-3D Relational Enhancement and Consistency
Authors: Yuqi Zhang, Han Luo, Yinjie Lei
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D visual grounding, Relation, Scene Understanding, Text Generation, Visual Grounding

    3D visual grounding plays a crucial role in scene understanding with extensive applications in AR/VR. Despite the significant progress made in recent methods the requirement of dense textual descriptions for each individual object which is time-consuming and costly hinders their scalability. To mitigate reliance on text annotations during training researchers have explored language-free training paradigms in the 2D field via explicit text generation or implicit feature substitution. Nevertheless unlike 2D images the complexity of spatial relations in 3D coupled with the absence of robust 3D visual language pre-trained models makes it challenging to directly transfer previous strategies. To tackle the above issues in this paper we introduce a language-free training framework for 3D visual grounding. By utilizing the visual-language joint embedding in 2D large cross-modality model as a bridge we can expediently produce the pseudo-language features by leveraging the features of 2D images which are equivalent to that of real textual descriptions. We further develop a relation injection scheme with a Neighboring Relation-aware Modeling module and a Cross-modality Relation Consistency module aiming to enhance and preserve the complex relationships between the 2D and 3D embedding space. Extensive experiments demonstrate that our proposed language-free 3D visual grounding approach can obtain promising performance across three widely used datasets --ScanRefer Nr3D and Sr3D. Our codes are available at https://github.com/xibi777/3DLFVG
Making Visual Sense of Oracle Bones for You and Me
Authors: Runqi Qiao, Lan Yang, Kaiyue Pang, Honggang Zhang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Semantic correspondence

    Visual perception evolves over time. This is particularly the case of oracle bone scripts where visual glyphs seem intuitive to people from distant past prove difficult to be understood in contemporary eyes. While semantic correspondence of an oracle can be found via a dictionary lookup this proves to be not enough for public viewers to connect the dots i.e. why does this oracle mean that? Common solution relies on a laborious curation process to collect visual guide for each oracle (Fig.1) which hinges on the case-by-case effort and taste of curators. This paper delves into one natural follow-up question: can AI take over?Begin with a comprehensive human study we show participants could indeed make better sense of an oracle glyph subjected to a proper visual guide and its efficacy can be approximated via a novel metric termed TransOV (Transferable Oracle Visuals). We then define a new conditional visual generation task based on an oracle glyph and its semantic meaning and importantly approach it by circumventing any form of model training in the presence of fatal lack of oracle data. At its heart is to leverage foundation model like GPT-4V to reason about the visual cues hidden inside an oracle and take advantage of an existing text-to-image model for final visual guide generation. Extensive empirical evidence shows our AI-enabled visual guides achieve significantly comparable TransOV performance compared with those collected under manual efforts. Finally we demonstrate the versatility of our system under a more complex setting where it is required to work alongside an AI image denoiser to cope with raw oracle scan image inputs (cf. processed clean oracle glyphs). Code is available at https://github.com/RQ-Lab/OBS-Visual.
Flow-Guided Online Stereo Rectification for Wide Baseline Stereo
Authors: Anush Kumar, Fahim Mannan, Omid Hosseini jafari, Shile Li, Felix Heide
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Autonomous Vehicles, Optical Flow Estimation

    Stereo rectification is widely considered "solved" due to the abundance of traditional approaches to perform rectification. However autonomous vehicles and robots in-the-wild require constant re-calibration due to exposure to various environmental factors including vibration and structural stress when cameras are arranged in a wide-baseline configuration. Conventional rectification methods fail in these challenging scenarios: especially for larger vehicles such as autonomous freight trucks and semi-trucks the resulting incorrect rectification severely affects the quality of downstream tasks that use stereo/multi-view data. To tackle these challenges we propose an online rectification approach that operates at real-time rates while achieving high accuracy. We propose a novel learning-based online calibration approach that utilizes stereo correlation volumes built from a feature representation obtained from cross-image attention. Our model is trained to minimize vertical optical flow as proxy rectification constraint and predicts the relative rotation between the stereo pair. The method is real-time and even outperforms conventional methods used for offline calibration and substantially improves downstream stereo depth post-rectification. We release two public datasets (https://light.princeton.edu/online-stereo-recification/) a synthetic and experimental wide baseline dataset to foster further research.
A Theory of Joint Light and Heat Transport for Lambertian Scenes
Authors: Mani Ramanagopal, Sriram Narayanan, Aswin C. Sankaranarayanan, Srinivasa G. Narasimhan
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Intrinsic Image Decomposition

    We present a novel theory that establishes the relationship between light transport in visible and thermal infrared and heat transport in solids. We show that heat generated due to light absorption can be estimated by modeling heat transport using a thermal camera. For situations where heat conduction is negligible we analytically solve the heat transport equation to derive a simple expression relating the change in thermal image intensity to the absorbed light intensity and heat capacity of the material. Next we prove that intrinsic image decomposition for Lambertian scenes becomes a well-posed problem if one has access to the absorbed light. Our theory generalizes to arbitrary shapes and unstructured illumination. Our theory is based on applying energy conservation principle at each pixel independently. We validate our theory using real-world experiments on diffuse objects made of different materials that exhibit both direct and global components (inter-reflections) of light transport under unknown complex lighting.
Multiway Point Cloud Mosaicking with Diffusion and Global Optimization
Authors: Shengze Jin, Iro Armeni, Marc Pollefeys, Daniel Barath
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Denoising, global-optimization

    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.