GenAI4ELab/papercli-papers huggingface dataset:
Unsupervised Learning of Category-Level 3D Pose from Object-Centric Videos
Authors: Leonhard Sommer; Artur Jesslen; Eddy Ilg; Adam Kortylewski
Venue: CVPR 2024 (main)

Abstract: Category-level 3D pose estimation is a fundamentally important problem in computer vision and robotics e.g. for embodied agents or to train 3D generative models. However so far methods that estimate the category-level object pose require either large amounts of human annotations CAD models or input from RGB-D sensors. In contrast we tackle the problem of learning to estimate the category-level 3D pose only from casually taken object-centric videos without human supervision. We propose a two-step pipeline: First we introduce a multi-view alignment procedure that determines canonical camera poses across videos with a novel and robust cyclic distance formulation for geometric and appearance matching using reconstructed coarse meshes and DINOv2 features. In a second step the canonical poses and reconstructed meshes enable us to train a model for 3D pose estimation from a single image. In particular our model learns to estimate dense correspondences between images and a prototypical 3D template by predicting for each pixel in a 2D image a feature vector of the corresponding vertex in the template mesh. We demonstrate that our method outperforms all baselines at the unsupervised alignment of object-centric videos by a large margin and provides faithful and robust predictions in-the-wild on the Pascal3D+ and ObjectNet3D datasets.
Plug-and-Play Diffusion Distillation
Authors: Yi-Ting Hsiao; Siavash Khodadadeh; Kevin Duarte; Wei-An Lin; Hui Qu; Mingi Kwon; Ratheesh Kalarot
Venue: CVPR 2024 (main)

Abstract: Diffusion models have shown tremendous results in image generation. However due to the iterative nature of the diffusion process and its reliance on classifier-free guidance inference times are slow. In this paper we propose a new distillation approach for guided diffusion models in which an external lightweight guide model is trained while the original text-to-image model remains frozen. We show that our method reduces the inference computation of classifier-free guided latent-space diffusion models by almost half and only requires 1% trainable parameters of the base model. Furthermore once trained our guide model can be applied to various fine-tuned domain-specific versions of the base diffusion model without the need for additional training: this "plug-and-play" functionality drastically improves inference computation while maintaining the visual fidelity of generated images. Empirically we show that our approach is able to produce visually appealing results and achieve a comparable FID score to the teacher with as few as 8 to 16 steps.
MindBridge: A Cross-Subject Brain Decoding Framework
Authors: Shizun Wang; Songhua Liu; Zhenxiong Tan; Xinchao Wang
Venue: CVPR 2024 (main)

Abstract: Brain decoding a pivotal field in neuroscience aims to reconstruct stimuli from acquired brain signals primarily utilizing functional magnetic resonance imaging (fMRI). Currently brain decoding is confined to a per-subject-per-model paradigm limiting its applicability to the same individual for whom the decoding model is trained. This constraint stems from three key challenges: 1) the inherent variability in input dimensions across subjects due to differences in brain size; 2) the unique intrinsic neural patterns influencing how different individuals perceive and process sensory information; 3) limited data availability for new subjects in real-world scenarios hampers the performance of decoding models. In this paper we present a novel approach MindBridge that achieves cross-subject brain decoding by employing only one model. Our proposed framework establishes a generic paradigm capable of addressing these challenges by introducing biological-inspired aggregation function and novel cyclic fMRI reconstruction mechanism for subject-invariant representation learning. Notably by cycle reconstruction of fMRI MindBridge can enable novel fMRI synthesis which also can serve as pseudo data augmentation. Within the framework we also devise a novel reset-tuning method for adapting a pretrained model to a new subject. Experimental results demonstrate MindBridge's ability to reconstruct images for multiple subjects which is competitive with dedicated subject-specific models. Furthermore with limited data for a new subject we achieve a high level of decoding accuracy surpassing that of subject-specific models. This advancement in cross-subject brain decoding suggests promising directions for wider applications in neuroscience and indicates potential for more efficient utilization of limited fMRI data in real-world scenarios. Project page: https://littlepure2333.github.io/MindBridge
Make Pixels Dance: High-Dynamic Video Generation
Authors: Yan Zeng; Guoqiang Wei; Jiani Zheng; Jiaxin Zou; Yang Wei; Yuchen Zhang; Hang Li
Venue: CVPR 2024 (main)

Abstract: Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately current state-of-the-art video generation methods primarily focusing on text-to-video generation tend to produce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text instructions is insufficient and suboptimal for video generation. In this paper we introduce PixelDance a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions setting a new standard for video generation.
MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning
Authors: Chaoyi Zhang; Kevin Lin; Zhengyuan Yang; Jianfeng Wang; Linjie Li; Chung-Ching Lin; Zicheng Liu; Lijuan Wang
Venue: CVPR 2024 (main)

Abstract: We present MM-Narrator a novel system leveraging GPT-4 with multimodal in-context learning for the generation of audio descriptions (AD). Unlike previous methods that primarily focused on downstream fine-tuning with short video clips MM-Narrator excels in generating precise audio descriptions for videos of extensive lengths even beyond hours in an autoregressive manner. This capability is made possible by the proposed memory-augmented generation process which effectively utilizes both the short-term textual context and long-term visual memory through an efficient register-and-recall mechanism. These contextual memories compile pertinent past information including storylines and character identities ensuring an accurate tracking and depicting of story-coherent and character-centric audio descriptions. Maintaining the training-free design of MM-Narrator we further propose a complexity-based demonstration selection strategy to largely enhance its multi-step reasoning capability via few-shot multimodal in-context learning (MM-ICL). Experimental results on MAD-eval dataset demonstrate that MM-Narrator consistently outperforms both the existing fine-tuning-based approaches and LLM-based approaches in most scenarios as measured by standard evaluation metrics. Additionally we introduce the first segment-based evaluator for recurrent text generation. Empowered by GPT-4 this evaluator comprehensively reasons and marks AD generation performance in various extendable dimensions.
Morphable Diffusion: 3D-Consistent Diffusion for Single-image Avatar Creation
Authors: Xiyi Chen; Marko Mihajlovic; Shaofei Wang; Sergey Prokudin; Siyu Tang
Venue: CVPR 2024 (main)

Abstract: Recent advances in generative diffusion models have enabled the previously unfeasible capability of generating 3D assets from a single input image or a text prompt. In this work we aim to enhance the quality and functionality of these models for the task of creating controllable photorealistic human avatars. We achieve this by integrating a 3D morphable model into the state-of-the-art multi-view-consistent diffusion approach. We demonstrate that accurate conditioning of a generative pipeline on the articulated 3D model enhances the baseline model performance on the task of novel view synthesis from a single image. More importantly this integration facilitates a seamless and accurate incorporation of facial expression and body pose control into the generation process. To the best of our knowledge our proposed framework is the first diffusion model to enable the creation of fully 3D-consistent animatable and photorealistic human avatars from a single image of an unseen subject; extensive quantitative and qualitative evaluations demonstrate the advantages of our approach over existing state-of-the-art avatar creation models on both novel view and novel expression synthesis tasks. The code for our project is publicly available.
Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI
Authors: Sean I. Young; Yael Balbastre; Bruce Fischl; Polina Golland; Juan Eugenio Iglesias
Venue: CVPR 2024 (main)

Abstract: In magnetic resonance imaging (MRI) slice-to-volume reconstruction (SVR) refers to computational reconstruction of an unknown 3D magnetic resonance volume from stacks of 2D slices corrupted by motion. While promising current SVR methods require multiple slice stacks for accurate 3D reconstruction leading to long scans and limiting their use in time-sensitive applications such as fetal fMRI. Here we propose a SVR method that overcomes the shortcomings of previous work and produces state-of-the-art reconstructions in the presence of extreme inter-slice motion. Inspired by the recent success of single-view depth estimation methods we formulate SVR as a single-stack motion estimation task and train a fully convolutional network to predict a motion stack for a given slice stack producing a 3D reconstruction as a byproduct of the predicted motion. Extensive experiments on the SVR of adult and fetal brains demonstrate that our fully convolutional method is twice as accurate as previous SVR methods. Our code is available at github.com/seannz/svr.
Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model
Authors: Zhicai Wang; Longhui Wei; Tan Wang; Heyu Chen; Yanbin Hao; Xiang Wang; Xiangnan He; Qi Tian
Venue: CVPR 2024 (main)

Abstract: Text-to-image (T2I) generative models have recently emerged as a powerful tool enabling the creation of photo-realistic images and giving rise to a multitude of applications. However the effective integration of T2I models into fundamental image classification tasks remains an open question. A prevalent strategy to bolster image classification performance is through augmenting the training set with synthetic images generated by T2I models. In this study we scrutinize the shortcomings of both current generative and conventional data augmentation techniques. Our analysis reveals that these methods struggle to produce images that are both faithful (in terms of foreground objects) and diverse (in terms of background contexts) for domain-specific concepts. To tackle this challenge we introduce an innovative inter-class data augmentation method known as Diff-Mix (\href https://github.com/Zhicaiwww/Diff-Mix) https://github.com/Zhicaiwww/Diff-Mix   which enriches the dataset by performing image translations between classes. Our empirical results demonstrate that Diff-Mix achieves a better balance between faithfulness and diversity leading to a marked improvement in performance across diverse image classification scenarios including few-shot conventional and long-tail classifications for domain-specific datasets.
A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network
Authors: Ruichen Ma; Guanchao Qiao; Yian Liu; Liwei Meng; Ning Ning; Yang Liu; Shaogang Hu
Venue: CVPR 2024 (main)

Abstract: Binary neural networks utilize 1-bit quantized weights and activations to reduce both the model's storage demands and computational burden. However advanced binary architectures still incorporate millions of inefficient and nonhardware-friendly full-precision multiplication operations. A&B BNN is proposed to directly remove part of the multiplication operations in a traditional BNN and replace the rest with an equal number of bit operations introducing the mask layer and the quantized RPReLU structure based on the normalizer-free network architecture. The mask layer can be removed during inference by leveraging the intrinsic characteristics of BNN with straightforward mathematical transformations to avoid the associated multiplication operations. The quantized RPReLU structure enables more efficient bit operations by constraining its slope to be integer powers of 2. Experimental results achieved 92.30% 69.35% and 66.89% on the CIFAR-10 CIFAR-100 and ImageNet datasets respectively which are competitive with the state-of-the-art. Ablation studies have verified the efficacy of the quantized RPReLU structure leading to a 1.14% enhancement on the ImageNet compared to using a fixed slope RLeakyReLU. The proposed add&bit-operation-only BNN offers an innovative approach for hardware-friendly network architecture.
Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
Authors: Zeyi Sun; Ye Fang; Tong Wu; Pan Zhang; Yuhang Zang; Shu Kong; Yuanjun Xiong; Dahua Lin; Jiaqi Wang
Venue: CVPR 2024 (main)

Abstract: Contrastive Language-Image Pre-training (CLIP) plays an essential role in extracting valuable content information from images across diverse tasks. It aligns textual and visual modalities to comprehend the entire image including all the details even those irrelevant to specific tasks. However for a finer understanding and controlled editing of images it becomes crucial to focus on specific regions of interest which can be indicated as points masks or boxes by humans or perception models. To fulfill the requirements we introduce Alpha-CLIP an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs. Alpha-CLIP not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents. It demonstrates effectiveness in various tasks including but not limited to open-world recognition multimodal large language models and conditional 2D / 3D generation. It has a strong potential to serve as a versatile tool for image-related tasks.
FutureHuman3D: Forecasting Complex Long-Term 3D Human Behavior from Video Observations
Authors: Christian Diller; Thomas Funkhouser; Angela Dai
Venue: CVPR 2024 (main)

Abstract: We present a generative approach to forecast long-term future human behavior in 3D requiring only weak supervision from readily available 2D human action data. This is a fundamental task enabling many downstream applications. The required ground-truth data is hard to capture in 3D (mocap suits expensive setups) but easy to acquire in 2D (simple RGB cameras). Thus we design our method to only require 2D RGB data at inference time while being able to generate 3D human motion sequences. We use a differentiable 2D projection scheme in an autoregressive manner for weak supervision and an adversarial loss for 3D regularization. Our method predicts long and complex human behavior sequences (e.g. cooking assembly) consisting of multiple sub-actions. We tackle this in a semantically hierarchical manner jointly predicting high-level coarse action labels together with their low-level fine-grained realizations as characteristic 3D human poses. We observe that these two action representations are coupled in nature and joint prediction benefits both action and pose forecasting. Our experiments demonstrate the complementary nature of joint action and 3D pose prediction: our joint approach outperforms each task treated individually enables robust longer-term sequence prediction and improves over alternative approaches to forecast actions and characteristic 3D poses.
NightCC: Nighttime Color Constancy via Adaptive Channel Masking
Authors: Shuwei Li; Robby T. Tan
Venue: CVPR 2024 (main)

Abstract: Nighttime conditions pose a significant challenge to color constancy due to the diversity of lighting conditions and the presence of substantial low-light noise. Existing color constancy methods struggle with nighttime scenes frequently leading to imprecise light color estimations. To tackle nighttime color constancy we propose a novel unsupervised domain adaptation approach that utilizes labeled daytime data to facilitate learning on unlabeled nighttime images. To specifically address the unique lighting conditions of nighttime and ensure the robustness of pseudo labels we propose adaptive channel masking and light uncertainty. By selectively masking channels that are less sensitive to lighting conditions adaptive channel masking directs the model to progressively focus on features less affected by variations in light colors and noise. Additionally our model leverages light uncertainty to provide a pixel-wise uncertainty estimation regarding light color prediction which helps avoid learning from incorrect labels. Our model demonstrates a significant improvement in accuracy achieving 21.5% lower Mean Angular Error (MAE) compared to the state-of-the-art method on our nighttime dataset.
Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations
Authors: Daan de Geus; Gijs Dubbelman
Venue: CVPR 2024 (main)

Abstract: Part-aware panoptic segmentation (PPS) requires (a) that each foreground object and background region in an image is segmented and classified and (b) that all parts within foreground objects are segmented classified and linked to their parent object. Existing methods approach PPS by separately conducting object-level and part-level segmentation. However their part-level predictions are not linked to individual parent objects. Therefore their learning objective is not aligned with the PPS task objective which harms the PPS performance. To solve this and make more accurate PPS predictions we propose Task-Aligned Part-aware Panoptic Segmentation (TAPPS). This method uses a set of shared queries to jointly predict (a) object-level segments and (b) the part-level segments within those same objects. As a result TAPPS learns to predict part-level segments that are linked to individual parent objects aligning the learning objective with the task objective and allowing TAPPS to leverage joint object-part representations. With experiments we show that TAPPS considerably outperforms methods that predict objects and parts separately and achieves new state-of-the-art PPS results.
From Activation to Initialization: Scaling Insights for Optimizing Neural Fields
Authors: Hemanth Saratchandran; Sameera Ramasinghe; Simon Lucey
Venue: CVPR 2024 (main)

Abstract: In the realm of computer vision Neural Fields have gained prominence as a contemporary tool harnessing neural networks for signal representation. Despite the remarkable progress in adapting these networks to solve a variety of problems the field still lacks a comprehensive theoretical framework. This article aims to address this gap by delving into the intricate interplay between initialization and activation providing a foundational basis for the robust optimization of Neural Fields. Our theoretical insights reveal a deep-seated connection among network initialization architectural choices and the optimization process emphasizing the need for a holistic approach when designing cutting-edge Neural Fields.
UnScene3D: Unsupervised 3D Instance Segmentation for Indoor Scenes
Authors: David Rozenberszki; Or Litany; Angela Dai
Venue: CVPR 2024 (main)

Abstract: 3D instance segmentation is fundamental to geometric understanding of the world around us. Existing methods for instance segmentation of 3D scenes rely on supervision from expensive manual 3D annotations. We propose UnScene3D the first fully unsupervised 3D learning approach for class-agnostic 3D instance segmentation of indoor scans. UnScene3D first generates pseudo masks by leveraging self-supervised color and geometry features to find potential object regions. We operate on a basis of 3D segment primitives enabling efficient representation and learning on high-resolution 3D data. The coarse proposals are then refined through self-training our model on its predictions. Our approach improves over state-of-the-art unsupervised 3D instance segmentation methods by more than 300% Average Precision score demonstrating effective instance segmentation even in challenging cluttered 3D scenes.
Nearest is Not Dearest: Towards Practical Defense against Quantization-conditioned Backdoor Attacks
Authors: Boheng Li; Yishuo Cai; Haowei Li; Feng Xue; Zhifeng Li; Yiming Li
Venue: CVPR 2024 (main)

Abstract: Model quantization is widely used to compress and accelerate deep neural networks. However recent studies have revealed the feasibility of weaponizing model quantization via implanting quantization-conditioned backdoors (QCBs). These special backdoors stay dormant on released full-precision models but will come into effect after standard quantization. Due to the peculiarity of QCBs existing defenses have minor effects on reducing their threats or are even infeasible. In this paper we conduct the first in-depth analysis of QCBs. We reveal that the activation of existing QCBs primarily stems from the nearest rounding operation and is closely related to the norms of neuron-wise truncation errors (i.e. the difference between the continuous fullprecision weights and its quantized version). Motivated by these insights we propose Error-guided Flipped Rounding with Activation Preservation (EFRAP) an effective and practical defense against QCBs. Specifically EFRAP learns a non-nearest rounding strategy with neuron-wise error norm and layer-wise activation preservation guidance flipping the rounding strategies of neurons crucial for backdoor effects but with minimal impact on clean accuracy. Extensive evaluations on benchmark datasets demonstrate that our EFRAP can defeat state-of-the-art QCB attacks under various settings. Code is available here.
DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation
Authors: Yifei Li; Hsiao-yu Chen; Egor Larionov; Nikolaos Sarafianos; Wojciech Matusik; Tuur Stuyck
Venue: CVPR 2024 (main)

Abstract: The realism of digital avatars is crucial in enabling telepresence applications with self-expression and customization. While physical simulations can produce realistic motions for clothed humans they require high-quality garment assets with associated physical parameters for cloth simulations. However manually creating these assets and calibrating their parameters is labor-intensive and requires specialized expertise. Current methods focus on reconstructing geometry but don't generate complete assets for physics-based applications. To address this gap we propose DiffAvatar a novel approach that performs body and garment co-optimization using differentiable simulation. By integrating physical simulation into the optimization loop and accounting for the complex nonlinear behavior of cloth and its intricate interaction with the body our framework recovers body and garment geometry and extracts important material parameters in a physically plausible way. Our experiments demonstrate that our approach generates realistic clothing and body shape suitable for downstream applications. We provide additional insights and results on our webpage: people.csail.mit.edu/liyifei/publication/diffavatar.