mtybilly/Instruct2DS huggingface dataset:
EarthLoc: Astronaut Photography Localization by Indexing Earth from Space
Authors: Gabriele Berton, Alex Stoken, Barbara Caputo, Carlo Masone
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Astronaut photography spanning six decades of human spaceflight presents a unique Earth observations dataset with immense value for both scientific research and disaster response. Despite its significance accurately localizing the geographical extent of these images crucial for effective utilization poses substantial challenges. Current manual localization efforts are time-consuming motivating the need for automated solutions. We propose a novel approach - leveraging image retrieval - to address this challenge efficiently. We introduce innovative training techniques including Year-Wise Data Augmentation and a Neutral-Aware Multi-Similarity Loss which contribute to the development of a high-performance model EarthLoc. We develop six evaluation datasets and perform a comprehensive benchmark comparing EarthLoc to existing methods showcasing its superior efficiency and accuracy. Our approach marks a significant advancement in automating the localization of astronaut photography which will help bridge a critical gap in Earth observations data. Code and datasets are available at this https://github.com/gmberton/EarthLoc
SmartMask: Context Aware High-Fidelity Mask Generation for Fine-grained Object Insertion and Layout Control
Authors: Jaskirat Singh, Jianming Zhang, Qing Liu, Cameron Smith, Zhe Lin, Liang Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The field of generative image inpainting and object insertion has made significant progress with the recent advent of latent diffusion models. Utilizing a precise object mask can greatly enhance these applications. However due to the challenges users encounter in creating high-fidelity masks there is a tendency for these methods to rely on more coarse masks (e.g. bounding box) for these applications. This results in limited control and compromised background content preservation. To overcome these limitations we introduce SmartMask which allows any novice user to create detailed masks for precise object insertion. Combined with a ControlNet-Inpaint model our experiments demonstrate that SmartMask achieves superior object insertion quality preserving the background content more effectively than previous methods. Notably unlike prior works the proposed approach can also be used even without user-mask guidance which allows it to perform mask-free object insertion at diverse positions and scales. Furthermore we find that when used iteratively with a novel instruction-tuning based planning model SmartMask can be used to design detailed layouts from scratch. As compared with user-scribble based layout design we observe that SmartMask allows for better quality outputs with layout-to-image generation methods.
Text-Image Alignment for Diffusion-Based Perception
Authors: Neehar Kondapaneni, Markus Marks, Manuel Knott, Rogerio Guimaraes, Pietro Perona
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps leading to better perceptual performance. Our approach improves upon the current state-of-the-art in diffusion-based semantic segmentation on ADE20K and the current overall SOTA for depth estimation on NYUv2. Furthermore our method generalizes to the cross-domain setting. We use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our cross-domain object detection model trained on Pascal VOC achieves SOTA results on Watercolor2K. Our cross-domain segmentation method trained on Cityscapes achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: vision.caltech.edu/TADP/. Code: github.com/damaggu/TADP
Customization Assistant for Text-to-Image Generation
Authors: Yufan Zhou, Ruiyi Zhang, Jiuxiang Gu, Tong Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Customizing pre-trained text-to-image generation model has attracted massive research interest recently due to its huge potential in real-world applications. Although existing methods are able to generate creative content for a novel concept contained in single user-input image their capability are still far from perfection. Specifically most existing methods require fine-tuning the generative model on testing images. Some existing methods do not require fine-tuning while their performance are unsatisfactory. Furthermore the interaction between users and models are still limited to directive and descriptive prompts such as instructions and captions. In this work we build a customization assistant based on pre-trained large language model and diffusion model which can not only perform customized generation in a tuning-free manner but also enable more user-friendly interactions: users can chat with the assistant and input either ambiguous text or clear instruction. Specifically we propose a new framework consists of a new model design and a novel training strategy. The resulting assistant can perform customized generation in 2-5 seconds without any test time fine-tuning. Extensive experiments are conducted competitive results have been obtained across different domains illustrating the effectiveness of the proposed method.
GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions
Authors: Junjie Wang, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, Qi Tian
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently impressive results have been achieved in 3D scene editing with text instructions based on a 2D diffusion model. However current diffusion models primarily generate images by predicting noise in the latent space and the editing is usually applied to the whole image which makes it challenging to perform delicate especially localized editing for 3D scenes. Inspired by recent 3D Gaussian splatting we propose a systematic framework named GaussianEditor to edit 3D scenes delicately via 3D Gaussians with text instructions. Benefiting from the explicit property of 3D Gaussians we design a series of techniques to achieve delicate editing. Specifically we first extract the region of interest (RoI) corresponding to the text instruction aligning it to 3D Gaussians. The Gaussian RoI is further used to control the editing process. Our framework can achieve more delicate and precise editing of 3D scenes than previous methods while enjoying much faster training speed i.e. within 20 minutes on a single V100 GPU more than twice as fast as Instruct-NeRF2NeRF (45 minutes -- 2 hours). The project page is at GaussianEditor.github.io.
MemFlow: Optical Flow Estimation and Prediction with Memory
Authors: Qiaole Dong, Yanwei Fu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Optical flow is a classical task that is important to the vision community. Classical optical flow estimation uses two frames as input whilst some recent methods consider multiple frames to explicitly model long-range information. The former ones limit their ability to fully leverage temporal coherence along the video sequence; and the latter ones incur heavy computational overhead typically not possible for real-time flow estimation. Some multi-frame-based approaches even necessitate unseen future frames for current estimation compromising real-time applicability in safety-critical scenarios. To this end we present MemFlow a real-time method for optical flow estimation and prediction with memory. Our method enables memory read-out and update modules for aggregating historical motion information in real-time. Furthermore we integrate resolution-adaptive re-scaling to accommodate diverse video resolutions. Besides our approach seamlessly extends to the future prediction of optical flow based on past observations. Leveraging effective historical motion aggregation our method outperforms VideoFlow with fewer parameters and faster inference speed on Sintel and KITTI-15 datasets in terms of generalization performance. At the time of submission MemFlow also leads in performance on the 1080p Spring dataset. Codes and models will be available at: https://dqiaole.github.io/MemFlow/.
Novel Class Discovery for Ultra-Fine-Grained Visual Categorization
Authors: Yu Liu, Yaqi Cai, Qi Jia, Binglin Qiu, Weimin Wang, Nan Pu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Ultra-fine-grained visual categorization (Ultra-FGVC) aims at distinguishing highly similar sub-categories within fine-grained objects such as different soybean cultivars. Compared to traditional fine-grained visual categorization Ultra-FGVC encounters more hurdles due to the small inter-class and large intra-class variation. Given these challenges relying on human annotation for Ultra-FGVC is impractical. To this end our work introduces a novel task termed Ultra-Fine-Grained Novel Class Discovery (UFG-NCD) which leverages partially annotated data to identify new categories of unlabeled images for Ultra-FGVC. To tackle this problem we devise a Region-Aligned Proxy Learning (RAPL) framework which comprises a Channel-wise Region Alignment (CRA) module and a Semi-Supervised Proxy Learning (SemiPL) strategy. The CRA module is designed to extract and utilize discriminative features from local regions facilitating knowledge transfer from labeled to unlabeled classes. Furthermore SemiPL strengthens representation learning and knowledge transfer with proxy-guided supervised learning and proxy-guided contrastive learning. Such techniques leverage class distribution information in the embedding space improving the mining of subtle differences between labeled and unlabeled ultra-fine-grained classes. Extensive experiments demonstrate that RAPL significantly outperforms baselines across various datasets indicating its effectiveness in handling the challenges of UFG-NCD. Code is available at https://github.com/SSDUT-Caiyq/UFG-NCD.
Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Rendering
Authors: Kim Youwang, Tae-Hyun Oh, Gerard Pons-Moll
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present Paint-it a text-driven high-fidelity texture map synthesis method for 3D meshes via neural re-parameterized texture optimization. Paint-it synthesizes texture maps from a text description by synthesis-through-optimization exploiting the Score-Distillation Sampling (SDS). We observe that directly applying SDS yields undesirable texture quality due to its noisy gradients. We reveal the importance of texture parameterization when using SDS. Specifically we propose Deep Convolutional Physically-Based Rendering (DC-PBR) parameterization which re-parameterizes the physically-based rendering (PBR) texture maps with randomly initialized convolution-based neural kernels instead of a standard pixel-based parameterization. We show that DC-PBR inherently schedules the optimization curriculum according to texture frequency and naturally filters out the noisy signals from SDS. In experiments Paint-it obtains remarkable quality PBR texture maps within 15 min. given only a text description. We demonstrate the generalizability and practicality of Paint-it by synthesizing high-quality texture maps for large-scale mesh datasets and showing test-time applications such as relighting and material control using a popular graphics engine.
HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation
Authors: Ce Zhang, Simon Stepputtis, Joseph Campbell, Katia Sycara, Yaqi Xie
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Being able to understand visual scenes is a precursor for many downstream tasks including autonomous driving robotics and other vision-based approaches. A common approach enabling the ability to reason over visual data is Scene Graph Generation (SGG); however many existing approaches assume undisturbed vision i.e. the absence of real-world corruptions such as fog snow smoke as well as non-uniform perturbations like sun glare or water drops. In this work we propose a novel SGG benchmark containing procedurally generated weather corruptions and other transformations over the Visual Genome dataset. Further we introduce a corresponding approach Hierarchical Knowledge Enhanced Robust Scene Graph Generation (HiKER-SGG) providing a strong baseline for scene graph generation under such challenging setting. At its core HiKER-SGG utilizes a hierarchical knowledge graph in order to refine its predictions from coarse initial estimates to detailed predictions. In our extensive experiments we show that HiKER-SGG does not only demonstrate superior performance on corrupted images in a zero-shot manner but also outperforms current state-of-the-art methods on uncorrupted SGG tasks. Code is available at https://github.com/zhangce01/HiKER-SGG.
DiffusionGAN3D: Boosting Text-guided 3D Generation and Domain Adaptation by Combining 3D GANs and Diffusion Priors
Authors: Biwen Lei, Kai Yu, Mengyang Feng, Miaomiao Cui, Xuansong Xie
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-guided domain adaptation and generation of 3D-aware portraits find many applications in various fields. However due to the lack of training data and the challenges in handling the high variety of geometry and appearance the existing methods for these tasks suffer from issues like inflexibility instability and low fidelity. In this paper we propose a novel framework DiffusionGAN3D which boosts text-guided 3D domain adaptation and generation by combining 3D GANs and diffusion priors. Specifically we integrate the pre-trained 3D generative models (e.g. EG3D) and text-to-image diffusion models. The former provides a strong foundation for stable and high-quality avatar generation from text. And the diffusion models in turn offer powerful priors and guide the 3D generator finetuning with informative direction to achieve flexible and efficient text-guided domain adaptation. To enhance the diversity in domain adaptation and the generation capability in text-to-avatar we introduce the relative distance loss and case-specific learnable triplane respectively. Besides we design a progressive texture refinement module to improve the texture quality for both tasks above. Extensive experiments demonstrate that the proposed framework achieves excellent results in both domain adaptation and text-to-avatar tasks outperforming existing methods in terms of generation quality and efficiency. The project homepage is at https://younglbw.github.io/DiffusionGAN3D-homepage/.
Physics-Aware Hand-Object Interaction Denoising
Authors: Haowen Luo, Yunze Liu, Li Yi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The credibility and practicality of a reconstructed hand-object interaction sequence depend largely on its physical plausibility. However due to high occlusions during hand-object interaction physical plausibility remains a challenging criterion for purely vision-based tracking methods. To address this issue and enhance the results of existing hand trackers this paper proposes a novel physically-aware hand motion de-noising method. Specifically we introduce two learned loss terms that explicitly capture two crucial aspects of physical plausibility: grasp credibility and manipulation feasibility. These terms are used to train a physically-aware de-noising network. Qualitative and quantitative experiments demonstrate that our approach significantly improves both fine-grained physical plausibility and overall pose accuracy surpassing current state-of-the-art de-noising methods.
VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction
Authors: Jiaqi Lin, Zhihao Li, Xiao Tang, Jianzhuang Liu, Shiyong Liu, Jiayue Liu, Yangdi Lu, Xiaofei Wu, Songcen Xu, Youliang Yan, Wenming Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing NeRF-based methods for large scene reconstruction often have limitations in visual quality and rendering speed. While the recent 3D Gaussian Splatting works well on small-scale and object-centric scenes scaling it up to large scenes poses challenges due to limited video memory long optimization time and noticeable appearance variations. To address these challenges we present VastGaussian the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. We propose a progressive partitioning strategy to divide a large scene into multiple cells where the training cameras and point cloud are properly distributed with an airspace-aware visibility criterion. These cells are merged into a complete scene after parallel optimization. We also introduce decoupled appearance modeling into the optimization process to reduce appearance variations in the rendered images. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets enabling fast optimization and high-fidelity real-time rendering.
Edit One for All: Interactive Batch Image Editing
Authors: Thao Nguyen, Utkarsh Ojha, Yuheng Li, Haotian Liu, Yong Jae Lee
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In recent years image editing has advanced remarkably. With increased human control it is now possible to edit an image in a plethora of ways; from specifying in text what we want to change to straight up dragging the contents of the image in an interactive point-based manner. However most of the focus has remained on editing single images at a time. Whether and how we can simultaneously edit large batches of images has remained understudied. With the goal of minimizing human supervision in the editing process this paper presents a novel method for interactive batch image editing using StyleGAN as the medium. Given an edit specified by users in an example image (e.g. make the face frontal) our method can automatically transfer that edit to other test images so that regardless of their initial state (pose) they all arrive at the same final state (e.g. all facing front). Extensive experiments demonstrate that edits performed using our method have similar visual quality to existing single-image-editing methods while having more visual consistency and saving significant time and human effort.
Rethinking Boundary Discontinuity Problem for Oriented Object Detection
Authors: Hang Xu, Xinyuan Liu, Haonan Xu, Yike Ma, Zunjie Zhu, Chenggang Yan, Feng Dai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Oriented object detection has been developed rapidly in the past few years where rotation equivariance is crucial for detectors to predict rotated boxes. It is expected that the prediction can maintain the corresponding rotation when objects rotate but severe mutation in angular prediction is sometimes observed when objects rotate near the boundary angle which is well-known boundary discontinuity problem. The problem has been long believed to be caused by the sharp loss increase at the angular boundary and widely used joint-optim IoU-like methods deal with this problem by loss-smoothing. However we experimentally find that even state-of-the-art IoU-like methods actually fail to solve the problem. On further analysis we find that the key to solution lies in encoding mode of the smoothing function rather than in joint or independent optimization. In existing IoU-like methods the model essentially attempts to fit the angular relationship between box and object where the break point at angular boundary makes the predictions highly unstable. To deal with this issue we propose a dual-optimization paradigm for angles. We decouple reversibility and joint-optim from single smoothing function into two distinct entities which for the first time achieves the objectives of both correcting angular boundary and blending angle with other parameters. Extensive experiments on multiple datasets show that boundary discontinuity problem is well-addressed. Moreover typical IoU-like methods are improved to the same level without obvious performance gap. The code is available at https://github.com/hangxu-cv/cvpr24acm.
Deformable One-shot Face Stylization via DINO Semantic Guidance
Authors: Yang Zhou, Zichong Chen, Hui Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper addresses the complex issue of one-shot face stylization focusing on the simultaneous consideration of appearance and structure where previous methods have fallen short. We explore deformation-aware face stylization that diverges from traditional single-image style reference opting for a real-style image pair instead. The cornerstone of our method is the utilization of a self-supervised vision transformer specifically DINO-ViT to establish a robust and consistent facial structure representation across both real and style domains. Our stylization process begins by adapting the StyleGAN generator to be deformation-aware through the integration of spatial transformers (STN). We then introduce two innovative constraints for generator fine-tuning under the guidance of DINO semantics: i) a directional deformation loss that regulates directional vectors in DINO space and ii) a relative structural consistency constraint based on DINO token self-similarities ensuring diverse generation. Additionally style-mixing is employed to align the color generation with the reference minimizing inconsistent correspondences. This framework delivers enhanced deformability for general one-shot face stylization achieving notable efficiency with a fine-tuning duration of approximately 10 minutes. Extensive qualitative and quantitative comparisons demonstrate our superiority over state-of-the-art one-shot face stylization methods. Code is available at https://github.com/zichongc/DoesFS
SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers
Authors: Jonathan F. Carter, João Jorge, Oliver Gibson, Lionel Tarassenko
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Advances in camera-based physiological monitoring have enabled the robust non-contact measurement of respiration and the cardiac pulse which are known to be indicative of the sleep stage. This has led to research into camera-based sleep monitoring as a promising alternative to "gold-standard" polysomnography which is cumbersome expensive to administer and hence unsuitable for longer-term clinical studies. In this paper we introduce SleepVST a transformer model which enables state-of-the-art performance in camera-based sleep stage classification (sleep staging). After pre-training on contact sensor data SleepVST outperforms existing methods for cardio-respiratory sleep staging on the SHHS and MESA datasets achieving total Cohen's kappa scores of 0.75 and 0.77 respectively. We then show that SleepVST can be successfully transferred to cardio-respiratory waveforms extracted from video enabling fully contact-free sleep staging. Using a video dataset of 50 nights we achieve a total accuracy of 78.8% and a Cohen's \kappa of 0.71 in four-class video-based sleep staging setting a new state-of-the-art in the domain.
Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis
Authors: Yanzuo Lu, Manlin Zhang, Andy J Ma, Xiaohua Xie, Jianhuang Lai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Diffusion model is a promising approach to image generation and has been employed for Pose-Guided Person Image Synthesis (PGPIS) with competitive performance. While existing methods simply align the person appearance to the target pose they are prone to overfitting due to the lack of a high-level semantic understanding on the source person image. In this paper we propose a novel Coarse-to-Fine Latent Diffusion (CFLD) method for PGPIS. In the absence of image-caption pairs and textual prompts we develop a novel training paradigm purely based on images to control the generation process of a pre-trained text-to-image diffusion model. A perception-refined decoder is designed to progressively refine a set of learnable queries and extract semantic understanding of person images as a coarse-grained prompt. This allows for the decoupling of fine-grained appearance and pose information controls at different stages and thus circumventing the potential overfitting problem. To generate more realistic texture details a hybrid-granularity attention module is proposed to encode multi-scale fine-grained appearance features as bias terms to augment the coarse-grained prompt. Both quantitative and qualitative experimental results on the DeepFashion benchmark demonstrate the superiority of our method over the state of the arts for PGPIS. Code is available at https://github.com/YanzuoLu/CFLD.
Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models
Authors: Peifei Zhu, Tsubasa Takahashi, Hirokatsu Kataoka
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Diffusion Models (DMs) have shown remarkable capabilities in various image-generation tasks. However there are growing concerns that DMs could be used to imitate unauthorized creations and thus raise copyright issues. To address this issue we propose a novel framework that embeds personal watermarks in the generation of adversarial examples. Such examples can force DMs to generate images with visible watermarks and prevent DMs from imitating unauthorized images. We construct a generator based on conditional adversarial networks and design three losses (adversarial loss GAN loss and perturbation loss) to generate adversarial examples that have subtle perturbation but can effectively attack DMs to prevent copyright violations. Training a generator for a personal watermark by our method only requires 5-10 samples within 2-3 minutes and once the generator is trained it can generate adversarial examples with that watermark significantly fast (0.2s per image). We conduct extensive experiments in various conditional image-generation scenarios. Compared to existing methods that generate images with chaotic textures our method adds visible watermarks on the generated images which is a more straightforward way to indicate copyright violations. We also observe that our adversarial examples exhibit good transferability across unknown generative models. Therefore this work provides a simple yet powerful way to protect copyright from DM-based imitation.
TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model
Authors: Hantao Yao, Rui Zhang, Changsheng Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Prompt tuning represents a valuable technique for adapting pre-trained visual-language models (VLM) to various downstream tasks. Recent advancements in CoOp-based methods propose a set of learnable domain-shared or image-conditional textual tokens to facilitate the generation of task-specific textual classifiers. However those textual tokens have a limited generalization ability regarding unseen domains as they cannot dynamically adjust to the distribution of testing classes. To tackle this issue we present a novel Textual-based Class-aware Prompt tuning(TCP) that explicitly incorporates prior knowledge about classes to enhance their discriminability. The critical concept of TCP involves leveraging Textual Knowledge Embedding (TKE) to map the high generalizability of class-level textual knowledge into class aware textual tokens. By seamlessly integrating these class-aware prompts into the Text Encoder a dynamic class-aware classifier is generated to enhance discriminability for unseen domains. During inference TKE dynamically generates class-aware prompts related to the unseen classes. Comprehensive evaluations demonstrate that TKE serves as a plug-and-play module effortlessly combinable with existing methods. Furthermore TCP consistently achieves superior performance while demanding less training time.
OMG: Towards Open-vocabulary Motion Generation via Mixture of Controllers
Authors: Han Liang, Jiacheng Bao, Ruichi Zhang, Sihan Ren, Yuecheng Xu, Sibei Yang, Xin Chen, Jingyi Yu, Lan Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We have recently seen tremendous progress in realistic text-to-motion generation. Yet the existing methods often fail or produce implausible motions with unseen text inputs which limits the applications. In this paper we present OMG a novel framework which enables compelling motion generation from zero-shot open-vocabulary text prompts. Our key idea is to carefully tailor the pretrain-then-finetune paradigm into the text-to-motion generation. At the pre-training stage our model improves the generation ability by learning the rich out-of-domain inherent motion traits. To this end we scale up a large unconditional diffusion model up to 1B parameters so as to utilize the massive unlabeled motion data up to over 20M motion instances. At the subsequent fine-tuning stage we introduce motion ControlNet which incorporates text prompts as conditioning information through a trainable copy of the pre-trained model and the proposed novel Mixture-of-Controllers (MoC) block. MoC block adaptively recognizes various ranges of the sub-motions with a cross-attention mechanism and processes them separately with the text-token-specific experts. Such a design effectively aligns the CLIP token embeddings of text prompts to various ranges of compact and expressive motion features. Extensive experiments demonstrate that our OMG achieves significant improvements over the state-of-the-art methods on zero-shot text-to-motion generation. Project page: https://tr3e.github.io/omg-page.
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
Authors: Shuhuai Ren, Linli Yao, Shicheng Li, Xu Sun, Lu Hou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This work proposes TimeChat a time-sensitive multimodal large language model specifically designed for long video understanding. Our model incorporates two key architectural contributions: (1) a timestamp-aware frame encoder that binds visual content with the timestamp of each frame and (2) a sliding video Q-Former that produces a video token sequence of varying lengths to accommodate videos of various durations. Additionally we construct an instruction-tuning dataset encompassing 6 tasks and a total of 125K instances to further enhance TimeChat's instruction-following performance. Experiment results across various video understanding tasks such as dense captioning temporal grounding and highlight detection demonstrate TimeChat's strong zero-shot temporal localization and reasoning capabilities. For example it achieves +9.2 F1 score and +2.8 CIDEr on YouCook2 +5.8 HIT@1 on QVHighlights and +27.5 R@1 (IoU=0.5) on Charades-STA compared to state-of-the-art video large language models holding the potential to serve as a versatile video assistant for long-form video comprehension tasks and satisfy realistic user requirements.
Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models
Authors: Huan Ling, Seung Wook Kim, Antonio Torralba, Sanja Fidler, Karsten Kreis
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-guided diffusion models have revolutionized image and video generation and have also been successfully used for optimization-based 3D object synthesis. Here we instead focus on the underexplored text-to-4D setting and synthesize dynamic animated 3D objects using score distillation methods with an additional temporal dimension. Compared to previous work we pursue a novel compositional generation-based approach and combine text-to-image text-to-video and 3D-aware multiview diffusion models to provide feedback during 4D object optimization thereby simultaneously enforcing temporal consistency high-quality visual appearance and realistic geometry. Our method called Align Your Gaussians (AYG) leverages dynamic 3D Gaussian Splatting with deformation fields as 4D representation. Crucial to AYG is a novel method to regularize the distribution of the moving 3D Gaussians and thereby stabilize the optimization and induce motion. We also propose a motion amplification mechanism as well as a new autoregressive synthesis scheme to generate and combine multiple 4D sequences for longer generation. These techniques allow us to synthesize vivid dynamic scenes outperform previous work qualitatively and quantitatively and achieve state-of-the-art text-to-4D performance. Due to the Gaussian 4D representation different 4D animations can be seamlessly combined as we demonstrate. AYG opens up promising avenues for animation simulation and digital content creation as well as synthetic data generation.
PDF: A Probability-Driven Framework for Open World 3D Point Cloud Semantic Segmentation
Authors: Jinfeng Xu, Siyuan Yang, Xianzhi Li, Yuan Tang, Yixue Hao, Long Hu, Min Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing point cloud semantic segmentation networks cannot identify unknown classes and update their knowledge due to a closed-set and static perspective of the real world which would induce the intelligent agent to make bad decisions. To address this problem we propose a Probability-Driven Framework (PDF) for open world semantic segmentation that includes (i) a lightweight U-decoder branch to identify unknown classes by estimating the uncertainties (ii) a flexible pseudo-labeling scheme to supply geometry features along with probability distribution features of unknown classes by generating pseudo labels and (iii) an incremental knowledge distillation strategy to incorporate novel classes into the existing knowledge base gradually. Our framework enables the model to behave like human beings which could recognize unknown objects and incrementally learn them with the corresponding knowledge. Experimental results on the S3DIS and ScanNetv2 datasets demonstrate that the proposed PDF outperforms other methods by a large margin in both important tasks of open world semantic segmentation.
Test-Time Domain Generalization for Face Anti-Spoofing
Authors: Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu, Shouhong Ding, Lizhuang Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Face Anti-Spoofing (FAS) is pivotal in safeguarding facial recognition systems against presentation attacks. While domain generalization (DG) methods have been developed to enhance FAS performance they predominantly focus on learning domain-invariant features during training which may not guarantee generalizability to unseen data that differs largely from the source distributions. Our insight is that testing data can serve as a valuable resource to enhance the generalizability beyond mere evaluation for DG FAS. In this paper we introduce a novel Test-Time Domain Generalization (TTDG) framework for FAS which leverages the testing data to boost the model's generalizability. Our method consisting of Test-Time Style Projection (TTSP) and Diverse Style Shifts Simulation (DSSS) effectively projects the unseen data to the seen domain space. In particular we first introduce the innovative TTSP to project the styles of the arbitrarily unseen samples of the testing distribution to the known source space of the training distributions. We then design the efficient DSSS to synthesize diverse style shifts via learnable style bases with two specifically designed losses in a hyperspherical feature space. Our method eliminates the need for model updates at the test time and can be seamlessly integrated into not only the CNN but also ViT backbones. Comprehensive experiments on widely used cross-domain FAS benchmarks demonstrate our method's state-of-the-art performance and effectiveness.
DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data
Authors: Hanrong Ye, Dan Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently there has been an increased interest in the practical problem of learning multiple dense scene understanding tasks from partially annotated data where each training sample is only labeled for a subset of the tasks. The missing of task labels in training leads to low-quality and noisy predictions as can be observed from state-of-the-art methods. To tackle this issue we reformulate the partially-labeled multi-task dense prediction as a pixel-level denoising problem and propose a novel multi-task denoising diffusion framework coined as DiffusionMTL. It designs a joint diffusion and denoising paradigm to model a potential noisy distribution in the task prediction or feature maps and generate rectified outputs for different tasks. To exploit multi-task consistency in denoising we further introduce a Multi-Task Conditioning strategy which can implicitly utilize the complementary nature of the tasks to help learn the unlabeled tasks leading to an improvement in the denoising performance of the different tasks. Extensive quantitative and qualitative experiments demonstrate that the proposed multi-task denoising diffusion model can significantly improve multi-task prediction maps and outperform the state-of-the-art methods on three challenging multi-task benchmarks under two different partial-labeling evaluation settings. The code is available at https://prismformore.github.io/diffusionmtl/.
Spike-guided Motion Deblurring with Unknown Modal Spatiotemporal Alignment
Authors: Jiyuan Zhang, Shiyan Chen, Yajing Zheng, Zhaofei Yu, Tiejun Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The traditional frame-based cameras that rely on exposure windows for imaging experience motion blur in high-speed scenarios. Frame-based deblurring methods lack reliable motion cues to restore sharp images under extreme blur conditions. The spike camera is a novel neuromorphic visual sensor that outputs spike streams with ultra-high temporal resolution. It can supplement the temporal information lost in traditional cameras and guide motion deblurring. However in real-world scenarios aligning discrete RGB images and continuous spike streams along both temporal and spatial axes is challenging due to the complexity of calibrating their coordinates device displacements in vibrations and time deviations. Misalignment of pixels leads to severe degradation of deblurring. We introduce the first framework for spike-guided motion deblurring without knowing the spatiotemporal alignment between spikes and images. To address the problem we first propose a novel three-stage network containing a basic deblurring net a carefully designed bi-directional deformable aligning module and a flow-based multi-scale fusion net. Experimental results demonstrate that our approach can effectively guide the image deblurring with unknown alignment surpassing the performance of other methods. Public project page: https://github.com/Leozhangjiyuan/UaSDN.
VRP-SAM: SAM with Visual Reference Prompt
Authors: Yanpeng Sun, Jiahui Chen, Shan Zhang, Xinyu Zhang, Qiang Chen, Gang Zhang, Errui Ding, Jingdong Wang, Zechao Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation creating the VRP-SAM model. In essence VRP-SAM can utilize annotated reference images to comprehend specific objects and perform segmentation of specific objects in target image. It is note that the VRP encoder can support a variety of annotation formats for reference images including point box scribble and mask. VRP-SAM achieves a breakthrough within the SAM framework by extending its versatility and applicability while preserving SAM's inherent strengths thus enhancing user-friendliness. To enhance the generalization ability of VRP-SAM the VRP encoder adopts a meta-learning strategy. To validate the effectiveness of VRP-SAM we conducted extensive empirical studies on the Pascal and COCO datasets. Remarkably VRP-SAM achieved state-of-the-art performance in visual reference segmentation with minimal learnable parameters. Furthermore VRP-SAM demonstrates strong generalization capabilities allowing it to perform segmentation of unseen objects and enabling cross-domain segmentation. The source code and models will be available at https://github.com/syp2ysy/VRP-SAM
Discriminability-Driven Channel Selection for Out-of-Distribution Detection
Authors: Yue Yuan, Rundong He, Yicong Dong, Zhongyi Han, Yilong Yin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world environments. Activation-based methods are a key approach in OOD detection working to mitigate overconfident predictions of OOD data. These techniques rectifying anomalous activations enhancing the distinguishability between in-distribution (ID) data and OOD data. However they assume by default that every channel is necessary for OOD detection and rectify anomalous activations in each channel. Empirical evidence has shown that there is a significant difference among various channels in OOD detection and discarding some channels can greatly enhance the performance of OOD detection. Based on this insight we propose \underline D iscriminability-\underline D riven \underline C hannel \underline S election (DDCS) which leverages an adaptive channel selection by estimating the discriminative score of each channel to boost OOD detection. The discriminative score takes inter-class similarity and inter-class variance of training data into account. However the estimation of discriminative score itself is susceptible to anomalous activations. To better estimate score we pre-rectify anomalous activations for each channel mildly. The experimental results show that DDCS achieves state-of-the-art performance on CIFAR and ImageNet-1K benchmarks. Moreover DDCS can generalize to different backbones and OOD scores.
ManiFPT: Defining and Analyzing Fingerprints of Generative Models
Authors: Hae Jin Song, Mahyar Khayatkhoei, Wael AbdAlmageed
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent works have shown that generative models leave traces of their underlying generative process on the generated samples broadly referred to as fingerprints of a generative model and have studied their utility in detecting synthetic images from real ones. However the extend to which these fingerprints can distinguish between various types of synthetic image and help identify the underlying generative process remain under-explored. In particular the very definition of a fingerprint remains unclear to our knowledge. To that end in this work we formalize the definition of artifact and fingerprint in generative models propose an algorithm for computing them in practice and finally study its effectiveness in distinguishing a large array of different generative models. We find that using our proposed definition can significantly improve the performance on the task of identifying the underlying generative process from samples (model attribution) compared to existing methods. Additionally we study the structure of the fingerprints and observe that it is very predictive of the effect of different design choices on the generative process.
Real-time 3D-aware Portrait Video Relighting
Authors: Ziqi Cai, Kaiwen Jiang, Shu-Yu Chen, Yu-Kun Lai, Hongbo Fu, Boxin Shi, Lin Gao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Synthesizing realistic videos of talking faces under custom lighting conditions and viewing angles benefits various downstream applications like video conferencing. However most existing relighting methods are either time-consuming or unable to adjust the viewpoints. In this paper we present the first real-time 3D-aware method for relighting in-the-wild videos of talking faces based on Neural Radiance Fields (NeRF). Given an input portrait video our method can synthesize talking faces under both novel views and novel lighting conditions with a photo-realistic and disentangled 3D representation. Specifically we infer an albedo tri-plane as well as a shading tri-plane based on a desired lighting condition for each video frame with fast dual-encoders. We also leverage a temporal consistency network to ensure smooth transitions and reduce flickering artifacts. Our method runs at 32.98 fps on consumer-level hardware and achieves state-of-the-art results in terms of reconstruction quality lighting error lighting instability temporal consistency and inference speed. We demonstrate the effectiveness and interactivity of our method on various portrait videos with diverse lighting and viewing conditions.
3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting
Authors: Zhiyin Qian, Shaofei Wang, Marko Mihajlovic, Andreas Geiger, Siyu Tang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce an approach that creates animatable human avatars from monocular videos using 3D Gaussian Splatting (3DGS). Existing methods based on neural radiance fields (NeRFs) achieve high-quality novel-view/novel-pose image synthesis but often require days of training and are extremely slow at inference time. Recently the community has explored fast grid structures for efficient training of clothed avatars. Albeit being extremely fast at training these methods can barely achieve an interactive rendering frame rate with around 15 FPS. In this paper we use 3D Gaussian Splatting and learn a non-rigid deformation network to reconstruct animatable clothed human avatars that can be trained within 30 minutes and rendered at real-time frame rates (50+ FPS). Given the explicit nature of our representation we further introduce as-isometric-as-possible regularizations on both the Gaussian mean vectors and the covariance matrices enhancing the generalization of our model on highly articulated unseen poses. Experimental results show that our method achieves comparable and even better performance compared to state-of-the-art approaches on animatable avatar creation from a monocular input while being 400x and 250x faster in training and inference respectively.