CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
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.
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/.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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/.
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.