CVPR 2024 论文列表

Face personalization aims to insert specific faces taken from images into pretrained text-to-image diffusion models. However it is still challenging for previous methods to preserve both the identity similarity and editability due to overfitting to training samples. In this paper we propose Face2Diffusion (F2D) for high-editability face personalization. The core idea behind F2D is that removing identity-irrelevant information from the training pipeline prevents the overfitting problem and improves editability of encoded faces. F2D consists of the following three novel components: 1) Multi-scale identity encoder provides well-disentangled identity features while keeping the benefits of multi-scale information which improves the diversity of camera poses. 2) Expression guidance disentangles face expressions from identities and improves the controllability of face expressions. 3) Class-guided denoising regularization encourages models to learn how faces should be denoised which boosts the text-alignment of backgrounds. Extensive experiments on the FaceForensics++ dataset and diverse prompts demonstrate our method greatly improves the trade-off between the identity- and text-fidelity compared to previous state-of-the-art methods. Code is available at https://github.com/mapooon/Face2Diffusion.

The remarkable efficacy of text-to-image diffusion models has motivated extensive exploration of their potential application in video domains. Zero-shot methods seek to extend image diffusion models to videos without necessitating model training. Recent methods mainly focus on incorporating inter-frame correspondence into attention mechanisms. However the soft constraint imposed on determining where to attend to valid features can sometimes be insufficient resulting in temporal inconsistency. In this paper we introduce FRESCO intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint. This enhancement ensures a more consistent transformation of semantically similar content across frames. Beyond mere attention guidance our approach involves an explicit update of features to achieve high spatial-temporal consistency with the input video significantly improving the visual coherence of the resulting translated videos. Extensive experiments demonstrate the effectiveness of our proposed framework in producing high-quality coherent videos marking a notable improvement over existing zero-shot methods.

We propose ExtraNeRF a novel method for extrapolating the range of views handled by a Neural Radiance Field (NeRF). Our main idea is to leverage NeRFs to model scene-specific fine-grained details while capitalizing on diffusion models to extrapolate beyond our observed data. A key ingredient is to track visibility to determine what portions of the scene have not been observed and focus on reconstructing those regions consistently with diffusion models. Our primary contributions include a visibility-aware diffusion-based inpainting module that is fine-tuned on the input imagery yielding an initial NeRF with moderate quality (often blurry) inpainted regions followed by a second diffusion model trained on the input imagery to consistently enhance notably sharpen the inpainted imagery from the first pass. We demonstrate high-quality results extrapolating beyond a small number of (typically six or fewer) input views effectively outpainting the NeRF as well as inpainting newly disoccluded regions inside the original viewing volume. We compare with related work both quantitatively and qualitatively and show significant gains over prior art.

Video prediction is a challenging task due to its nature of uncertainty especially for forecasting a long period. To model the temporal dynamics advanced methods benefit from the recent success of diffusion models and repeatedly refine the predicted future frames with 3D spatiotemporal U-Net. However there exists a gap between the present and future and the repeated usage of U-Net brings a heavy computation burden. To address this we propose a diffusion-based video prediction method that predicts future frames by extrapolating the present distribution of features namely ExtDM. Specifically our method consists of three components: (i) a motion autoencoder conducts a bijection transformation between video frames and motion cues; (ii) a layered distribution adaptor module extrapolates the present features in the guidance of Gaussian distribution; (iii) a 3D U-Net architecture specialized for jointly fusing guidance and features among the temporal dimension by spatiotemporal-window attention. Extensive experiments on five popular benchmarks covering short- and long-term video prediction verify the effectiveness of ExtDM.

Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate due to the immitigable domain gap. We introduce DMP a pipeline utilizing pre-trained T2I models as a prior for dense prediction tasks. To address the misalignment between deterministic prediction tasks and stochastic T2I models we reformulate the diffusion process through a sequence of interpolations establishing a deterministic mapping between input RGB images and output prediction distributions. To preserve generalizability we use low-rank adaptation to fine-tune pre-trained models. Extensive experiments across five tasks including 3D property estimation semantic segmentation and intrinsic image decomposition showcase the efficacy of the proposed method. Despite limited-domain training data the approach yields faithful estimations for arbitrary images surpassing existing state-of-the-art algorithms.

We introduce EscherNet a multi-view conditioned diffusion model for view synthesis. EscherNet learns implicit and generative 3D representations coupled with a specialised camera positional encoding allowing precise and continuous relative control of the camera transformation between an arbitrary number of reference and target views. EscherNet offers exceptional generality flexibility and scalability in view synthesis --- it can generate more than 100 consistent target views simultaneously on a single consumer-grade GPU despite being trained with a fixed number of 3 reference views to 3 target views. As a result EscherNet not only addresses zero-shot novel view synthesis but also naturally unifies single- and multi-image 3D reconstruction combining these diverse tasks into a single cohesive framework. Our extensive experiments demonstrate that EscherNet achieves state-of-the-art performance in multiple benchmarks even when compared to methods specifically tailored for each individual problem. This remarkable versatility opens up new directions for designing scalable neural architectures for 3D vision. Project page: https://kxhit.github.io/EscherNet.

Generating multiview images from a single view facilitates the rapid generation of a 3D mesh conditioned on a single image. Recent methods that introduce 3D global representation into diffusion models have shown the potential to generate consistent multiviews but they have reduced generation speed and face challenges in maintaining generalizability and quality. To address this issue we propose EpiDiff a localized interactive multiview diffusion model. At the core of the proposed approach is to insert a lightweight epipolar attention block into the frozen diffusion model leveraging epipolar constraints to enable cross-view interaction among feature maps of neighboring views. The newly initialized 3D modeling module preserves the original feature distribution of the diffusion model exhibiting compatibility with a variety of base diffusion models. Experiments show that EpiDiff generates 16 multiview images in just 12 seconds and it surpasses previous methods in quality evaluation metrics including PSNR SSIM and LPIPS. Additionally EpiDiff can generate a more diverse distribution of views improving the reconstruction quality from generated multiviews. Please see the project page at https://huanngzh.github.io/EpiDiff/.

Leveraging multi-view diffusion models as priors for 3D optimization have alleviated the problem of 3D consistency e.g. the Janus face problem or the content drift problem in zero-shot text-to-3D models. However the 3D geometric fidelity of the output remains an unresolved issue; albeit the rendered 2D views are realistic the underlying geometry may contain errors such as unreasonable concavities. In this work we propose CorrespondentDream an effective method to leverage annotation-free cross-view correspondences yielded from the diffusion U-Net to provide additional 3D prior to the NeRF optimization process. We find that these correspondences are strongly consistent with human perception and by adopting it in our loss design we are able to produce NeRF models with geometries that are more coherent with common sense e.g. more smoothed object surface yielding higher 3D fidelity. We demonstrate the efficacy of our approach through various comparative qualitative results and a solid user study.

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.

Existing methods for synthesizing 3D human gestures from speech have shown promising results but they do not explicitly model the impact of emotions on the generated gestures. Instead these methods directly output animations from speech without control over the expressed emotion. To address this limitation we present AMUSE an emotional speech-driven body animation model based on latent diffusion. Our observation is that content (i.e. gestures related to speech rhythm and word utterances) emotion and personal style are separable. To account for this AMUSE maps the driving audio to three disentangled latent vectors: one for content one for emotion and one for personal style. A latent diffusion model trained to generate gesture motion sequences is then conditioned on these latent vectors. Once trained AMUSE synthesizes 3D human gestures directly from speech with control over the expressed emotions and style by combining the content from the driving speech with the emotion and style of another speech sequence. Randomly sampling the noise of the diffusion model further generates variations of the gesture with the same emotional expressivity. Qualitative quantitative and perceptual evaluations demonstrate that AMUSE outputs realistic gesture sequences. Compared to the state of the art the generated gestures are better synchronized with the speech content and better represent the emotion expressed by the input speech. Our code is available at amuse.is.tue.mpg.de.

Recent years have witnessed remarkable progress in image generation task where users can create visually astonishing images with high-quality. However exsiting text-to-image diffusion models are proficient in generating concrete concepts (dogs) but encounter challenges with more abstract ones (emotions). Several efforts have been made to modify image emotions with color and style adjustments facing limitations in effectively conveying emotions with fixed image contents. In this work we introduce Emotional Image Content Generation (EIGC) a new task to generate semantic-clear and emotion-faithful images given emotion categories. Specifically we propose an emotion space and construct a mapping network to align it with powerful Contrastive Language-Image Pre-training (CLIP) space providing a concrete interpretation of abstract emotions. Attribute loss and emotion confidence are further proposed to ensure the semantic diversity and emotion fidelity of the generated images. Our method outperforms the state-the-art text-to-image approaches both quantitatively and qualitatively where we derive three custom metrics i.e.emotion accuracy semantic clarity and semantic diversity. In addition to generation our method can help emotion understanding and inspire emotional art design. Project page: https://vcc.tech/research/2024/EmoGen.

Diffusion models have revolutionized image generation in recent years yet they are still limited to a few sizes and aspect ratios. We propose ElasticDiffusion a novel training-free decoding method that enables pretrained text-to-image diffusion models to generate images with various sizes. ElasticDiffusion attempts to decouple the generation trajectory of a pretrained model into local and global signals. The local signal controls low-level pixel information and can be estimated on local patches while the global signal is used to maintain overall structural consistency and is estimated with a reference image. We test our method on CelebA-HQ (faces) and LAION-COCO (objects/indoor/outdoor scenes). Our experiments and qualitative results show superior image coherence quality across aspect ratios compared to MultiDiffusion and the standard decoding strategy of Stable Diffusion. Project Webpage: https://elasticdiffusion.github.io

In this work we explore egocentric whole-body motion capture using a single fisheye camera which simultaneously estimates human body and hand motion. This task presents significant challenges due to three factors: the lack of high-quality datasets fisheye camera distortion and human body self-occlusion. To address these challenges we propose a novel approach that leverages FisheyeViT to extract fisheye image features which are subsequently converted into pixel-aligned 3D heatmap representations for 3D human body pose prediction. For hand tracking we incorporate dedicated hand detection and hand pose estimation networks for regressing 3D hand poses. Finally we develop a diffusion-based whole-body motion prior model to refine the estimated whole-body motion while accounting for joint uncertainties. To train these networks we collect a large synthetic dataset EgoWholeBody comprising 840000 high-quality egocentric images captured across a diverse range of whole-body motion sequences. Quantitative and qualitative evaluations demonstrate the effectiveness of our method in producing high-quality whole-body motion estimates from a single egocentric camera.

While image diffusion models have made significant progress in text-driven 3D content creation they often fail to accurately capture the intended meaning of text prompts especially for view information. This limitation leads to the Janus problem where multi-faced 3D models are generated under the guidance of such diffusion models. In this paper we propose a robust high-quality 3D content generation pipeline by exploiting orthogonal-view image guidance. First we introduce a novel 2D diffusion model that generates an image consisting of four orthogonal-view sub-images based on the given text prompt. Then the 3D content is created using this diffusion model. Notably the generated orthogonal-view image provides strong geometric structure priors and thus improves 3D consistency. As a result it effectively resolves the Janus problem and significantly enhances the quality of 3D content creation. Additionally we present a 3D synthesis fusion network that can further improve the details of the generated 3D contents. Both quantitative and qualitative evaluations demonstrate that our method surpasses previous text-to-3D techniques. Project page: https://efficientdreamer.github.io.

We introduce Deformable Convolution v4 (DCNv4) a highly efficient and effective operator designed for a broad spectrum of vision applications. DCNv4 addresses the limitations of its predecessor DCNv3 with two key enhancements: 1. removing softmax normalization in spatial aggregation to enhance its dynamic property and expressive power and 2. optimizing memory access to minimize redundant operations for speedup. These improvements result in a significantly faster convergence compared to DCNv3 and a substantial increase in processing speed with DCNv4 achieving more than three times the forward speed. DCNv4 demonstrates exceptional performance across various tasks including image classification instance and semantic segmentation and notably image generation. When integrated into generative models like U-Net in the latent diffusion model DCNv4 outperforms its baseline underscoring its possibility to enhance generative models. In practical applications replacing DCNv3 with DCNv4 in the InternImage model to create FlashInternImage results in up to 80% speed increase and further performance improvement without further modifications. The advancements in speed and efficiency of DCNv4 combined with its robust performance across diverse vision tasks show its potential as a foundational building block for future vision models.

Dataset distillation reduces the storage and computational consumption of training a network by generating a small surrogate dataset that encapsulates rich information of the original large-scale one. However previous distillation methods heavily rely on the sample-wise iterative optimization scheme. As the images-per-class (IPC) setting or image resolution grows larger the necessary computation will demand overwhelming time and resources. In this work we intend to incorporate generative diffusion techniques for computing the surrogate dataset. Observing that key factors for constructing an effective surrogate dataset are representativeness and diversity we design additional minimax criteria in the generative training to enhance these facets for the generated images of diffusion models. We present a theoretical model of the process as hierarchical diffusion control demonstrating the flexibility of the diffusion process to target these criteria without jeopardizing the faithfulness of the sample to the desired distribution. The proposed method achieves state-of-the-art validation performance while demanding much less computational resources. Under the 100-IPC setting on ImageWoof our method requires less than one-twentieth the distillation time of previous methods yet yields even better performance. Source code and generated data are available in https://github.com/vimar-gu/MinimaxDiffusion.

Generative models are gaining increasing popularity and the demand for precisely generating images is on the rise. However generating an image that perfectly aligns with users' expectations is extremely challenging. The shapes of objects the poses of animals the structures of landscapes and more may not match the user's desires and this applies to real images as well. This is where point-based image editing becomes essential. An excellent image editing method needs to meet the following criteria: user-friendly interaction high performance and good generalization capability. Due to the limitations of StyleGAN DragGAN exhibits limited robustness across diverse scenarios while DragDiffusion lacks user-friendliness due to the necessity of LoRA fine-tuning and masks. In this paper we introduce a novel interactive point-based image editing framework called EasyDrag that leverages pretrained diffusion models to achieve high-quality editing outcomes and user-friendship. Extensive experimentation demonstrates that our approach surpasses DragDiffusion in terms of both image quality and editing precision for point-based image manipulation tasks.

In the absence of parallax cues a learning-based single image depth estimation (SIDE) model relies heavily on shading and contextual cues in the image. While this simplicity is attractive it is necessary to train such models on large and varied datasets which are difficult to capture. It has been shown that using embeddings from pre-trained foundational models such as CLIP improves zero shot transfer in several applications. Taking inspiration from this in our paper we explore the use of global image priors generated from a pre-trained ViT model to provide more detailed contextual information. We argue that the embedding vector from a ViT model pre-trained on a large dataset captures greater relevant information for SIDE than the usual route of generating pseudo image captions followed by CLIP based text embeddings. Based on this idea we propose a new SIDE model using a diffusion backbone which is conditioned on ViT embeddings. Our proposed design establishes a new state-of-the-art (SOTA) for SIDE on NYUv2 dataset achieving Abs Rel error of 0.059(14% improvement) compared to 0.069 by the current SOTA (VPD). And on KITTI dataset achieving Sq Rel error of 0.139 (2% improvement) compared to 0.142 by the current SOTA (GEDepth). For zero-shot transfer with a model trained on NYUv2 we report mean relative improvement of (20% 23% 81% 25%) over NeWCRFs on (Sun-RGBD iBims1 DIODE HyperSim) datasets compared to (16% 18% 45% 9%) by ZoeDepth. The project page is available at https://ecodepth-iitd.github.io

Text-to-image (T2I) diffusion models notably the unCLIP models (e.g. DALL-E-2) achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks at the cost of significant computational resources. The unCLIP stack comprises T2I prior and diffusion image decoder. The T2I prior model alone adds a billion parameters compared to the Latent Diffusion Models which increases the computational and high-quality data requirements. We introduce ECLIPSE a novel contrastive learning method that is both parameter and data-efficient. ECLIPSE leverages pre-trained vision-language models (e.g. CLIP) to distill the knowledge into the prior model. We demonstrate that the ECLIPSE trained prior with only 3.3% of the parameters and trained on a mere 2.8% of the data surpasses the baseline T2I priors with an average of 71.6% preference score under resource-limited setting. It also attains performance on par with SOTA big models achieving an average of 63.36% preference score in terms of the ability to follow the text compositions. Extensive experiments on two unCLIP diffusion image decoders Karlo and Kandinsky affirm that ECLIPSE priors consistently deliver high performance while significantly reducing resource dependency. Project page: https://eclipse-t2i.vercel.app/