CVPR 2024 论文列表

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.

This paper addresses the challenge of example-based non-stationary texture synthesis. We introduce a novel two-step approach wherein users first modify a reference texture using standard image editing tools yielding an initial rough target for the synthesis. Subsequently our proposed method termed "self-rectification" automatically refines this target into a coherent seamless texture while faithfully preserving the distinct visual characteristics of the reference exemplar. Our method leverages a pre-trained diffusion network and uses self-attention mechanisms to gradually align the synthesized texture with the reference ensuring the retention of the structures in the provided target. Through experimental validation our approach exhibits exceptional proficiency in handling non-stationary textures demonstrating significant advancements in texture synthesis when compared to existing state-of-the-art techniques. Code is available at https://github.com/xiaorongjun000/Self-Rectification

We introduce a new task of generating "Illustrated Instructions" i.e. visual instructions customized to a user's needs. We identify desiderata unique to this task and formalize it through a suite of automatic and human evaluation metrics designed to measure the validity consistency and efficacy of the generations. We combine the power of large language models (LLMs) together with strong text-to-image generation diffusion models to propose a simple approach called StackedDiffusion which generates such illustrated instructions given text as input. The resulting model strongly outperforms baseline approaches and state-of-the-art multimodal LLMs; and in 30% of cases users even prefer it to human-generated articles. Most notably it enables various new and exciting applications far beyond what static articles on the web can provide such as personalized instructions complete with intermediate steps and pictures in response to a user's individual situation.

Few-shot font generation (FFG) produces stylized font images with a limited number of reference samples which can significantly reduce labor costs in manual font designs. Most existing FFG methods follow the style-content disentanglement paradigm and employ the Generative Adversarial Network (GAN) to generate target fonts by combining the decoupled content and style representations. The complicated structure and detailed style are simultaneously generated in those methods which may be the sub-optimal solutions for FFG task. Inspired by most manual font design processes of expert designers in this paper we model font generation as a multi-stage generative process. Specifically as the injected noise and the data distribution in diffusion models can be well-separated into different sub-spaces we are able to incorporate the font transfer process into these models. Based on this observation we generalize diffusion methods to model font generative process by separating the reverse diffusion process into three stages with different functions: The structure construction stage first generates the structure information for the target character based on the source image and the font transfer stage subsequently transforms the source font to the target font. Finally the font refinement stage enhances the appearances and local details of the target font images. Based on the above multi-stage generative process we construct our font generation framework named MSD-Font with a dual-network approach to generate font images. The superior performance demonstrates the effectiveness of our model. The code is available at: https://github.com/fubinfb/MSD-Font .

As a significant step for human face modeling editing and generation face landmarking aims at extracting facial keypoints from images. A generalizable face landmarker is required in practice because real-world facial images e.g. the avatars in animations and games are often stylized in various ways. However achieving generalizable face landmarking is challenging due to the diversity of facial styles and the scarcity of labeled stylized faces. In this study we propose a simple but effective paradigm to learn a generalizable face landmarker based on labeled real human faces and unlabeled stylized faces. Our method learns the face landmarker as the key module of a conditional face warper. Given a pair of real and stylized facial images the conditional face warper predicts a warping field from the real face to the stylized one in which the face landmarker predicts the ending points of the warping field and provides us with high-quality pseudo landmarks for the corresponding stylized facial images. Applying an alternating optimization strategy we learn the face landmarker to minimize i) the discrepancy between the stylized faces and the warped real ones and ii) the prediction errors of both real and pseudo landmarks. Experiments on various datasets show that our method outperforms existing state-of-the-art domain adaptation methods in face landmarking tasks leading to a face landmarker with better generalizability. Code is available at https://plustwo0.github.io/project-face-landmarker.

Efficient generation of 3D digital humans is important in several industries including virtual reality social media and cinematic production. 3D generative adversarial networks (GANs) have demonstrated state-of-the-art (SOTA) quality and diversity for generated assets. Current 3D GAN architectures however typically rely on volume representations which are slow to render thereby hampering the GAN training and requiring multi-view-inconsistent 2D upsamplers. Here we introduce Gaussian Shell Maps (GSMs) as a framework that connects SOTA generator network architectures with emerging 3D Gaussian rendering primitives using an articulable multi shell--based scaffold. In this setting a CNN generates a 3D texture stack with features that are mapped to the shells. The latter represent inflated and deflated versions of a template surface of a digital human in a canonical body pose. Instead of rasterizing the shells directly we sample 3D Gaussians on the shells whose attributes are encoded in the texture features. These Gaussians are efficiently and differentiably rendered. The ability to articulate the shells is important during GAN training and at inference time to deform a body into arbitrary user-defined poses. Our efficient rendering scheme bypasses the need for view-inconsistent upsamplers and achieves high-quality multi-view consistent renderings at a native resolution of 512 x512 pixels. We demonstrate that GSMs successfully generate 3D humans when trained on single-view datasets including SHHQ and DeepFashion.

Scene coordinate regression (SCR) methods are a family of visual localization methods that directly regress 2D-3D matches for camera pose estimation. They are effective in small-scale scenes but face significant challenges in large-scale scenes that are further amplified in the absence of ground truth 3D point clouds for supervision. Here the model can only rely on reprojection constraints and needs to implicitly triangulate the points. The challenges stem from a fundamental dilemma: The network has to be invariant to observations of the same landmark at different viewpoints and lighting conditions etc. but at the same time discriminate unrelated but similar observations. The latter becomes more relevant and severe in larger scenes. In this work we tackle this problem by introducing the concept of co-visibility to the network. We propose GLACE which integrates pre-trained global and local encodings and enables SCR to scale to large scenes with only a single small-sized network. Specifically we propose a novel feature diffusion technique that implicitly groups the reprojection constraints with co-visibility and avoids overfitting to trivial solutions. Additionally our position decoder parameterizes the output positions for large-scale scenes more effectively. Without using 3D models or depth maps for supervision our method achieves state-of-the-art results on large-scale scenes with a low-map-size model. On Cambridge landmarks with a single model we achieve a 17% lower median position error than Poker the ensemble variant of the state-of-the-art SCR method ACE. Code is available at: https://github.com/cvg/glace.

Estimating the relative camera pose from n \geq 5 correspondences between two calibrated views is a fundamental task in computer vision. This process typically involves two stages: 1) estimating the essential matrix between the views and 2) disambiguating among the four candidate relative poses that satisfy the epipolar geometry. In this paper we demonstrate a novel approach that for the first time bypasses the second stage. Specifically we show that it is possible to directly estimate the correct relative camera pose from correspondences without needing a post-processing step to enforce the cheirality constraint on the correspondences. Building on recent advances in certifiable non-minimal optimization we frame the relative pose estimation as a Quadratically Constrained Quadratic Program (QCQP). By applying the appropriate constraints we ensure the estimation of a camera pose that corresponds to a valid 3D geometry and that is globally optimal when certified. We validate our method through exhaustive synthetic and real-world experiments confirming the efficacy efficiency and accuracy of the proposed approach. Code is available at https://github.com/javrtg/C2P.

In this paper we uncover the untapped potential of diffusion U-Net which serves as a "free lunch" that substantially improves the generation quality on the fly. We initially investigate the key contributions of the U-Net architecture to the denoising process and identify that its main backbone primarily contributes to denoising whereas its skip connections mainly introduce high-frequency features into the decoder module causing the potential neglect of crucial functions intrinsic to the backbone network. Capitalizing on this discovery we propose a simple yet effective method termed "FreeU" which enhances generation quality without additional training or finetuning. Our key insight is to strategically re-weight the contributions sourced from the U-Net's skip connections and backbone feature maps to leverage the strengths of both components of the U-Net architecture. Promising results on image and video generation tasks demonstrate that our FreeU can be readily integrated to existing diffusion models e.g. Stable Diffusion DreamBooth and ControlNet to improve the generation quality with only a few lines of code. All you need is to adjust two scaling factors during inference.

Benefiting from large-scale pre-trained text-to-image (T2I) generative models impressive progress has been achieved in customized image generation which aims to generate user-specified concepts. Existing approaches have extensively focused on single-concept customization and still encounter challenges when it comes to complex scenarios that involve combining multiple concepts. These approaches often require retraining/fine-tuning using a few images leading to time-consuming training processes and impeding their swift implementation. Furthermore the reliance on multiple images to represent a singular concept increases the difficulty of customization. To this end we propose FreeCustom a novel tuning-free method to generate customized images of multi-concept composition based on reference concepts using only one image per concept as input. Specifically we introduce a new multi-reference self-attention (MRSA) mechanism and a weighted mask strategy that enables the generated image to access and focus more on the reference concepts. In addition MRSA leverages our key finding that input concepts are better preserved when providing images with context interactions. Experiments show that our method's produced images are consistent with the given concepts and better aligned with the input text. Our method outperforms or performs on par with other training-based methods in terms of multi-concept composition and single-concept customization but is simpler. Codes can be found \href https://github.com/aim-uofa/FreeCustom here .

Generating emotional talking faces is a practical yet challenging endeavor. To create a lifelike avatar we draw upon two critical insights from a human perspective: 1) The connection between audio and the non-deterministic facial dynamics encompassing expressions blinks poses should exhibit synchronous and one-to-many mapping. 2) Vibrant expressions are often accompanied by emotion-aware high-definition (HD) textures and finely detailed teeth. However both aspects are frequently overlooked by existing methods. To this end this paper proposes using normalizing Flow and Vector-Quantization modeling to produce emotional talking faces that satisfy both insights concurrently (FlowVQTalker). Specifically we develop a flowbased coefficient generator that encodes the dynamics of facial emotion into a multi-emotion-class latent space represented as a mixture distribution. The generation process commences with random sampling from the modeled distribution guided by the accompanying audio enabling both lip-synchronization and the uncertain nonverbal facial cues generation. Furthermore our designed vector-quantization image generator treats the creation of expressive facial images as a code query task utilizing a learned codebook to provide rich high-quality textures that enhance the emotional perception of the results. Extensive experiments are conducted to showcase the effectiveness of our approach.

Optical flow estimation a process of predicting pixel-wise displacement between consecutive frames has commonly been approached as a regression task in the age of deep learning. Despite notable advancements this de facto paradigm unfortunately falls short in generalization performance when trained on synthetic or constrained data. Pioneering a paradigm shift we reformulate optical flow estimation as a conditional flow generation challenge unveiling FlowDiffuser --- a new family of optical flow models that could have stronger learning and generalization capabilities. FlowDiffuser estimates optical flow through a `noise-to-flow' strategy progressively eliminating noise from randomly generated flows conditioned on the provided pairs. To optimize accuracy and efficiency our FlowDiffuser incorporates a novel Conditional Recurrent Denoising Decoder (Conditional-RDD) streamlining the flow estimation process. It incorporates a unique Hidden State Denoising (HSD) paradigm effectively leveraging the information from previous time steps. Moreover FlowDiffuser can be easily integrated into existing flow networks leading to significant improvements in performance metrics compared to conventional implementations. Experiments on challenging benchmarks including Sintel and KITTI demonstrate the effectiveness of our FlowDiffuser with superior performance to existing state-of-the-art models. Code is available at https://github.com/LA30/FlowDiffuser.

Recently subject-driven generation has garnered significant interest due to its ability to personalize text-to-image generation. Typical works focus on learning the new subject's private attributes. However an important fact has not been taken seriously that a subject is not an isolated new concept but should be a specialization of a certain category in the pre-trained model. This results in the subject failing to comprehensively inherit the attributes in its category causing poor attribute-related generations. In this paper motivated by object-oriented programming we model the subject as a derived class whose base class is its semantic category. This modeling enables the subject to inherit public attributes from its category while learning its private attributes from the user-provided example. Specifically we propose a plug-and-play method Subject-Derived regularization (SuDe). It constructs the base-derived class modeling by constraining the subject-driven generated images to semantically belong to the subject's category. Extensive experiments under three baselines and two backbones on various subjects show that our SuDe enables imaginative attribute-related generations while maintaining subject fidelity. For the codes please refer to \href https://github.com/modelscope/facechain FaceChain .

Instruction-based image editing holds immense potential for a variety of applications as it enables users to perform any editing operation using a natural language instruction. However current models in this domain often struggle with accurately executing user instructions. We present Emu Edit a multi-task image editing model which sets state-of-the-art results in instruction-based image editing. To develop Emu Edit we train it to multi-task across an unprecedented range of tasks such as region-based editing free-form editing and Computer Vision tasks all of which are formulated as generative tasks. Additionally to enhance Emu Edit's multi-task learning abilities we provide it with learned task embeddings which guide the generation process towards the correct edit type. Both these elements are essential for Emu Edit's outstanding performance. Furthermore we show that Emu Edit can generalize to new tasks such as image inpainting super-resolution and compositions of editing tasks with just a few labeled examples. This capability offers a significant advantage in scenarios where high-quality samples are scarce. Lastly to facilitate a more rigorous and informed assessment of instructable image editing models we release a new challenging and versatile benchmark that includes seven different image editing tasks.

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

Scene Graph Generation (SGG) is a challenging task of detecting objects and predicting relationships between objects. After DETR was developed one-stage SGG models based on a one-stage object detector have been actively studied. However complex modeling is used to predict the relationship between objects and the inherent relationship between object queries learned in the multi-head self-attention of the object detector has been neglected. We propose a lightweight one-stage SGG model that extracts the relation graph from the various relationships learned in the multi-head self-attention layers of the DETR decoder. By fully utilizing the self-attention by-products the relation graph can be extracted effectively with a shallow relation extraction head. Considering the dependency of the relation extraction task on the object detection task we propose a novel relation smoothing technique that adjusts the relation label adaptively according to the quality of the detected objects. By the relation smoothing the model is trained according to the continuous curriculum that focuses on object detection task at the beginning of training and performs multi-task learning as the object detection performance gradually improves. Furthermore we propose a connectivity prediction task that predicts whether a relation exists between object pairs as an auxiliary task of the relation extraction. We demonstrate the effectiveness and efficiency of our method for the Visual Genome and Open Image V6 datasets. Our code is publicly available at https://github.com/naver-ai/egtr.

While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centered images novel challenges arise with a nuanced task of "identity fine editing" - precisely modifying specific features of a subject while maintaining its inherent identity and context. Existing personalization methods either require time-consuming optimization or learning additional encoders adept in "identity re-contextualization". However they often struggle with detailed and sensitive tasks like human face editing. To address these challenges we introduce DreamSalon a noise-guided staged-editing framework uniquely focusing on detailed image manipulations and identity-context preservation. By discerning editing and boosting stages via the frequency and gradient of predicted noises DreamSalon first performs detailed manipulations on specific features in the editing stage guided by high-frequency information and then employs stochastic denoising in the boosting stage to improve image quality. For more precise editing DreamSalon semantically mixes source and target textual prompts guided by differences in their embedding covariances to direct the model's focus on specific manipulation areas. Our experiments demonstrate DreamSalon's ability to efficiently and faithfully edit fine details on human faces outperforming existing methods both qualitatively and quantitatively.

Utilizing pre-trained 2D large-scale generative models recent works are capable of generating high-quality novel views from a single in-the-wild image. However due to the lack of information from multiple views these works encounter difficulties in generating controllable novel views. In this paper we present DreamComposer a flexible and scalable framework that can enhance existing view-aware diffusion models by injecting multi-view conditions. Specifically DreamComposer first uses a view-aware 3D lifting module to obtain 3D representations of an object from multiple views. Then it renders the latent features of the target view from 3D representations with the multi-view feature fusion module. Finally the target view features extracted from multi-view inputs are injected into a pre-trained diffusion model. Experiments show that DreamComposer is compatible with state-of-the-art diffusion models for zero-shot novel view synthesis further enhancing them to generate high-fidelity novel view images with multi-view conditions ready for controllable 3D object reconstruction and various other applications.

Despite advancements in text-to-image generation (T2I) prior methods often face text-image misalignment problems such as relation confusion in generated images. Existing solutions involve cross-attention manipulation for better compositional understanding or integrating large language models for improved layout planning. However the inherent alignment capabilities of T2I models are still inadequate. By reviewing the link between generative and discriminative modeling we posit that T2I models' discriminative abilities may reflect their text-image alignment proficiency during generation. In this light we advocate bolstering the discriminative abilities of T2I models to achieve more precise text-to-image alignment for generation. We present a discriminative adapter built on T2I models to probe their discriminative abilities on two representative tasks and leverage discriminative fine-tuning to improve their text-image alignment. As a bonus of the discriminative adapter a self-correction mechanism can leverage discriminative gradients to better align generated images to text prompts during inference. Comprehensive evaluations across three benchmark datasets including both in-distribution and out-of-distribution scenarios demonstrate our method's superior generation performance. Meanwhile it achieves state-of-the-art discriminative performance on the two discriminative tasks compared to other generative models. The code is available at https://dpt-t2i.github.io/.