CVPR 2024 论文列表

This paper introduces the first text-guided work for generating the sequence of hand-object interaction in 3D. The main challenge arises from the lack of labeled data where existing ground-truth datasets are nowhere near generalizable in interaction type and object category which inhibits the modeling of diverse 3D hand-object interaction with the correct physical implication (e.g. contacts and semantics) from text prompts. To address this challenge we propose to decompose the interaction generation task into two subtasks: hand-object contact generation; and hand-object motion generation. For contact generation a VAE-based network takes as input a text and an object mesh and generates the probability of contacts between the surfaces of hands and the object during the interaction. The network learns a variety of local geometry structure of diverse objects that is independent of the objects' category and thus it is applicable to general objects. For motion generation a Transformer-based diffusion model utilizes this 3D contact map as a strong prior for generating physically plausible hand-object motion as a function of text prompts by learning from the augmented labeled dataset; where we annotate text labels from many existing 3D hand and object motion data. Finally we further introduce a hand refiner module that minimizes the distance between the object surface and hand joints to improve the temporal stability of the object-hand contacts and to suppress the penetration artifacts. In the experiments we demonstrate that our method can generate more realistic and diverse interactions compared to other baseline methods. We also show that our method is applicable to unseen objects. We will release our model and newly labeled data as a strong foundation for future research. Codes and data are available in: https://github.com/JunukCha/Text2HOI.

This paper for the first time explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR). We highlight a pivotal discovery: the capacity of text-to-image diffusion models to seamlessly bridge the gap between sketches and photos. This proficiency is underpinned by their robust cross-modal capabilities and shape bias findings that are substantiated through our pilot studies. In order to harness pre-trained diffusion models effectively we introduce a straightforward yet powerful strategy focused on two key aspects: selecting optimal feature layers and utilising visual and textual prompts. For the former we identify which layers are most enriched with information and are best suited for the specific retrieval requirements (category-level or fine-grained). Then we employ visual and textual prompts to guide the model's feature extraction process enabling it to generate more discriminative and contextually relevant cross-modal representations. Extensive experiments on several benchmark datasets validate significant performance improvements.

Automatic text-to-3D generation that combines Score Distillation Sampling (SDS) with the optimization of volume rendering has achieved remarkable progress in synthesizing realistic 3D objects. Yet most existing text-to-3D methods by SDS and volume rendering suffer from inaccurate geometry e.g. the Janus issue since it is hard to explicitly integrate 3D priors into implicit 3D representations. Besides it is usually time-consuming for them to generate elaborate 3D models with rich colors. In response this paper proposes GSGEN a novel method that adopts Gaussian Splatting a recent state-of-the-art representation to text-to-3D generation. GSGEN aims at generating high-quality 3D objects and addressing existing shortcomings by exploiting the explicit nature of Gaussian Splatting that enables the incorporation of 3D prior. Specifically our method adopts a progressive optimization strategy which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization a coarse representation is established under 3D point cloud diffusion prior along with the ordinary 2D SDS optimization ensuring a sensible and 3D-consistent rough shape. Subsequently the obtained Gaussians undergo an iterative appearance refinement to enrich texture details. In this stage we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs our approach can generate 3D assets with delicate details and accurate geometry. Extensive evaluations demonstrate the effectiveness of our method especially for capturing high-frequency components.

Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-view datasets. Without explicit 3D priors these methods often lead to geometric anomalies and multi-view inconsistency. Recently researchers have attempted to improve the genuineness of 3D objects by directly training on 3D datasets albeit at the cost of low-quality texture generation due to the limited texture diversity in 3D datasets. To harness the advantages of both approaches we propose Bidirectional Diffusion (BiDiff) a unified framework that incorporates both a 3D and a 2D diffusion process to preserve both 3D fidelity and 2D texture richness respectively. Moreover as a simple combination may yield inconsistent generation results we further bridge them with novel bidirectional guidance. In addition our method can be used as an initialization of optimization-based models to further improve the quality of 3D model and efficiency of optimization reducing the process from 3.4 hours to 20 minutes. Experimental results have shown that our model achieves high-quality diverse and scalable 3D generation. Project website https://bidiff.github.io/.

In this paper we introduce the problem of zero-shot text-guided exploration of the solutions to open-domain image super-resolution. Our goal is to allow users to explore diverse semantically accurate reconstructions that preserve data consistency with the low-resolution inputs for different large downsampling factors without explicitly training for these specific degradations. We propose two approaches for zero-shot text-guided super-resolution - i) modifying the generative process of text-to-image (T2I) diffusion models to promote consistency with low-resolution inputs and ii) incorporating language guidance into zero-shot diffusion-based restoration methods. We show that the proposed approaches result in diverse solutions that match the semantic meaning provided by the text prompt while preserving data consistency with the degraded inputs. We evaluate the proposed baselines for the task of extreme super-resolution and demonstrate advantages in terms of restoration quality diversity and explorability of solutions.

With the advent of generative models and vision language pretraining significant improvement has been made in text-driven face manipulation. The text embedding can be used as target supervision for expression control.However it is non-trivial to associate with its 3D attributesi.e. pose and illumination. To address these issues we propose a Text-conditional Attribute aLignment approach for 3D controllable face image synthesis and our model is referred to as TcALign. Specifically since the 3D rendered image can be precisely controlled with the 3D face representation we first propose a Text-conditional 3D Editor to produce the target face representation to realize text-driven manipulation in the 3D space. An attribute embedding space spanned by the target-related attributes embeddings is also introduced to infer the disentangled task-specific direction. Next we train a cross-modal latent mapping network conditioned on the derived difference of 3D representation to infer a correct vector in the latent space of StyleGAN.Thiscorrection vector learning design can accurately transfer the attribute manipulation on 3D images to 2D images. We show that the proposed method delivers more precise text-driven multi-attribute manipulation for 3D controllable face image synthesis. Extensive qualitative and quantitative experiments verify the effectiveness and superiority of our method over the other competing methods.

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

Image fusion aims to combine information from different source images to create a comprehensively representative image. Existing fusion methods are typically helpless in dealing with degradations in low-quality source images and non-interactive to multiple subjective and objective needs. To solve them we introduce a novel approach that leverages semantic text guidance image fusion model for degradation-aware and interactive image fusion task termed as Text-IF. It innovatively extends the classical image fusion to the text guided image fusion along with the ability to harmoniously address the degradation and interaction issues during fusion. Through the text semantic encoder and semantic interaction fusion decoder Text-IF is accessible to the all-in-one infrared and visible image degradation-aware processing and the interactive flexible fusion outcomes. In this way Text-IF achieves not only multi-modal image fusion but also multi-modal information fusion. Extensive experiments prove that our proposed text guided image fusion strategy has obvious advantages over SOTA methods in the image fusion performance and degradation treatment. The code is available at https://github.com/XunpengYi/Text-IF.

We propose a text-guided variational image generation method to address the challenge of getting clean data for anomaly detection in industrial manufacturing. Our method utilizes text information about the target object learned from extensive text library documents to generate non-defective data images resembling the input image. The proposed framework ensures that the generated non-defective images align with anticipated distributions derived from textual and image-based knowledge ensuring stability and generality. Experimental results demonstrate the effectiveness of our approach surpassing previous methods even with limited non-defective data. Our approach is validated through generalization tests across four baseline models and three distinct datasets. We present an additional analysis to enhance the effectiveness of anomaly detection models by utilizing the generated images.

Text-guided 3D face synthesis has achieved remarkable results by leveraging text-to-image (T2I) diffusion models. However most existing works focus solely on the direct generation ignoring the editing restricting them from synthesizing customized 3D faces through iterative adjustments. In this paper we propose a unified text-guided framework from face generation to editing. In the generation stage we propose a geometry-texture decoupled generation to mitigate the loss of geometric details caused by coupling. Besides decoupling enables us to utilize the generated geometry as a condition for texture generation yielding highly geometry-texture aligned results. We further employ a fine-tuned texture diffusion model to enhance texture quality in both RGB and YUV space. In the editing stage we first employ a pre-trained diffusion model to update facial geometry or texture based on the texts. To enable sequential editing we introduce a UV domain consistency preservation regularization preventing unintentional changes to irrelevant facial attributes. Besides we propose a self-guided consistency weight strategy to improve editing efficacy while preserving consistency. Through comprehensive experiments we showcase our method's superiority in face synthesis.

Federated Class-Incremental Learning (FCIL) is an underexplored yet pivotal issue involving the dynamic addition of new classes in the context of federated learning. In this field Data-Free Knowledge Transfer (DFKT) plays a crucial role in addressing catastrophic forgetting and data privacy problems. However prior approaches lack the crucial synergy between DFKT and the model training phases causing DFKT to encounter difficulties in generating high-quality data from a non-anchored latent space of the old task model. In this paper we introduce LANDER (Label Text Centered Data-Free Knowledge Transfer) to address this issue by utilizing label text embeddings (LTE) produced by pretrained language models. Specifically during the model training phase our approach treats LTE as anchor points and constrains the feature embeddings of corresponding training samples around them enriching the surrounding area with more meaningful information. In the DFKT phase by using these LTE anchors LANDER can synthesize more meaningful samples thereby effectively addressing the forgetting problem. Additionally instead of tightly constraining embeddings toward the anchor the Bounding Loss is introduced to encourage sample embeddings to remain flexible within a defined radius. This approach preserves the natural differences in sample embeddings and mitigates the embedding overlap caused by heterogeneous federated settings. Extensive experiments conducted on CIFAR100 Tiny-ImageNet and ImageNet demonstrate that LANDER significantly outperforms previous methods and achieves state-of-the-art performance in FCIL. The code is available at https://github.com/tmtuan1307/lander.

Language has emerged as a natural interface for image editing. In this paper we introduce a method for region-based image editing driven by textual prompts without the need for user-provided masks or sketches. Specifically our approach leverages an existing pre-trained text-to-image model and introduces a bounding box generator to identify the editing regions that are aligned with the textual prompts. We show that this simple approach enables flexible editing that is compatible with current image generation models and is able to handle complex prompts featuring multiple objects complex sentences or lengthy paragraphs. We conduct an extensive user study to compare our method against state-of-the-art methods. The experiments demonstrate the competitive performance of our method in manipulating images with high fidelity and realism that correspond to the provided language descriptions. Our project webpage can be found at: https://yuanzelin.me/LearnableRegions_page.

We present HAAR a new strand-based generative model for 3D human hairstyles. Specifically based on textual inputs HAAR produces 3D hairstyles that could be used as production-level assets in modern computer graphics engines. Current AI-based generative models take advantage of powerful 2D priors to reconstruct 3D content in the form of point clouds meshes or volumetric functions. However by using the 2D priors they are intrinsically limited to only recovering the visual parts. Highly occluded hair structures can not be reconstructed with those methods and they only model the "outer shell" which is not ready to be used in physics-based rendering or simulation pipelines. In contrast we propose a first text-guided generative method that uses 3D hair strands as an underlying representation. Leveraging 2D visual question-answering (VQA) systems we automatically annotate synthetic hair models that are generated from a small set of artist-created hairstyles. This allows us to train a latent diffusion model that operates in a common hairstyle UV space. In qualitative and quantitative studies we demonstrate the capabilities of the proposed model and compare it to existing hairstyle generation approaches. For results please refer to our project page https://haar.is.tue.mpg.de/.

Weakly supervised video anomaly detection (WSVAD) is a challenging task. Generating fine-grained pseudo-labels based on weak-label and then self-training a classifier is currently a promising solution. However since the existing methods use only RGB visual modality and the utilization of category text information is neglected thus limiting the generation of more accurate pseudo-labels and affecting the performance of self-training. Inspired by the manual labeling process based on the event description in this paper we propose a novel pseudo-label generation and self-training framework based on Text Prompt with Normality Guidance (TPWNG) for WSVAD. Our idea is to transfer the rich language-visual knowledge of the contrastive language-image pre-training (CLIP) model for aligning the video event description text and corresponding video frames to generate pseudo-labels. Specifically We first fine-tune the CLIP for domain adaptation by designing two ranking losses and a distributional inconsistency loss. Further we propose a learnable text prompt mechanism with the assist of a normality visual prompt to further improve the matching accuracy of video event description text and video frames. Then we design a pseudo-label generation module based on the normality guidance to infer reliable frame-level pseudo-labels. Finally we introduce a temporal context self-adaptive learning module to learn the temporal dependencies of different video events more flexibly and accurately. Extensive experiments show that our method achieves state-of-the-art performance on two benchmark datasets UCF-Crime and XD-Violence demonstrating the effectiveness of our proposed method.

Significant progress has been made in scene text detection models since the rise of deep learning but scene text layout analysis which aims to group detected text instances as paragraphs has not kept pace. Previous works either treated text detection and grouping using separate models or train a model from scratch while using a unified one. All of them have not yet made full use of the already well-trained text detectors and easily obtainable detection datasets. In this paper we present Text Grouping Adapter (TGA) a module that can enable the utilization of various pre-trained text detectors to learn layout analysis allowing us to adopt a well-trained text detector right off the shelf or just fine-tune it efficiently. Designed to be compatible with various text detector architectures TGA takes detected text regions and image features as universal inputs to assemble text instance features. To capture broader contextual information for layout analysis we propose to predict text group masks from text instance features by one-to-many assignment. Our comprehensive experiments demonstrate that even with frozen pre-trained models incorporating our TGA into various pre-trained text detectors and text spotters can achieve superior layout analysis performance simultaneously inheriting generalized text detection ability from pre-training. In the case of full parameter fine-tuning we can further improve layout analysis performance.

To adequately utilize the available image evidence in multi-view video-based avatar modeling we propose TexVocab a novel avatar representation that constructs a texture vocabulary and associates body poses with texture maps for animation. Given multi-view RGB videos our method initially back-projects all the available images in the training videos to the posed SMPL surface producing texture maps in the SMPL UV domain. Then we construct pairs of human poses and texture maps to establish a texture vocabulary for encoding dynamic human appearances under various poses. Unlike the commonly used joint-wise manner we further design a body-part-wise encoding strategy to learn the structural effects of the kinematic chain. Given a driving pose we query the pose feature hierarchically by decomposing the pose vector into several body parts and interpolating the texture features for synthesizing fine-grained human dynamics. Overall our method is able to create animatable human avatars with detailed and dynamic appearances from RGB videos and the experiments show that our method outperforms state-of-the-art approaches.

We introduce TexTile a novel differentiable metric to quantify the degree upon which a texture image can be concatenated with itself without introducing repeating artifacts (i.e. the tileability). Existing methods for tileable texture synthesis focus on general texture quality but lack explicit analysis of the intrinsic repeatability properties of a texture. In contrast our TexTile metric effectively evaluates the tileable properties of a texture opening the door to more informed synthesis and analysis of tileable textures. Under the hood TexTile is formulated as a binary classifier carefully built from a large dataset of textures of different styles semantics regularities and human annotations.Key to our method is a set of architectural modifications to baseline pre-train image classifiers to overcome their shortcomings at measuring tileability along with a custom data augmentation and training regime aimed at increasing robustness and accuracy. We demonstrate that TexTile can be plugged into different state-of-the-art texture synthesis methods including diffusion-based strategies and generate tileable textures while keeping or even improving the overall texture quality. Furthermore we show that TexTile can objectively evaluate any tileable texture synthesis method whereas the current mix of existing metrics produces uncorrelated scores which heavily hinders progress in the field.

This paper focuses on synthesizing high-quality and complete textures directly on the surface of 3D models within 3D space. 2D diffusion-based methods face challenges in generating 2D texture maps due to the infinite possibilities of UV mapping for a given 3D mesh. Utilizing point clouds helps circumvent variations arising from diverse mesh topologies and UV mappings. Nevertheless achieving dense point clouds to accurately represent texture details poses a challenge due to limited computational resources. To address these challenges we propose an efficient octree-based diffusion pipeline called TexOct. Our method starts by sampling a point cloud from the surface of a given 3D model with each point containing texture noise values. We utilize an octree structure to efficiently represent this point cloud. Additionally we introduce an innovative octree-based diffusion model that leverages the denoising capabilities of the Denoising Diffusion Probabilistic Model (DDPM). This model gradually reduces the texture noise on the octree nodes resulting in the restoration of fine texture. Experimental results on ShapeNet demonstrate that TexOct effectively generates high-quality 3D textures in both unconditional and text / image-conditional scenarios.

In many practical applications 3D point cloud analysis requires rotation invariance. In this paper we present a learnable descriptor invariant under 3D rotations and reflections i.e. the O(3) actions utilizing the recently introduced steerable 3D spherical neurons and vector neurons. Specifically we propose an embedding of the 3D spherical neurons into 4D vector neurons which leverages end-to-end training of the model. In our approach we perform TetraTransform---an equivariant embedding of the 3D input into 4D constructed from the steerable neurons---and extract deeper O(3)-equivariant features using vector neurons. This integration of the TetraTransform into the VN-DGCNN framework termed TetraSphere negligibly increases the number of parameters by less than 0.0002%. TetraSphere sets a new state-of-the-art performance classifying randomly rotated real-world object scans of the challenging subsets of ScanObjectNN. Additionally TetraSphere outperforms all equivariant methods on randomly rotated synthetic data: classifying objects from ModelNet40 and segmenting parts of the ShapeNet shapes. Thus our results reveal the practical value of steerable 3D spherical neurons for learning in 3D Euclidean space. The code is available at https://github.com/pavlo-melnyk/tetrasphere.