3D human avatar animation aims at transforming a human avatar from an arbitrary initial pose to a specified target pose using deformation algorithms. Existing approaches typically divide this task into two stages: canonical template construction and target pose deformation. However, current template construction methods demand extensive skeletal rigging and often produce artifacts in contact regions. Moreover, target pose deformation suffers from structural distortions caused by Linear Blend Skinning (LBS), which significantly undermines animation realism. To address these problems, we propose a unified learning-based framework to address both challenges in two phases. For the former phase, to overcome the inefficiencies and artifacts during template construction, we leverage a U-Net architecture that decouples texture and pose information in a feed-forward process, enabling fast generation of a human template. For the latter phase, we propose a data-driven refinement technique that enhances structural integrity. Extensive experiments show that our model delivers consistent performance across diverse poses with an optimal balance between efficiency and quality, surpassing state-of-the-art (SOTA) methods.

Monocular 3D clothed human reconstruction aims to generate a complete and realistic textured 3D avatar from a single image. Existing methods are commonly trained under multi-view supervision with annotated geometric priors, and during inference, these priors are estimated by the pre-trained network from the monocular input. These methods are constrained by three key limitations: texturally by the unavailability of training data, geometrically by inaccurate external priors, and systematically by biased single-modality supervision, all leading to suboptimal reconstruction. To address these issues, we propose a novel reconstruction framework, named MultiGO++, which achieves effective systematic geometry-texture collaboration. It consists of three core parts: (1) a multi-source texture synthesis strategy that constructs more than 15,000 3D textured human scans to improve the performance of texture quality estimation in challenging scenarios; (2) a region-aware shape extraction module that extracts features and enables feature interactions from each body region to obtain geometry information and a Fourier geometry encoder that mitigates the modality gap to achieve effective geometry learning; (3) a dual reconstruction U-Net that leverages geometry-texture collaborative features to refine and generate high-fidelity textured 3D human meshes. Extensive experiments on two benchmarks and numerous in-the-wild cases show the superiority of our method over state-of-the-art approaches.

Recent advances in vision-language pre-training have significantly enhanced the model capabilities on grounded object detection. However, these studies often pre-train with coarse-grained text prompts, such as plain category names and brief grounded phrases. This limitation curtails the model's capacity for fine-grained linguistic comprehension and leads to a significant decline in performance when faced with detailed descriptions or contextual information. To tackle these problems, we develop DoGA: Detect objects with Grouped Attributes, which employs commonly apparent attributes to bridge different granular semantics and uses specific attributes to identify the object discrepancy. Our DoGA incorporates three principle components: 1) Generation of attribute-based prompts, consisting of linguistic definitions enriched with common-sense visible attributes and hard negative notations deriving from the image-specific attribute features; 2) Paralleled entity fusion and optimization, designed to manage long attribute-based descriptions and negative concepts efficiently; and 3) Prompt-wise grouped training to accommodate model to perform many-to-many assignments, facilitating simultaneous training and inferring with multiple attribute-based synonyms. Extensive experiments demonstrate that training with synonymous attribute-based prompts allows DoGA to generalize multi-granular prompts and surpass previous state-of-the-art approaches, yielding 50.2 on the COCO and 38.0 on the LVIS benchmarks under the zero-short setting. We will make our code publicly available upon acceptance.

This paper investigates the research task of reconstructing the 3D clothed human body from a monocular image. Due to the inherent ambiguity of single-view input, existing approaches leverage pre-trained SMPL(-X) estimation models or generative models to provide auxiliary information for human reconstruction. However, these methods capture only the general human body geometry and overlook specific geometric details, leading to inaccurate skeleton reconstruction, incorrect joint positions, and unclear cloth wrinkles. In response to these issues, we propose a multi-level geometry learning framework. Technically, we design three key components: skeleton-level enhancement, joint-level augmentation, and wrinkle-level refinement modules. Specifically, we effectively integrate the projected 3D Fourier features into a Gaussian reconstruction model, introduce perturbations to improve joint depth estimation during training, and refine the human coarse wrinkles by resembling the de-noising process of the diffusion model. Extensive quantitative and qualitative experiments on two test sets show the superior performance of our approach compared to state-of-the-art (SOTA) methods.

Temporal sentence grounding in videos (TSGV) faces challenges due to public TSGV datasets containing significant temporal biases, which are attributed to the uneven temporal distributions of target moments. Existing methods generate augmented videos, where target moments are forced to have varying temporal locations. However, since the video lengths of the given datasets have small variations, only changing the temporal locations results in poor generalization ability in videos with varying lengths. In this paper, we propose a novel training framework complemented by diversified data augmentation and a domain discriminator. The data augmentation generates videos with various lengths and target moment locations to diversify temporal distributions. However, augmented videos inevitably exhibit distinct feature distributions which may introduce noise. To address this, we design a domain adaptation auxiliary task to diminish feature discrepancies between original and augmented videos. We also encourage the model to produce distinct predictions for videos with the same text queries but different moment locations to promote debiased training. Experiments on Charades-CD and ActivityNet-CD datasets demonstrate the effectiveness and generalization abilities of our method in multiple grounding structures, achieving state-of-the-art results.

This paper investigates an open research challenge of reconstructing high-quality, large-scale 3D open scenes from images. It is observed existing methods have various limitations, such as requiring precise camera poses for input and dense viewpoints for supervision. To perform effective and efficient 3D scene reconstruction, we propose a novel graph-guided 3D scene reconstruction framework, GraphGS. Specifically, given a set of images captured by RGB cameras on a scene, we first design a spatial prior-based scene structure estimation method. This is then used to create a camera graph that includes information about the camera topology. Further, we propose to apply the graph-guided multi-view consistency constraint and adaptive sampling strategy to the 3D Gaussian Splatting optimization process. This greatly alleviates the issue of Gaussian points overfitting to specific sparse viewpoints and expedites the 3D reconstruction process. We demonstrate GraphGS achieves high-fidelity 3D reconstruction from images, which presents state-of-the-art performance through quantitative and qualitative evaluation across multiple datasets.

Single-view textured human reconstruction aims to reconstruct a clothed 3D digital human by inputting a monocular 2D image. Existing approaches include feed-forward methods, limited by scarce 3D human data, and diffusion-based methods, prone to erroneous 2D hallucinations. To address these issues, we propose a novel SMPL normal map Equipped 3D Human Reconstruction (SEHR) framework, integrating a pretrained large 3D reconstruction model with human geometry prior. SEHR performs single-view human reconstruction without using a preset diffusion model in one forward propagation. Concretely, SEHR consists of two key components: SMPL Normal Map Guidance (SNMG) and SMPL Normal Map Constraint (SNMC). SNMG incorporates SMPL normal maps into an auxiliary network to provide improved body shape guidance. SNMC enhances invisible body parts by constraining the model to predict an extra SMPL normal Gaussians. Extensive experiments on two benchmark datasets demonstrate that SEHR outperforms existing state-of-the-art methods.

Monocular texture 3D human reconstruction aims to create a complete 3D digital avatar from just a single front-view human RGB image. However, the geometric ambiguity inherent in a single 2D image and the scarcity of 3D human training data are the main obstacles limiting progress in this field. To address these issues, current methods employ prior geometric estimation networks to derive various human geometric forms, such as the SMPL model and normal maps. However, they struggle to integrate these modalities effectively, leading to view inconsistencies, such as facial distortions. To this end, we propose a two-process 3D human reconstruction framework, SAT, which seamlessly learns various prior geometries in a unified manner and reconstructs high-quality textured 3D avatars as the final output. To further facilitate geometry learning, we introduce a Supervisor Feature Regularization module. By employing a multi-view network with the same structure to provide intermediate features as training supervision, these varied geometric priors can be better fused. To tackle data scarcity and further improve reconstruction quality, we also propose an Online Animation Augmentation module. By building a one-feed-forward animation network, we augment a massive number of samples from the original 3D human data online for model training. Extensive experiments on two benchmarks show the superiority of our approach compared to state-of-the-art methods.

Composed Image Retrieval(CIR) aims to model users’ query intention with multiple modalities and retrieve the desired images from a large image corpus. The biggest challenge is how to effectively integrate the semantic information between two different modalities. A popular solution is to design attention-based modules to extract the query embedding in a coarse manner, which leads to certain confusion about search intention. To address this problem, we propose a new method for query integration, which is composed of two key modules, i.e., Multi-granular Subspace Fusion (MSF) and Residual Regression (RR) constraint. Specifically, MSF focuses on mining cross-modal semantic dependency between reference image regions and modification text pieces in multi-granular subspaces, which can construct an implicit, holistic semantic relationship in a fine manner. And RR constraint pushes the visual-text semantic alignment under specific supervision. Extensive experiments on three prevalent datasets demonstrate the state-of-the-art performance of our method.

Composed query image retrieval task aims to retrieve the target image in the database by a query that composes two different modalities: a reference image and a sentence declaring that some details of the reference image need to be modified and replaced by new elements. Tackling this task needs to learn a multimodal embedding space, which can make semantically similar targets and queries close but dissimilar targets and queries as far away as possible. Most of the existing methods start from the perspective of model structure and design some clever interactive modules to promote the better fusion and embedding of different modalities. However, their learning objectives use conventional query-level examples as negatives while neglecting the composed query's multimodal characteristics, leading to the inadequate utilization of the training data and suboptimal construction of metric space. To this end, in this paper, we propose to improve the learning objective by constructing and mining hard negative examples from the perspective of multimodal fusion. Specifically, we compose the reference image and its logically unpaired sentences rather than paired ones to create component-level negative examples to better use data and enhance the optimization of metric space. In addition, we further propose a new sentence augmentation method to generate more indistinguishable multimodal negative examples from the element level and help the model learn a better metric space. Massive comparison experiments on four real-world datasets confirm the effectiveness of the proposed method.

Composed image retrieval (CIR) is an emerging and challenging research task that combines two modalities, a reference image, and a modification text, into one query to retrieve the target image. In online shopping scenarios, the user would use the modification text as feedback to describe the difference between the reference and the desired image. In order to handle the task, there must be two main problems needed to be addressed. One is the localization problem: how to precisely find those spatial areas of the image mentioned by the text. The other is the modification problem: how to effectively modify the image semantics based on the text. However, existing methods merely fuse information coarsely from the two-modality, while the accurate spatial and semantic correspondence between these two heterogeneous features tends to be neglected. Therefore, image details cannot be precisely located and modified. To this end, we consider integrating information from the two modalities more accurately from spatial and semantic aspects. Thus, we propose an end-to-end framework for the CIR task, which contains three key components, i.e., Multi-level Collaborative Localization module (MCL), Differential Semantics Discrimination module (DSD), and Image Difference Enhancement constraints (IDE). Specifically, to solve the localization problem, MCL precisely locates the text to the image areas by collaboratively using text positioning information on multiple image layers. For the modification problem, DSD builds a distribution to evaluate the modification possibility of each image semantic dimension, and IDE effectively learns the modification patterns of text against image embedding based on the distribution. Extensive experiments on three datasets show that the proposed method achieves outstanding performance against the SOTA methods.

Composed image retrieval (CIR) aims at fusing a reference image and text feedback to search for the desired images. Compared to general image retrieval, it can model the users' search intent more comprehensively and search the target images more accurately, which has significant impacts in various real-world applications, such as E-commerce and Internet search. However, because of the existing heterogeneous semantic gap, the synthetic understanding and fusion of both image and text are difficult to implement. In this work, to tackle this difficult problem, we propose an end-to-end framework MCR, which uses text and images as retrieval queries. The framework mainly includes four pivotal modules. Specifically, we introduce the Relative Caption-aware Consistency (RCC) constraint to align text pieces and images in the database, which can effectually bridge the heterogeneous gap. The Multi-modal Complementary Fusion (MCF) and Cross-modal Guided Pooling (CGP) are constructed to mine multiple interactions between image local features and text word features and learn the complementary representation of the composed query. Furthermore, we develop a plug-and-play Weak-text Semantic Augment (WSA) module for datasets with short or incomplete query texts, which can supplement the weak-text features and is conducive to modeling an augmented semantic space. Extensive experiments demonstrate the practical superior performance over the existing state-of-the-art empirical algorithms on several benchmarks.

Composed image retrieval aims at retrieving the desired images, given a reference image and a text piece. To handle this task, two important subprocesses should be modeled reasonably. One is to erase irrelated details of the reference image against the text piece, and the other is to replenish the desired details in the image against the text piece. Nowadays, the existing methods neglect to distinguish between the two subprocesses and implicitly put them together to solve the composed image retrieval task. To explicitly and orderly model the two subprocesses of the task, we propose a novel composed image retrieval method which contains three key components, i.e., Multi-semantic Dynamic Suppression module (MDS), Text-semantic Complementary Selection module (TCS), and Semantic Space Alignment constraints (SSA). Concretely, MDS is to erase irrelated details of the reference image by suppressing its semantic features. TCS aims to select and enhance the semantic features of the text piece and then replenish them to the reference image. In the end, to facilitate the erasure and replenishment subprocesses, SSA aligns the semantics of the two modality features in the final space. Extensive experiments on three benchmark datasets (Shoes, FashionIQ, and Fashion200K) show the superior performance of our approach against state-of-the-art methods.

Composed image retrieval aims at performing image retrieval task by giving a reference image and a complementary text piece. Since composing both image and text information can accurately model the users' search intent, composed image retrieval can perform target-specific image retrieval task and be potentially applied to many scenarios such as interactive product search. However, two key challenging issues must be addressed in composed image retrieval occasion. One of them is how to fuse heterogeneous image and text piece in the query into a complementary feature space. The other is how to bridge the heterogeneous gap between text pieces in the query and images in the database. To address the issues, we propose an end-to-end framework for composed image retrieval, which consists of three key components including Multi-modal Complementary Fusion (MCF), Cross-modal Guided Pooling (CGP), and Relative Caption-aware Consistency (RCC). By incorporating MCF and CGP modules, we can fully integrate the complementary information of image and text piece in the query through multiple deep interactions and aggregate obtained local features into an embedding vector. To bridge the heterogeneous gap, we introduce the RCC constraint to align text pieces in the query and images in the database. Extensive experiments on four public benchmark datasets show that the proposed composed image retrieval framework achieves outstanding performance against the state-of-the-art methods.