CVPR 2024 Papers

In the field of computer vision Vision Transformers (ViTs) have emerged as a prominent deep learning architecture. Despite being inspired by Convolutional Neural Networks (CNNs) ViTs are susceptible to small spatial shifts in the input data - they lack shift-equivariance. To address this shortcoming we introduce novel data-adaptive designs for each of the ViT modules that break shift-equivariance such as tokenization self-attention patch merging and positional encoding. With our proposed modules we achieve perfect circular shift-equivariance across four prominent ViT architectures: Swin SwinV2 CvT and MViTv2. Additionally we leverage our design to further enhance consistency under standard shifts. We evaluate our adaptive ViT models on image classification and semantic segmentation tasks. Our models achieve competitive performance across three diverse datasets showcasing perfect (100%) circular shift consistency while improving standard shift consistency.

In this work we introduce two types of makeup prior models to extend existing 3D face prior models: PCA-based and StyleGAN2-based priors. The PCA-based prior model is a linear model that is easy to construct and is computationally efficient. However it retains only low-frequency information. Conversely the StyleGAN2-based model can represent high-frequency information with relatively higher computational cost than the PCA-based model. Although there is a trade-off between the two models both are applicable to 3D facial makeup estimation and related applications. By leveraging makeup prior models and designing a makeup consistency module we effectively address the challenges that previous methods faced in robustly estimating makeup particularly in the context of handling self-occluded faces. In experiments we demonstrate that our approach reduces computational costs by several orders of magnitude achieving speeds up to 180 times faster. In addition by improving the accuracy of the estimated makeup we confirm that our methods are highly advantageous for various 3D facial makeup applications such as 3D makeup face reconstruction user-friendly makeup editing makeup transfer and interpolation.

Despite the remarkable process of talking-head-based avatar-creating solutions directly generating anchor-style videos with full-body motions remains challenging. In this study we propose Make-Your-Anchor a novel system necessitating only a one-minute video clip of an individual for training subsequently enabling the automatic generation of anchor-style videos with precise torso and hand movements. Specifically we finetune a proposed structure-guided diffusion model on input video to render 3D mesh conditions into human appearances. We adopt a two-stage training strategy for the diffusion model effectively binding movements with specific appearances. To produce arbitrary long temporal video we extend the 2D U-Net in the frame-wise diffusion model to a 3D style without additional training cost and a simple yet effective batch-overlapped temporal denoising module is proposed to bypass the constraints on video length during inference. Finally a novel identity-specific face enhancement module is introduced to improve the visual quality of facial regions in the output videos. Comparative experiments demonstrate the effectiveness and superiority of the system in terms of visual quality temporal coherence and identity preservation outperforming SOTA diffusion/non-diffusion methods. Project page: https://github.com/ICTMCG/Make-Your-Anchor.

Creating and animating 3D biped cartoon characters is crucial and valuable in various applications. Compared with geometry the diverse texture design plays an important role in making 3D biped cartoon characters vivid and charming. Therefore we focus on automatic texture design for cartoon characters based on input instructions. This is challenging for domain-specific requirements and a lack of high-quality data. To address this challenge we propose Make-It-Vivid the first attempt to enable high-quality texture generation from text in UV space. We prepare a detailed text-texture paired data for 3D characters by using vision-question-answering agents. Then we customize a pretrained text-to-image model to generate texture map with template structure while preserving the natural 2D image knowledge. Furthermore to enhance fine-grained details we propose a novel adversarial learning scheme to shorten the domain gap between original dataset and realistic texture domain. Extensive experiments show that our approach outperforms current texture generation methods resulting in efficient character texturing and faithful generation with prompts. Besides we showcase various applications such as out of domain generation and texture stylization. We also provide an efficient generation system for automatic text-guided textured character generation and animation.

Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately current state-of-the-art video generation methods primarily focusing on text-to-video generation tend to produce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text instructions is insufficient and suboptimal for video generation. In this paper we introduce PixelDance a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions setting a new standard for video generation.

Deep Neural Networks (DNNs) are powerful tools for various computer vision tasks yet they often struggle with reliable uncertainty quantification -a critical requirement for real-world applications. Bayesian Neural Networks (BNN) are equipped for uncertainty estimation but cannot scale to large DNNs where they are highly unstable to train. To address this challenge we introduce the Adaptable Bayesian Neural Network (ABNN) a simple and scalable strategy to seamlessly transform DNNs into BNNs in a post-hoc manner with minimal computational and training overheads. ABNN preserves the main predictive properties of DNNs while enhancing their uncertainty quantification abilities through simple BNN adaptation layers (attached to normalization layers) and a few fine-tuning steps on pre-trained models. We conduct extensive experiments across multiple datasets for image classification and semantic segmentation tasks and our results demonstrate that ABNN achieves state-of-the-art performance without the computational budget typically associated with ensemble methods.

This paper studies the human image animation task which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. Despite achieving reasonable results these approaches face challenges in maintaining temporal consistency throughout the animation due to the lack of temporal modeling and poor preservation of reference identity. In this work we introduce MagicAnimate a diffusion-based framework that aims at enhancing temporal consistency preserving reference image faithfully and improving animation fidelity. To achieve this we first develop a video diffusion model to encode temporal information. Second to maintain the appearance coherence across frames we introduce a novel appearance encoder to retain the intricate details of the reference image. Leveraging these two innovations we further employ a simple video fusion technique to encourage smooth transitions for long video animation. Empirical results demonstrate the superiority of our method over baseline approaches on two benchmarks. Notably our approach outperforms the strongest baseline by over 38% in terms of video fidelity on the challenging TikTok dancing dataset. Code and model will be made available at https://showlab.github.io/magicanimate.

Single-modal object re-identification (ReID) faces great challenges in maintaining robustness within complex visual scenarios. In contrast multi-modal object ReID utilizes complementary information from diverse modalities showing great potentials for practical applications. However previous methods may be easily affected by irrelevant backgrounds and usually ignore the modality gaps. To address above issues we propose a novel learning framework named EDITOR to select diverse tokens from vision Transformers for multi-modal object ReID. We begin with a shared vision Transformer to extract tokenized features from different input modalities. Then we introduce a Spatial-Frequency Token Selection (SFTS) module to adaptively select object-centric tokens with both spatial and frequency information. Afterwards we employ a Hierarchical Masked Aggregation (HMA) module to facilitate feature interactions within and across modalities. Finally to further reduce the effect of backgrounds we propose a Background Consistency Constraint (BCC) and an Object-Centric Feature Refinement (OCFR). They are formulated as two new loss functions which improve the feature discrimination with background suppression. As a result our framework can generate more discriminative features for multi-modal object ReID. Extensive experiments on three multi-modal ReID benchmarks verify the effectiveness of our methods. The code is available at https://github.com/924973292/EDITOR.

Human matting is a foundation task in image and video processing where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional guidance or improve the temporal consistency of a single instance across frames. We propose a new framework MaGGIe Masked Guided Gradual Human Instance Matting which predicts alpha mattes progressively for each human instances while maintaining the computational cost precision and consistency. Our method leverages modern architectures including transformer attention and sparse convolution to output all instance mattes simultaneously without exploding memory and latency. Although keeping constant inference costs in the multiple-instance scenario our framework achieves robust and versatile performance on our proposed synthesized benchmarks. With the higher quality image and video matting benchmarks the novel multi-instance synthesis approach from publicly available sources is introduced to increase the generalization of models in real-world scenarios. Our code and datasets are available at https://maggie-matt.github.io

Despite the emergence of successful NeRF inpainting methods built upon explicit RGB and depth 2D inpainting supervisions these methods are inherently constrained by the capabilities of their underlying 2D inpainters. This is due to two key reasons: (i) independently inpainting constituent images results in view-inconsistent imagery and (ii) 2D inpainters struggle to ensure high-quality geometry completion and alignment with inpainted RGB images. To overcome these limitations we propose a novel approach called MVIP-NeRF that harnesses the potential of diffusion priors for NeRF inpainting addressing both appearance and geometry aspects. MVIP-NeRF performs joint inpainting across multiple views to reach a consistent solution which is achieved via an iterative optimization process based on Score Distillation Sampling (SDS). Apart from recovering the rendered RGB images we also extract normal maps as a geometric representation and define a normal SDS loss that motivates accurate geometry inpainting and alignment with the appearance. Additionally we formulate a multi-view SDS score function to distill generative priors simultaneously from different view images ensuring consistent visual completion when dealing with large view variations. Our experimental results show better appearance and geometry recovery than previous NeRF inpainting methods.

In this era the success of large language models and text-to-image models can be attributed to the driving force of large-scale datasets. However in the realm of 3D vision while remarkable progress has been made with models trained on large-scale synthetic and real-captured object data like Objaverse and MVImgNet a similar level of progress has not been observed in the domain of human-centric tasks partially due to the lack of a large-scale human dataset. Existing datasets of high-fidelity 3D human capture continue to be mid-sized due to the significant challenges in acquiring large-scale high-quality 3D human data. To bridge this gap we present MVHumanNet a dataset that comprises multi-view human action sequences of 4500 human identities. The primary focus of our work is on collecting human data that features a large number of diverse identities and everyday clothing using a multi-view human capture system which facilitates easily scalable data collection. Our dataset contains 9000 daily outfits 60000 motion sequences and 645 million frames with extensive annotations including human masks camera parameters 2D and 3D keypoints SMPL/SMPLX parameters and corresponding textual descriptions. To explore the potential of MVHumanNet in various 2D and 3D visual tasks we conducted pilot studies on view-consistent action recognition human NeRF reconstruction text-driven view-unconstrained human image generation as well as 2D view-unconstrained human image and 3D avatar generation. Extensive experiments demonstrate the performance improvements and effective applications enabled by the scale provided by MVHumanNet. As the current largest-scale 3D human dataset we hope that the release of MVHumanNet data with annotations will foster further innovations in the domain of 3D human-centric tasks at scale.

We present MVD-Fusion: a method for single-view 3D inference via generative modeling of multi-view-consistent RGB-D images. While recent methods pursuing 3D inference advocate learning novel-view generative models these generations are not 3D-consistent and require a distillation process to generate a 3D output. We instead cast the task of 3D inference as directly generating mutually-consistent multiple views and build on the insight that additionally inferring depth can provide a mechanism for enforcing this consistency. Specifically we train a denoising diffusion model to generate multi-view RGB-D images given a single RGB input image and leverage the (intermediate noisy) depth estimates to obtain reprojection-based conditioning to maintain multi-view consistency. We train our model using large-scale synthetic dataset Obajverse as well as the real-world CO3D dataset comprising of generic camera viewpoints. We demonstrate that our approach can yield more accurate synthesis compared to recent state-of-the-art including distillation-based 3D inference and prior multi-view generation methods. We also evaluate the geometry induced by our multi-view depth prediction and find that it yields a more accurate representation than other direct 3D inference approaches.

Multi-view photometric stereo (MVPS) recovers a high-fidelity 3D shape of a scene by benefiting from both multi-view stereo and photometric stereo. While photometric stereo boosts detailed shape reconstruction it necessitates recording images under various light conditions for each viewpoint. In particular calibrating the light directions for each view significantly increases the cost of acquiring images. To make MVPS more accessible we introduce a practical and easy-to-implement setup multi-view constrained photometric stereo (MVCPS) where the light directions are unknown but constrained to move together with the camera. Unlike conventional multi-view uncalibrated photometric stereo our constrained setting reduces the ambiguities of surface normal estimates from per-view linear ambiguities to a single and global linear one thereby simplifying the disambiguation process. The proposed method integrates the ambiguous surface normal into neural surface reconstruction (NeuS) to simultaneously resolve the global ambiguity and estimate the detailed 3D shape. Experiments demonstrate that our method estimates accurate shapes under sparse viewpoints using only a few multi-view constrained light sources.

With the rapid development of Multi-modal Large Language Models (MLLMs) a number of diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities of these models. However most benchmarks predominantly assess spatial understanding in the static image tasks while overlooking temporal understanding in the dynamic video tasks. To alleviate this issue we introduce a comprehensive Multi-modal Video understanding Benchmark namely MVBench which covers 20 challenging video tasks that cannot be effectively solved with a single frame. Specifically we first introduce a novel static-to-dynamic method to define these temporal-related tasks. By transforming various static tasks into dynamic ones we enable the systematic generation of video tasks that require a broad spectrum of temporal skills ranging from perception to cognition. Then guided by the task definition we automatically convert public video annotations into multiple-choice QA to evaluate each task. On one hand such a distinct paradigm allows us to build MVBench efficiently without much manual intervention. On the other hand it guarantees evaluation fairness with ground-truth video annotations avoiding the biased scoring of LLMs. Moreover we further develop a robust video MLLM baseline i.e. VideoChat2 by progressive multi-modal training with diverse instruction-tuning data. The extensive results on our MVBench reveal that the existing MLLMs are far from satisfactory in temporal understanding while our VideoChat2 largely surpasses these leading models by over 15% on MVBench.

State-of-the-art video-text retrieval (VTR) methods typically involve fully fine-tuning a pre-trained model (e.g. CLIP) on specific datasets. However this can result in significant storage costs in practical applications as a separate model per task must be stored. To address this issue we present our pioneering work that enables parameter-efficient VTR using a pre-trained model with only a small number of tunable parameters during training. Towards this goal we propose a new method dubbed Multimodal Video Adapter (MV-Adapter) for efficiently transferring the knowledge in the pre-trained CLIP from image-text to video-text. Specifically MV-Adapter utilizes bottleneck structures in both video and text branches along with two novel components. The first is a Temporal Adaptation Module that is incorporated in the video branch to introduce global and local temporal contexts. We also train weights calibrations to adjust to dynamic variations across frames. The second is Cross Modality Tying that generates weights for video/text branches through sharing cross modality factors for better aligning between modalities. Thanks to above innovations MV-Adapter can achieve comparable or better performance than standard fine-tuning with negligible parameters overhead. Notably MV-Adapter consistently outperforms various competing methods in V2T/T2V tasks with large margins on five widely used VTR benchmarks (MSR-VTT MSVD LSMDC DiDemo and ActivityNet). Codes will be released.

While excellent in transfer learning Vision-Language models (VLMs) come with high computational costs due to their large number of parameters. To address this issue removing parameters via model pruning is a viable solution. However existing techniques for VLMs are task-specific and thus require pruning the network from scratch for each new task of interest. In this work we explore a new direction: Task-Agnostic Vision-Language Pruning (TA-VLP). Given a pretrained VLM the goal is to find a unique pruned counterpart transferable to multiple unknown downstream tasks. In this challenging setting the transferable representations already encoded in the pretrained model are a key aspect to preserve. Thus we propose Multimodal Flow Pruning (MULTIFLOW) a first gradient-free pruning framework for TA-VLP where: (i) the importance of a parameter is expressed in terms of its magnitude and its information flow by incorporating the saliency of the neurons it connects; and (ii) pruning is driven by the emergent (multimodal) distribution of the VLM parameters after pretraining. We benchmark eight state-of-the-art pruning algorithms in the context of TA-VLP experimenting with two VLMs three vision-language tasks and three pruning ratios. Our experimental results show that MULTIFLOW outperforms recent sophisticated combinatorial competitors in the vast majority of the cases paving the way towards addressing TA-VLP. The code is publicly available at https://github.com/FarinaMatteo/multiflow.

We propose a novel approach to video anomaly detection: we treat feature vectors extracted from videos as realizations of a random variable with a fixed distribution and model this distribution with a neural network. This lets us estimate the likelihood of test videos and detect video anomalies by thresholding the likelihood estimates. We train our video anomaly detector using a modification of denoising score matching a method that injects training data with noise to facilitate modeling its distribution. To eliminate hyperparameter selection we model the distribution of noisy video features across a range of noise levels and introduce a regularizer that tends to align the models for different levels of noise. At test time we combine anomaly indications at multiple noise scales with a Gaussian mixture model. Running our video anomaly detector induces minimal delays as inference requires merely extracting the features and forward-propagating them through a shallow neural network and a Gaussian mixture model. Our experiments on five popular video anomaly detection benchmarks demonstrate state-of-the-art performance both in the object-centric and in the frame-centric setup.

Text-to-image generation has achieved astonishing results yet precise spatial controllability and prompt fidelity remain highly challenging. This limitation is typically addressed through cumbersome prompt engineering scene layout conditioning or image editing techniques which often require hand drawn masks. Nonetheless pre-existing works struggle to take advantage of the natural instance-level compositionality of scenes due to the typically flat nature of rasterized RGB output images. Towards addressing this challenge we introduce MuLAn: a novel dataset comprising over 44K MUlti-Layer ANnotations of RGB images as multi-layer instance-wise RGBA decompositions and over 100K instance images. To build MuLAn we developed a training free pipeline which decomposes a monocular RGB image into a stack of RGBA layers comprising of background and isolated instances. We achieve this through the use of pretrained general-purpose models and by developing three modules: image decomposition for instance discovery and extraction instance completion to reconstruct occluded areas and image re-assembly. We use our pipeline to create MuLAn-COCO and MuLAn-LAION datasets which contain a variety of image decompositions in terms of style composition and complexity. With MuLAn we provide the first photorealistic resource providing instance decomposition and occlusion information for high quality images opening up new avenues for text-to-image generative AI research. With this we aim to encourage the development of novel generation and editing technology in particular layer-wise solutions. MuLAn data resources are available at https://MuLAn-dataset.github.io/

Multi-target multi-camera tracking is a crucial task that involves identifying and tracking individuals over time using video streams from multiple cameras. This task has practical applications in various fields such as visual surveillance crowd behavior analysis and anomaly detection. However due to the difficulty and cost of collecting and labeling data existing datasets for this task are either synthetically generated or artificially constructed within a controlled camera network setting which limits their ability to model real-world dynamics and generalize to diverse camera configurations. To address this issue we present MTMMC a real-world large-scale dataset that includes long video sequences captured by 16 multi-modal cameras in two different environments - campus and factory - across various time weather and season conditions. This dataset provides a challenging test bed for studying multi-camera tracking under diverse real-world complexities and includes an additional input modality of spatially aligned and temporally synchronized RGB and thermal cameras which enhances the accuracy of multi-camera tracking. MTMMC is a super-set of existing datasets benefiting independent fields such as person detection re-identification and multiple object tracking. We provide baselines and new learning setups on this dataset and set the reference scores for future studies. The datasets models and test server will be made publicly available.