CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
3D face reconstruction aims at generating high-fidelity 3D face shapes and textures from single-view or multi-view images. However current prevailing facial texture generation methods generally suffer from low-quality texture identity information loss and inadequate handling of occlusions. To solve these problems we introduce an Identity-Conditioned Latent Diffusion Model for face UV-texture generation (UV-IDM) to generate photo-realistic textures based on the Basel Face Model (BFM). UV-IDM leverages the powerful texture generation capacity of a latent diffusion model (LDM) to obtain detailed facial textures. To preserve the identity during the reconstruction procedure we design an identity-conditioned module that can utilize any in-the-wild image as a robust condition for the LDM to guide texture generation. UV-IDM can be easily adapted to different BFM-based methods as a high-fidelity texture generator. Furthermore in light of the limited accessibility of most existing UV-texture datasets we build a large-scale and publicly available UV-texture dataset based on BFM termed BFM-UV. Extensive experiments show that our UV-IDM can generate high-fidelity textures in 3D face reconstruction within seconds while maintaining image consistency bringing new state-of-the-art performance in facial texture generation.
Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models and training directly on 3D shapes. When training a diffusion or flow models on 3D shapes a crucial design choice is the shape representation. An effective shape representation needs to adhere three design principles: it should allow an efficient conversion of large 3D datasets to the representation form; it should provide a good tradeoff of approximation power versus number of parameters; and it should have a simple tensorial form that is compatible with existing powerful neural architectures. While standard 3D shape representations such as volumetric grids and point clouds do not adhere to all these principles simultaneously we advocate in this paper a new representation that does. We introduce Mosaic-SDF (M-SDF): a simple 3D shape representation that approximates the Signed Distance Function (SDF) of a given shape by using a set of local grids spread near the shape's boundary. The M-SDF representation is fast to compute for each shape individually making it readily parallelizable; it is parameter efficient as it only covers the space around the shape's boundary; and it has a simple matrix form compatible with Transformer-based architectures.
The M-SDF representation is fast to compute for each shape individually making it readily parallelizable; it is parameter efficient as it only covers the space around the shape's boundary; and it has a simple matrix form compatible with Transformer-based architectures. We demonstrate the efficacy of the M-SDF representation by using it to train a 3D generative flow model including class-conditioned generation with the ShapeNetCore-V2 (3D Warehouse) dataset and text-to-3D generation using a dataset of about 600k caption-shape pairs.
Diffusion handles is a novel approach to enable 3D object edits on diffusion images requiring only existing pre-trained diffusion models depth estimation without any fine-tuning or 3D object retrieval. The edited results remain plausible photo-real and preserve object identity. Diffusion handles address a critically missing facet of generative image-based creative design. Our key insight is to lift diffusion activations for a selected object to 3D using a proxy depth 3D-transform the depth and associated activations and project them back to image space. The diffusion process guided by the manipulated activations produces plausible edited images showing complex 3D occlusion and lighting effects. We evaluate diffusion handles: quantitatively on a large synthetic data benchmark; and qualitatively by a user study showing our output to be more plausible and better than prior art at both 3D editing and identity control.
Extensive advancements have been made in person ReID through the mining of semantic information. Nevertheless existing methods that utilize semantic-parts from a single image modality do not explicitly achieve this goal. Whiteness the impressive capabilities in multimodal understanding of Vision Language Foundation Model CLIP a recent two-stage CLIP-based method employs automated prompt engineering to obtain specific textual labels for classifying pedestrians. However we note that the predefined soft prompts may be inadequate in expressing the entire visual context and struggle to generalize to unseen classes. This paper presents an end-to-end Prompt-driven Semantic Guidance (PromptSG) framework that harnesses the rich semantics inherent in CLIP. Specifically we guide the model to attend to regions that are semantically faithful to the prompt. To provide personalized language descriptions for specific individuals we propose learning pseudo tokens that represent specific visual contexts. This design not only facilitates learning fine-grained attribute information but also can inherently leverage language prompts during inference. Without requiring additional labeling efforts our PromptSG achieves state-of-the-art by over 10% on MSMT17 and nearly 5% on the Market-1501 benchmark.
Sharpness-Aware Minimization (SAM) has been instrumental in improving deep neural network training by minimizing both training loss and loss sharpness. Despite the practical success the mechanisms behind SAM's generalization enhancements remain elusive limiting its progress in deep learning optimization. In this work we investigate SAM's core components for generalization improvement and introduce "Friendly-SAM" (F-SAM) to further enhance SAM's generalization. Our investigation reveals the key role of batch-specific stochastic gradient noise within the adversarial perturbation i.e. the current minibatch gradient which significantly influences SAM's generalization performance. By decomposing the adversarial perturbation in SAM into full gradient and stochastic gradient noise components we discover that relying solely on the full gradient component degrades generalization while excluding it leads to improved performance. The possible reason lies in the full gradient component's increase in sharpness loss for the entire dataset creating inconsistencies with the subsequent sharpness minimization step solely on the current minibatch data. Inspired by these insights F-SAM aims to mitigate the negative effects of the full gradient component. It removes the full gradient estimated by an exponentially moving average (EMA) of historical stochastic gradients and then leverages stochastic gradient noise for improved generalization.
Inspired by these insights F-SAM aims to mitigate the negative effects of the full gradient component. It removes the full gradient estimated by an exponentially moving average (EMA) of historical stochastic gradients and then leverages stochastic gradient noise for improved generalization. Moreover we provide theoretical validation for the EMA approximation and prove the convergence of F-SAM on non-convex problems. Extensive experiments demonstrate the superior generalization performance and robustness of F-SAM over vanilla SAM. Code is available at https://github.com/nblt/F-SAM.
Diffusion models have made tremendous progress in text-driven image and video generation. Now text-to-image foundation models are widely applied to various downstream image synthesis tasks such as controllable image generation and image editing while downstream video synthesis tasks are less explored for several reasons. First it requires huge memory and computation overhead to train a video generation foundation model. Even with video foundation models additional costly training is still required for downstream video synthesis tasks. Second although some works extend image diffusion models into videos in a training-free manner temporal consistency cannot be well preserved. Finally these adaption methods are specifically designed for one task and fail to generalize to different tasks. To mitigate these issues we propose a training-free general-purpose video synthesis framework coined as BIVDiff via bridging specific image diffusion models and general text-to-video foundation diffusion models. Specifically we first use a specific image diffusion model (e.g. ControlNet and Instruct Pix2Pix) for frame-wise video generation then perform Mixed Inversion on the generated video and finally input the inverted latents into the video diffusion models (e.g. VidRD and ZeroScope) for temporal smoothing.
ControlNet and Instruct Pix2Pix) for frame-wise video generation then perform Mixed Inversion on the generated video and finally input the inverted latents into the video diffusion models (e.g. VidRD and ZeroScope) for temporal smoothing. This decoupled framework enables flexible image model selection for different purposes with strong task generalization and high efficiency. To validate the effectiveness and general use of BIVDiff we perform a wide range of video synthesis tasks including controllable video generation video editing video inpainting and outpainting.
Despite their exceptional performance in vision tasks deep learning models often struggle when faced with domain shifts during testing. Test-Time Training (TTT) methods have recently gained popularity by their ability to enhance the robustness of models through the addition of an auxiliary objective that is jointly optimized with the main task. Being strictly unsupervised this auxiliary objective is used at test time to adapt the model without any access to labels. In this work we propose Noise-Contrastive Test-Time Training (NC-TTT) a novel unsupervised TTT technique based on the discrimination of noisy feature maps. By learning to classify noisy views of projected feature maps and then adapting the model accordingly on new domains classification performance can be recovered by an important margin. Experiments on several popular test-time adaptation baselines demonstrate the advantages of our method compared to recent approaches for this task. The code can be found at: https://github.com/GustavoVargasHakim/NCTTT.git
The complex dynamicity of open-world objects presents non-negligible challenges for multi-object tracking (MOT) often manifested as severe deformations fast motion and occlusions. Most methods that solely depend on coarse-grained object cues such as boxes and the overall appearance of the object are susceptible to degradation due to distorted internal relationships of dynamic objects. To address this problem this work proposes NetTrack an efficient generic and affordable tracking framework to introduce fine-grained learning that is robust to dynamicity. Specifically NetTrack constructs a dynamicity-aware association with a fine-grained Net leveraging point-level visual cues. Correspondingly a fine-grained sampler and matching method have been incorporated. Furthermore NetTrack learns object-text correspondence for fine-grained localization. To evaluate MOT in extremely dynamic open-world scenarios a bird flock tracking (BFT) dataset is constructed which exhibits high dynamicity with diverse species and open-world scenarios. Comprehensive evaluation on BFT validates the effectiveness of fine-grained learning on object dynamicity and thorough transfer experiments on challenging open-world benchmarks i.e. TAO TAO-OW AnimalTrack and GMOT-40 validate the strong generalization ability of NetTrack even without finetuning.
Existing approaches to video understanding mainly designed for short videos from a third-person perspective are limited in their applicability in certain fields such as robotics. In this paper we delve into open-ended question-answering (QA) in long egocentric videos which allows individuals or robots to inquire about their own past visual experiences. This task presents unique challenges including the complexity of temporally grounding queries within extensive video content the high resource demands for precise data annotation and the inherent difficulty of evaluating open-ended answers due to their ambiguous nature. Our proposed approach tackles these challenges by (i) integrating query grounding and answering within a unified model to reduce error propagation; (ii) employing large language models for efficient and scalable data synthesis; and (iii) introducing a close-ended QA task for evaluation to manage answer ambiguity. Extensive experiments demonstrate the effectiveness of our method which also achieves state-of-the-art performance on the QAEgo4D and Ego4D-NLQ benchmarks. Code data and models are open-sourced at https://github.com/Becomebright/GroundVQA.
Predicting the trajectories of road agents is essential for autonomous driving systems. The recent mainstream methods follow a static paradigm which predicts the future trajectory by using a fixed duration of historical frames. These methods make the predictions independently even at adjacent time steps which leads to potential instability and temporal inconsistency. As successive time steps have largely overlapping historical frames their forecasting should have intrinsic correlation such as overlapping predicted trajectories should be consistent or be different but share the same motion goal depending on the road situation. Motivated by this in this work we introduce HPNet a novel dynamic trajectory forecasting method. Aiming for stable and accurate trajectory forecasting our method leverages not only historical frames including maps and agent states but also historical predictions. Specifically we newly design a Historical Prediction Attention module to automatically encode the dynamic relationship between successive predictions. Besides it also extends the attention range beyond the currently visible window benefitting from the use of historical predictions. The proposed Historical Prediction Attention together with the Agent Attention and Mode Attention is further formulated as the Triple Factorized Attention module serving as the core design of HPNet. Experiments on the Argoverse and INTERACTION datasets show that HPNet achieves state-of-the-art performance and generates accurate and stable future trajectories.
The proposed Historical Prediction Attention together with the Agent Attention and Mode Attention is further formulated as the Triple Factorized Attention module serving as the core design of HPNet. Experiments on the Argoverse and INTERACTION datasets show that HPNet achieves state-of-the-art performance and generates accurate and stable future trajectories. Our code are available at https://github.com/XiaolongTang23/HPNet.
While recent depth completion methods have achieved remarkable results filling in relatively dense depth maps (e.g. projected 64-line LiDAR on KITTI or 500 sampled points on NYUv2) with RGB guidance their performance on very sparse input (e.g. 4-line LiDAR or 32 depth point measurements) is unverified. These sparser regimes present new challenges as a 4-line LiDAR increases the distance between pixels without depth and their nearest depth point sixfold from 5 pixels to 30 pixels compared to 64 lines. Observing that existing methods struggle with sparse and variable distribution depth maps we propose an Affinity-Based Shift Correction (ASC) module that iteratively aligns depth predictions to input depth based on predicted affinities between image pixels and depth points. Our framework enables each depth point to adaptively influence and improve predictions across the image leading to largely improved results for fewer-line fewer-point and variable sparsity settings. Further we show improved performance in domain transfer from KITTI to nuScenes and from random sampling to irregular point distributions. Our correction module can easily be added to any depth completion or RGB-only depth estimation model notably allowing the latter to perform both completion and estimation with a single model.
Data-free knowledge distillation is able to utilize the knowledge learned by a large teacher network to augment the training of a smaller student network without accessing the original training data avoiding privacy security and proprietary risks in real applications. In this line of research existing methods typically follow an inversion-and-distillation paradigm in which a generative adversarial network on-the-fly trained with the guidance of the pre-trained teacher network is used to synthesize a large-scale sample set for knowledge distillation. In this paper we reexamine this common data-free knowledge distillation paradigm showing that there is considerable room to improve the overall training efficiency through a lens of "small-scale inverted data for knowledge distillation". In light of three empirical observations indicating the importance of how to balance class distributions in terms of synthetic sample diversity and difficulty during both data inversion and distillation processes we propose Small Scale Data-free Knowledge Distillation (SSD-KD). In formulation SSD-KD introduces a modulating function to balance synthetic samples and a priority sampling function to select proper samples facilitated by a dynamic replay buffer and a reinforcement learning strategy. As a result SSD-KD can perform distillation training conditioned on an extremely small scale of synthetic samples (e.g.
In formulation SSD-KD introduces a modulating function to balance synthetic samples and a priority sampling function to select proper samples facilitated by a dynamic replay buffer and a reinforcement learning strategy. As a result SSD-KD can perform distillation training conditioned on an extremely small scale of synthetic samples (e.g. 10x less than the original training data scale) making the overall training efficiency one or two orders of magnitude faster than many mainstream methods while retaining superior or competitive model performance as demonstrated on popular image classification and semantic segmentation benchmarks. The code is available at https://github.com/OSVAI/SSD-KD.
Generative models can produce impressively realistic images. This paper demonstrates that generated images have geometric features different from those of real images. We build a set of collections of generated images prequalified to fool simple signal-based classifiers into believing they are real. We then show that prequalified generated images can be identified reliably by classifiers that only look at geometric properties. We use three such classifiers. All three classifiers are denied access to image pixels and look only at derived geometric features. The first classifier looks at the perspective field of the image the second looks at lines detected in the image and the third looks at relations between detected objects and shadows. Our procedure detects generated images more reliably than SOTA local signal based detectors for images from a number of distinct generators. Saliency maps suggest that the classifiers can identify geometric problems reliably. We conclude that current generators cannot reliably reproduce geometric properties of real images.
Domain generalization (DG) based Face Anti-Spoofing (FAS) aims to improve the model's performance on unseen domains. Existing methods either rely on domain labels to align domain-invariant feature spaces or disentangle generalizable features from the whole sample which inevitably lead to the distortion of semantic feature structures and achieve limited generalization. In this work we make use of large-scale VLMs like CLIP and leverage the textual feature to dynamically adjust the classifier's weights for exploring generalizable visual features. Specifically we propose a novel Class Free Prompt Learning (CFPL) paradigm for DG FAS which utilizes two lightweight transformers namely Content Q-Former (CQF) and Style Q-Former (SQF) to learn the different semantic prompts conditioned on content and style features by using a set of learnable query vectors respectively. Thus the generalizable prompt can be learned by two improvements: (1) A Prompt-Text Matched (PTM) supervision is introduced to ensure CQF learns visual representation that is most informative of the content description. (2) A Diversified Style Prompt (DSP) technology is proposed to diversify the learning of style prompts by mixing feature statistics between instance-specific styles.
(2) A Diversified Style Prompt (DSP) technology is proposed to diversify the learning of style prompts by mixing feature statistics between instance-specific styles. Finally the learned text features modulate visual features to generalization through the designed Prompt Modulation (PM). Extensive experiments show that the CFPL is effective and outperforms the state-of-the-art methods on several cross-domain datasets.
Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging given the complexity of gigapixel slides. Traditionally MIL interpretability is limited to identifying salient regions deemed pertinent for downstream tasks offering little insight to the end-user (pathologist) regarding the rationale behind these selections. To address this we propose Self-Interpretable MIL (SI-MIL) a method intrinsically designed for interpretability from the very outset. SI-MIL employs a deep MIL framework to guide an interpretable branch grounded on handcrafted pathological features facilitating linear predictions. Beyond identifying salient regions SI-MIL uniquely provides feature-level interpretations rooted in pathological insights for WSIs. Notably SI-MIL with its linear prediction constraints challenges the prevalent myth of an inevitable trade-off between model interpretability and performance demonstrating competitive results compared to state-of-the-art methods on WSI-level prediction tasks across three cancer types. In addition we thoroughly benchmark the local- and global-interpretability of SI-MIL in terms of statistical analysis a domain expert study and desiderata of interpretability namely user-friendliness and faithfulness.
Generating realistic hand motion sequences in interaction with objects has gained increasing attention with the growing interest in digital humans. Prior work has illustrated the effectiveness of employing occupancy-based or distance-based virtual sensors to extract hand-object interaction features. Nonetheless these methods show limited generalizability across object categories shapes and sizes. We hypothesize that this is due to two reasons: 1) the limited expressiveness of employed virtual sensors and 2) scarcity of available training data. To tackle this challenge we introduce a novel joint-centered sensor designed to reason about local object geometry near potential interaction regions. The sensor queries for object surface points in the neighbourhood of each hand joint. As an important step towards mitigating the learning complexity we transform the points from global frame to hand template frame and use a shared module to process sensor features of each individual joint. This is followed by a spatio-temporal transformer network aimed at capturing correlation among the joints in different dimensions. Moreover we devise simple heuristic rules to augment the limited training sequences with vast static hand grasping samples. This leads to a broader spectrum of grasping types observed during training in turn enhancing our model's generalization capability. We evaluate on two public datasets GRAB and InterCap where our method shows superiority over baselines both quantitatively and perceptually.