ECCV 2024 Papers

"Accurate object detection in LiDAR point clouds is a key prerequisite of robust and safe autonomous driving and robotics applications. Training the 3D object detectors currently involves the need to manually annotate vasts amounts of training data, which is very time-consuming and costly. As a result, the amount of annotated training data readily available is limited, and moreover these annotated datasets likely do not contain edge-case or otherwise rare instances, simply because the probability of them occurring in such a small dataset is low. In this paper, we propose a method to train 3D object detector without any need for manual annotations, by exploiting existing off-the-shelf vision components and by using the consistency of the world around us. The method can therefore be used to train a 3D detector by only collecting sensor recordings in the real world, which is extremely cheap and allows training using orders of magnitude more data than traditional fully-supervised methods. The method is evaluated on KITTI and Waymo Open datasets, where it outperforms all previous weakly-supervised methods and where it narrows the gap when compared to methods using human 3D labels. The source code of our method is publicly available at https://www.github. com/jskvrna/TCC-Det."

"Pose-driven human-image animation diffusion models have shown remarkable capabilities in realistic human video synthesis. Despite the promising results achieved by previous approaches, challenges persist in achieving temporally consistent animation and ensuring robustness with off-the-shelf pose detectors. In this paper, we present , a pose-driven human image animation method that is robust to erroneous poses and consistent over time. In contrast to previous methods, we utilize the pre-trained ControlNet without fine-tuning to leverage its extensive pre-acquired knowledge from numerous pose-image-caption pairs. To keep the ControlNet frozen, we adapt LoRA to the UNet layers, enabling the network to align the latent space between the pose and appearance features. Additionally, by introducing an additional temporal layer to the ControlNet, we enhance robustness against outliers of the pose detector. Through the analysis of attention maps over the temporal axis, we also designed a novel temperature map leveraging pose information, allowing for a more static background. Extensive experiments demonstrate that the proposed method can achieve promising results in video synthesis tasks encompassing various poses, like chibi. Project Page: https://eccv2024tcan.github.io/"

"Recent techniques for text-to-4D generation synthesize dynamic 3D scenes using supervision from pre-trained text-to-video models. However, existing representations, such as deformation models or time-dependent neural representations, are limited in the amount of motion they can generate—they cannot synthesize motion extending far beyond the bounding box used for volume rendering. The lack of a more flexible motion model contributes to the gap in realism between 4D generation methods and recent, near-photorealistic video generation models. Here, we propose TC4D: trajectory-conditioned text-to-4D generation, an approach that factors motion into global and local components. We represent the global motion of a scene’s bounding box using rigid transformation along a trajectory parameterized by a spline. We learn local deformations that conform to the global trajectory using supervision from a text-to-video model. Our approach enables synthesis of scenes animated along arbitrary trajectories, compositional scene generation, and significant improvements to the realism and amount of generated motion, which we evaluate qualitatively and through a user study. Video results can be viewed on our website: https://sherwinbahmani.github. io/tc4d."

"In this paper, we propose a simple yet effective approach for Tracking Any Point with TRansformers (). Based on the observation that point tracking bears a great resemblance to object detection and tracking, we borrow designs from DETR-like algorithms to address the task of TAP. In , in each video frame, each tracking point is represented as a point query, which consists of a positional part and a content part. As in DETR, each query (its position and content feature) is naturally updated layer by layer. Its visibility is predicted by its updated content feature. Queries belonging to the same tracking point can exchange information through self-attention along the temporal dimension. As all such operations are well-designed in DETR-like algorithms, the model is conceptually very simple. We also adopt some useful designs such as cost volume from optical flow models and develop simple designs to provide long temporal information while mitigating the feature drifting issue. demonstrates strong performance with state-of-the-art performance on various datasets with faster inference speed."

"Out-of-distribution (OOD) detection has been extensively studied for the reliable deployment of deep-learning models. Despite great progress in this research direction, most works focus on discriminative classifiers and perform OOD detection based on single-modal representations that consist of either visual or textual features. Moreover, they rely on training with in-distribution (ID) data. The emergence of vision-language models allows to perform zero-shot OOD detection by leveraging multi-modal feature embeddings and therefore only rely on labels defining ID data. Several approaches have been devised but these either need a given OOD label set, which might deviate from real OOD data, or fine-tune CLIP, which potentially has to be done for different ID datasets. In this paper, we first adapt various OOD scores developed for discriminative classifiers to CLIP. Further, we propose an enhanced method named TAG based on Text prompt AuGmentation to amplify the separation between ID and OOD data, which is simple but effective, and can be applied on various score functions. Its performance is demonstrated on CIFAR-100 and large-scale ImageNet-1k OOD detection benchmarks. It consistently improves AUROC and FPR95 on CIFAR-100 across four commonly used architectures over four baseline OOD scores. The average AUROC and FPR95 improvements are 6.35% and 10.67%, respectively. The results for ImageNet-1k follow a similar, but less pronounced pattern. The code is available at: https://github.com/XixiLiu95/TAG."

"While text-to-image diffusion models demonstrate impressive generation capabilities, they also exhibit vulnerability to backdoor attacks, which involve the manipulation of model outputs through malicious triggers. In this paper, for the first time, we propose a comprehensive defense method named T2IShield to detect, localize, and mitigate such attacks. Specifically, we find the ”Assimilation Phenomenon” on the cross-attention maps caused by the backdoor trigger. Based on this key insight, we propose two effective backdoor detection methods: Frobenius Norm Threshold Truncation and Covariance Discriminant Analysis. Besides, we introduce a binary-search approach to localize the trigger within a backdoor sample and assess the efficacy of existing concept editing methods in mitigating backdoor attacks. Empirical evaluations on two advanced backdoor attack scenarios show the effectiveness of our proposed defense method. For backdoor sample detection, T2IShield achieves a detection F1 score of 88.9% with low computational cost. Furthermore, T2IShield achieves a localization F1 score of 86.4% and invalidates 99% poisoned samples. Codes are released at https: //github.com/Robin-WZQ/T2IShield."

"We present , a highly practical model for open-set object detection. Previous open-set object detection methods relying on text prompts effectively encapsulate the abstract concept of common objects, but struggle with rare or complex object representation due to data scarcity and descriptive limitations. Conversely, visual prompts excel in depicting novel objects through concrete visual examples, but fall short in conveying the abstract concept of objects as effectively as text prompts. Recognizing the complementary strengths and weaknesses of both text and visual prompts, we introduce that synergizes both prompts within a single model through contrastive learning. accepts inputs in diverse formats, including text prompts, visual prompts, and the combination of both, so that it can handle different scenarios by switching between the two prompt modalities. Comprehensive experiments demonstrate that exhibits remarkable zero-shot object detection capabilities across a wide spectrum of scenarios. We show that text prompts and visual prompts can benefit from each other within the synergy, which is essential to cover massive and complicated real-world scenarios and pave the way towards generic object detection. API code is available at https://github.com/IDEA-Research/T-Rex."

"The scarcity of annotated data in LiDAR point cloud understanding hinders effective representation learning. Consequently, scholars have been actively investigating efficacious self-supervised pre-training paradigms. Nevertheless, temporal information, which is inherent in the LiDAR point cloud sequence, is consistently disregarded. To better utilize this property, we propose an effective pre-training strategy, namely Temporal Masked Auto-Encoders (T-MAE), which takes as input temporally adjacent frames and learns temporal dependency. A SiamWCA backbone, containing a Siamese encoder and a windowed cross-attention (WCA) module, is established for the two-frame input. Considering that the movement of an ego-vehicle alters the view of the same instance, temporal modeling also serves as a robust and natural data augmentation, enhancing the comprehension of target objects. is a powerful architecture but heavily relies on annotated data. Our pre-training strategy alleviates its demand for annotated data. Comprehensive experiments demonstrate that achieves the best performance on both Waymo and ONCE datasets among competitive self-supervised approaches."

"Point clouds are commonly used in various practical applications such as autonomous driving and the manufacturing industry. However, these point clouds often suffer from incompleteness due to limited perspectives, scanner resolution and occlusion. Therefore the prediction of missing parts performs a crucial task. In this paper, we propose a novel method for point cloud completion. We utilize a spherical template to guide the generation of the coarse complete template and generate the dynamic query tokens through a correspondence pooling (Corres-Pooling) query generator. Specifically, we first generate the coarse complete template by embedding a Gaussian spherical template into the partial input and transforming the template to best match the input. Then we use the Corres-Pooling query generator to refine the coarse template and generate dynamic query tokens which could be used to predict the complete point proxies. Finally, we generate the complete point cloud with a FoldingNet following the coarse-to-fine paradigm, according to the fine template and the predicted point proxies. Experimental results demonstrate that our T-CorresNet outperforms the state-of-the-art methods on several benchmarks. Our codes are available at https://github.com/df-boy/T-CorresNet."

"This paper introduces a method for synthesizing time-varying bidirectional reflectance distribution functions (BRDFs) by applying learned temporal changes to static BRDFs. Achieving realistic and natural changes in material appearance over time is crucial in computer graphics and virtual reality. Existing methods employ a parametric BRDF model, and the temporal changes in BRDFs are modeled by polynomial functions that represent the transitions of the BRDF parameters. However, the limited representational capabilities of both the parametric BRDF model and the polynomial temporal model restrict the fidelity of the appearance reproduction. In this paper, to overcome this limitation, we introduce a neural embedding for BRDFs and propose a neural temporal model that represents the temporal changes of BRDFs in the latent space, which allows flexible representations of BRDFs and temporal changes. The experiments using synthetic and real-world datasets demonstrate that the flexibility of the proposed approach achieves a faithful synthesis of temporal changes in material appearance."

"We present ESP, a novel method for context-aware full-body generation, that enables photo-realistic synthesis and inpainting of people wearing clothing that is semantically appropriate for the scene depicted in an input photograph. ESP is conditioned on a 2D pose and contextual cues that are extracted from the photograph of the scene and integrated into the generation process, where the clothing is modeled explicitly with human parsing masks (HPM). Generated HPMs are used as tight guiding masks for inpainting, such that no changes are made to the original background. Our models are trained on a dataset containing a set of in-the-wild photographs of people covering a wide range of different environments. The method is analyzed quantitatively and qualitatively, and we show that ESP outperforms the state-of-the-art on the task of contextual full-body generation. https://esp.is.tue.mpg.de/"

"Multi-label Intention Understanding (MIU) for images is a critical yet challenging domain, primarily due to the ambiguity of intentions leading to a resource-intensive annotation process. Current leading approaches are held back by the limited amount of labeled data. To mitigate the scarcity of annotated data, we leverage the Contrastive Language-Image Pre-training (CLIP) model, renowned for its wealth knowledge in textual and visual modalities. We introduce a novel framework, Intention Understanding with CLIP (IntCLIP), which utilizes a dual-branch approach. This framework exploits the ‘Sight’-oriented knowledge inherent in CLIP to augment ‘Semantic’-centric MIU tasks. Additionally, we propose Hierarchical Class Integration to effectively manage the complex layered label structure, aligning it with CLIP’s nuanced sentence feature extraction capabilities. Our Sight-assisted Aggregation further refines this model by infusing the semantic feature map with essential visual cues, thereby enhancing the intention understanding ability. Through extensive experiments conducted on the standard MIU benchmark and other subjective tasks such as Image Emotion Recognition, IntCLIP clearly demonstrates superiority over current state-of-the-art techniques. Code is available at https://github.com/yan9qu/ IntCLIP."

"Most recent unsupervised non-rigid 3D shape matching methods are based on the functional map framework due to its efficiency and superior performance. Nevertheless, respective methods struggle to obtain spatially smooth pointwise correspondences due to the lack of proper regularisation. In this work, inspired by the success of message passing on graphs, we propose a synchronous diffusion process which we use as regularisation to achieve smoothness in non-rigid 3D shape matching problems. The intuition of synchronous diffusion is that diffusing the same input function on two different shapes results in consistent outputs. Using different challenging datasets, we demonstrate that our novel regularisation can substantially improve the state-of-the-art in shape matching, especially in the presence of topological noise."

"Synchronization involves the task of inferring unknown vertex values (belonging to a group) in a graph, from edges labeled with vertex relations. While many matrix groups (e.g., rotations or permutations) have received extensive attention in Computer Vision, a complete solution for projectivities is lacking. Only the 3 × 3 case has been addressed so far, by mapping the problem onto the Special Linear Group, but the 4 × 4 projective case has remained unexplored and is the focus here. We propose novel strategies to address this task, and demonstrate their effectiveness in synthetic experiments, as well as on an application to projective Structure from Motion."

"We consider the problem of transferring a temporal action segmentation system initially designed for exocentric (fixed) cameras to an egocentric scenario, where wearable cameras capture video data. The conventional supervised approach requires the collection and labeling of a new set of egocentric videos to adapt the model, which is costly and time-consuming. Instead, we propose a novel methodology which performs the adaptation leveraging existing labeled exocentric videos and a new set of unlabeled, synchronized exocentric-egocentric video pairs, for which temporal action segmentation annotations do not need to be collected. We implement the proposed methodology with an approach based on knowledge distillation, which we investigate both at the feature and Temporal Action Segmentation model level. Experiments on Assembly101 and EgoExo4D demonstrate the effectiveness of the proposed method against classic unsupervised domain adaptation and temporal alignment approaches. Without bells and whistles, our best model performs on par with supervised approaches trained on labeled egocentric data, without ever seeing a single egocentric label, achieving a +15.99 improvement in the edit score (28.59 vs 12.60) on the Assembly101 dataset compared to a baseline model trained solely on exocentric data. In similar settings, our method also improves edit score by +3.32 on the challenging EgoExo4D benchmark. Code is available here: https://github.com/fpv-iplab/ synchronization-is-all-you-need."

"Temporal video alignment aims to synchronize the key events like object interactions or action phase transitions in two videos. Such methods could benefit various video editing, processing, and understanding tasks. However, existing approaches operate under the restrictive assumption that a suitable video pair for alignment is given, significantly limiting their broader applicability. To address this, we re-pose temporal alignment as a search problem and introduce the task of Alignable Video Retrieval (AVR). Given a query video, our approach can identify well-alignable videos from a large collection of clips and temporally synchronize them to the query. To achieve this, we make three key contributions: 1) we introduce DRAQ, a video alignability indicator to identify and re-rank the best alignable video from a set of candidates; 2) we propose an effective and generalizable frame-level video feature design to improve the alignment performance of several off-the-shelf feature representations, and 3) we propose a novel benchmark and evaluation protocol for AVR using cycle-consistency metrics. Our experiments on 3 datasets, including large-scale Kinetics700, demonstrate the effectiveness of our approach in identifying alignable video pairs from diverse datasets."

"Acquiring complete point clouds for real-world scenarios is labor-intensive, making it impractical for conventional learning-based approaches. Numerous methods have been proposed to overcome this limitation by leveraging synthetic complete point clouds. While access to complete point clouds offers a notable advantage, they often struggle to bridge domain gaps, leading to sub-optimal performance. As a remedy, we propose a novel part-based framework for synthetic-to-real domain adaptation in point cloud completion. Our approach starts on the observation that domain gaps inherent in part information are relatively small, as parts are shared properties across categories regardless of domains. To employ part-based approach to point cloud completion, we introduce Part-Based Decomposition (PBD) module to generate part input point clouds. Subsequently, we design a Part-Aware Completion (PAC) module, which operates in a part-wise manner to produce complete point clouds. Within PAC, we devise a novel part-aware transformer to learn relationships between parts and utilize this information to infer missing parts in incomplete point clouds. Extensive experiments demonstrate that our part-based framework significantly outperforms existing studies on real-world point cloud datasets. The code is available at https: //github.com/yun-seo/PPCC"

"Diffusion models have achieved remarkable success across a range of generative tasks. Recent efforts to enhance diffusion model architectures have reimagined them as a form of multi-task learning, where each task corresponds to a denoising task at a specific noise level. While these efforts have focused on parameter isolation and task routing, they fall short of capturing detailed inter-task relationships and risk losing semantic information, respectively. In response, we introduce Switch Diffusion Transformer (Switch-DiT), which establishes inter-task relationships between conflicting tasks without compromising semantic information. To achieve this, we employ a sparse mixture-of-experts within each transformer block to utilize semantic information and facilitate handling conflicts in tasks through parameter isolation. Also, we propose a diffusion prior loss, encouraging similar tasks to share their denoising paths while isolating conflicting ones. Through these, each transformer block contains a shared expert across all tasks, where the common and task-specific denoising paths enable the diffusion model to construct its beneficial way of synergizing denoising tasks. Extensive experiments validate the effectiveness of our approach in improving both image quality and convergence rate, and further analysis demonstrates that Switch-DiT constructs tailored denoising paths across various generation scenarios. Our project page is available at https://byeongjun-park.github.io/Switch-DiT/."

"In this paper, we aim to enhance the performance of SwiftBrush, a prominent one-step text-to-image diffusion model, to be competitive with its multi-step Stable Diffusion counterpart. Initially, we explore the quality-diversity trade-off between SwiftBrush and SD Turbo: the former excels in image diversity, while the latter excels in image quality. This observation motivates our proposed modifications in the training methodology, including better weight initialization and efficient LoRA training. Moreover, our introduction of a novel clamped CLIP loss enhances image-text alignment and results in improved image quality. Remarkably, by combining the weights of models trained with efficient LoRA and full training, we achieve a new state-of-the-art one-step diffusion model, achieving an FID of 8.14 and surpassing all GAN-based and multi-step Stable Diffusion models."