CVPR 2024 Papers

We address the problem of synthesizing multi-view optical illusions: images that change appearance upon a transformation such as a flip or rotation. We propose a simple zero-shot method for obtaining these illusions from off-the-shelf text-to-image diffusion models. During the reverse diffusion process we estimate the noise from different views of a noisy image and then combine these noise estimates together and denoise the image. A theoretical analysis suggests that this method works precisely for views that can be written as orthogonal transformations of which permutations are a subset. This leads to the idea of a visual anagram ---an image that changes appearance under some rearrangement of pixels. This includes rotations and flips but also more exotic pixel permutations such as a jigsaw rearrangement. Our approach also naturally extends to illusions with more than two views. We provide both qualitative and quantitative results demonstrating the effectiveness and flexibility of our method. Please see our project webpage for additional visualizations and results: https://dangeng.github.io/visual_anagrams/

In the pursuit of robust and generalizable environment perception and language understanding the ubiquitous challenge of dataset bias continues to plague vision-and-language navigation (VLN) agents hindering their performance in unseen environments. This paper introduces the generalized cross-modal causal transformer (GOAT) a pioneering solution rooted in the paradigm of causal inference. By delving into both observable and unobservable confounders within vision language and history we propose the back-door and front-door adjustment causal learning (BACL and FACL) modules to promote unbiased learning by comprehensively mitigating potential spurious correlations. Additionally to capture global confounder features we propose a cross-modal feature pooling (CFP) module supervised by contrastive learning which is also shown to be effective in improving cross-modal representations during pre-training. Extensive experiments across multiple VLN datasets (R2R REVERIE RxR and SOON) underscore the superiority of our proposed method over previous state-of-the-art approaches. Code is available at https://github.com/CrystalSixone/VLN-GOAT.

Recently virtual staining technology has greatly promoted the advancement of histopathology. Despite the practical successes achieved the outstanding performance of most virtual staining methods relies on hard-to-obtain paired images in training. In this paper we propose a method for virtual immunohistochemistry (IHC) staining named confusion-GAN which does not require paired images and can achieve comparable performance to supervised algorithms. Specifically we propose a multi-branch discriminator which judges if the features of generated images can be embedded into the feature pool of target domain images to improve the visual quality of generated images. Meanwhile we also propose a novel patch-level pathology information extractor which is assisted by multiple instance learning to ensure pathological consistency during virtual staining. Extensive experiments were conducted on three types of IHC images including a high-resolution hepatocellular carcinoma immunohistochemical dataset proposed by us. The results demonstrated that our proposed confusion-GAN can generate highly realistic images that are capable of deceiving even experienced pathologists. Furthermore compared to using H&E images directly the downstream diagnosis achieved higher accuracy when using images generated by confusion-GAN. Our dataset and codes will be available at https://github.com/jiahanli2022/confusion-GAN.

Referring expressions for visual objects often include descriptions of relative spatial arrangements to other objects -- e.g. "to the right of" -- that depend on the point of view of the speaker. In 2D referring expression tasks this viewpoint is captured unambiguously in the image. However grounding expressions with such spatial language in 3D without viewpoint annotations can be ambiguous. In this paper we investigate the significance of viewpoint information in 3D visual grounding -- introducing a model that explicitly predicts the speaker's viewpoint based on the referring expression and scene. We pretrain this model on a synthetically generated dataset that provides viewpoint annotations and then finetune on 3D referring expression datasets. Further we introduce an auxiliary uniform object representation loss to encourage viewpoint invariance in learned object representations. We find that our proposed ViewPoint Prediction Network (VPP-Net) achieves state-of-the-art performance on ScanRefer SR3D and NR3D -- improving Accuracy@0.25IoU by 1.06% 0.60% and 2.00% respectively compared to prior work.

Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images. Yet the independent process of image generation in these prevailing methods leads to challenges in maintaining multiple-view consistency. To address this we introduce ViewFusion a novel training-free algorithm that can be seamlessly integrated into existing pre-trained diffusion models. Our approach adopts an auto-regressive method that implicitly leverages previously generated views as context for the next view generation ensuring robust multi-view consistency during the novel-view generation process. Through a diffusion process that fuses known-view information via interpolated denoising our framework successfully extends single-view conditioned models to work in multiple-view conditional settings without any additional fine-tuning. Extensive experimental results demonstrate the effectiveness of ViewFusion in generating consistent and detailed novel views.

3D asset generation is getting massive amounts of attention inspired by the recent success on text-guided 2D content creation. Existing text-to-3D methods use pretrained text-to-image diffusion models in an optimization problem or fine-tune them on synthetic data which often results in non-photorealistic 3D objects without backgrounds. In this paper we present a method that leverages pretrained text-to-image models as a prior and learn to generate multi-view images in a single denoising process from real-world data. Concretely we propose to integrate 3D volume-rendering and cross-frame-attention layers into each block of the existing U-Net network of the text-to-image model. Moreover we design an autoregressive generation that renders more 3D-consistent images at any viewpoint. We train our model on real-world datasets of objects and showcase its capabilities to generate instances with a variety of high-quality shapes and textures in authentic surroundings. Compared to the existing methods the results generated by our method are consistent and have favorable visual quality (-30% FID -37% KID).

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras such as ground-ground matching. However as a more practical scenario aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features namely hierarchical subtractive separation and orthogonal loss where the former separates these two features inside the VDT and the latter constrains these two to be independent. In addition we contribute a large-scale AGPReID dataset called CARGO consisting of five/eight aerial/ground cameras 5000 identities and 108563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID keeping the same magnitude of computational complexity. Our project is available at https://github.com/LinlyAC/VDT-AGPReID

As a problem often encountered in real-world scenarios multi-view multi-label learning has attracted considerable research attention. However due to oversights in data collection and uncertainties in manual annotation real-world data often suffer from incompleteness. Regrettably most existing multi-view multi-label learning methods sidestep missing views and labels. Furthermore they often neglect the potential of harnessing complementary information between views and labels thus constraining their classification capabilities. To address these challenges we propose a view-category interactive sharing transformer tailored for incomplete multi-view multi-label learning. Within this network we incorporate a two-layer transformer module to characterize the interplay between views and labels. Additionally to address view incompleteness a KNN-style missing view generation module is employed. Finally we introduce a view-category consistency guided embedding enhancement module to align different views and improve the discriminating power of the embeddings. Collectively these modules synergistically integrate to classify the incomplete multi-view multi-label data effectively. Extensive experiments substantiate that our approach outperforms the existing state-of-the-art methods.

This paper presents a novel aerial-to-ground feature aggregation strategy tailored for the task of cross-view image-based geo-localization. Conventional vision-based methods heavily rely on matching ground-view image features with a pre-recorded image database often through establishing planar homography correspondences via a planar ground assumption. As such they tend to ignore features that are off-ground and not suited for handling visual occlusions leading to unreliable localization in challenging scenarios. We propose a Top-to-Ground Aggregation module that capitalizes aerial orthographic views to aggregate features down to the ground level leveraging reliable off-ground information to improve feature alignment. Furthermore we introduce a Cycle Domain Adaptation loss that ensures feature extraction robustness across domain changes. Additionally an Equidistant Re-projection loss is introduced to equalize the impact of all keypoints on orientation error leading to a more extended distribution of keypoints which benefits orientation estimation. On both KITTI and Ford Multi-AV datasets our method consistently achieves the lowest mean longitudinal and lateral translations across different settings and obtains the smallest orientation error when the initial pose is less accurate a more challenging setting. Further it can complete an entire route through continual vehicle pose estimation with initial vehicle pose given only at the starting point.

Current diffusion-based video editing primarily focuses on structure-preserved editing by utilizing various dense correspondences to ensure temporal consistency and motion alignment. However these approaches are often ineffective when the target edit involves a shape change. To embark on video editing with shape change we explore customized video subject swapping in this work where we aim to replace the main subject in a source video with a target subject having a distinct identity and potentially different shape. In contrast to previous methods that rely on dense correspondences we introduce the VideoSwap framework that exploits semantic point correspondences inspired by our observation that only a small number of semantic points are necessary to align the subject's motion trajectory and modify its shape. We also introduce various user-point interactions (e.g. removing points and dragging points) to address various semantic point correspondence. Extensive experiments demonstrate state-of-the-art video subject swapping results across a variety of real-world videos.

Neural Radiance Fields (NeRFs) excel in photorealistically rendering static scenes. However rendering dynamic long-duration radiance fields on ubiquitous devices remains challenging due to data storage and computational constraints. In this paper we introduce VideoRF the first approach to enable real-time streaming and rendering of dynamic human-centric radiance fields on mobile platforms. At the core is a serialized 2D feature image stream representing the 4D radiance field all in one. We introduce a tailored training scheme directly applied to this 2D domain to impose the temporal and spatial redundancy of the feature image stream. By leveraging the redundancy we show that the feature image stream can be efficiently compressed by 2D video codecs which allows us to exploit video hardware accelerators to achieve real-time decoding. On the other hand based on the feature image stream we propose a novel rendering pipeline for VideoRF which has specialized space mappings to query radiance properties efficiently. Paired with a deferred shading model VideoRF has the capability of real-time rendering on mobile devices thanks to its efficiency. We have developed a real-time interactive player that enables online streaming and rendering of dynamic scenes offering a seamless and immersive free-viewpoint experience across a range of devices from desktops to mobile phones. Our project page is available at https://aoliao12138.github.io/VideoRF/.

Recently the advancement of self-supervised learning techniques like masked autoencoders (MAE) has greatly influenced visual representation learning for images and videos. Nevertheless it is worth noting that the predominant approaches in existing masked image / video modeling rely excessively on resource-intensive vision transformers (ViTs) as the feature encoder. In this paper we propose a new approach termed as VideoMAC which combines video masked autoencoders with resource-friendly ConvNets. Specifically VideoMAC employs symmetric masking on randomly sampled pairs of video frames. To prevent the issue of mask pattern dissipation we utilize ConvNets which are implemented with sparse convolutional operators as encoders. Simultaneously we present a simple yet effective masked video modeling (MVM) approach a dual encoder architecture comprising an online encoder and an exponential moving average target encoder aimed to facilitate inter-frame reconstruction consistency in videos. Additionally we demonstrate that VideoMAC empowering classical (ResNet) / modern (ConvNeXt) convolutional encoders to harness the benefits of MVM outperforms ViT-based approaches on downstream tasks including video object segmentation (+5.2% / 6.4% \mathcal J &\mathcal F ) body part propagation (+6.3% / 3.1% mIoU) and human pose tracking (+10.2% / 11.1% PCK@0.1).

Large Language Models (LLMs) have been enhanced with vision capabilities enabling them to comprehend images videos and interleaved vision-language content. However the learning methods of these large multimodal models (LMMs) typically treat videos as predetermined clips rendering them less effective and efficient at handling streaming video inputs. In this paper we propose a novel Learning-In-Video-Stream (LIVE) framework which enables temporally aligned long-context and real-time dialogue within a continuous video stream. Our LIVE framework comprises comprehensive approaches to achieve video streaming dialogue encompassing: (1) a training objective designed to perform language modeling for continuous streaming inputs (2) a data generation scheme that converts offline temporal annotations into a streaming dialogue format and (3) an optimized inference pipeline to speed up interactive chat in real-world video streams. With our LIVE framework we develop a simplified model called VideoLLM-online and demonstrate its significant advantages in processing streaming videos. For instance our VideoLLM-online-7B model can operate at over 10 FPS on an A100 GPU for a 5-minute video clip from Ego4D narration. Moreover VideoLLM-online also showcases state-of-the-art performance on public offline video benchmarks such as recognition captioning and forecasting. The code model data and demo have been made available at showlab.github.io/videollm-online.

Video grounding aims to localize a spatio-temporal section in a video corresponding to an input text query. This paper addresses a critical limitation in current video grounding methodologies by introducing an Open-Vocabulary Spatio-Temporal Video Grounding task. Unlike prevalent closed-set approaches that struggle with open-vocabulary scenarios due to limited training data and predefined vocabularies our model leverages pre-trained representations from foundational spatial grounding models. This empowers it to effectively bridge the semantic gap between natural language and diverse visual content achieving strong performance in closed-set and open-vocabulary settings. Our contributions include a novel spatio-temporal video grounding model surpassing state-of-the-art results in closed-set evaluations on multiple datasets and demonstrating superior performance in open-vocabulary scenarios. Notably the proposed model outperforms state-of-the-art methods in closed-set settings on VidSTG (Declarative and Interrogative) and HC-STVG (V1 and V2) datasets. Furthermore in open-vocabulary evaluations on HC-STVG V1 and YouCook-Interactions our model surpasses the recent best-performing models by 4.88 m_vIoU and 1.83 accuracy demonstrating its efficacy in handling diverse linguistic and visual concepts for improved video understanding. Our codes will be publicly released.

Existing approaches to unsupervised video instance segmentation typically rely on motion estimates and experience difficulties tracking small or divergent motions. We present VideoCutLER a simple method for unsupervised multi-instance video segmentation without using motion-based learning signals like optical flow or training on natural videos. Our key insight is that using high-quality pseudo masks and a simple video synthesis method for model training is surprisingly sufficient to enable the resulting video model to effectively segment and track multiple instances across video frames. We show the first competitive unsupervised learning results on the challenging YouTubeVIS-2019 benchmark achieving 50.7% AP50 surpassing the previous state-of-the-art by a large margin. VideoCutLER can also serve as a strong pretrained model for supervised video instance segmentation tasks exceeding DINO by 15.9% on YouTubeVIS-2019 in terms of AP.

Text-to-video generation aims to produce a video based on a given prompt. Recently several commercial video models have been able to generate plausible videos with minimal noise excellent details and high aesthetic scores. However these models rely on large-scale well-filtered high-quality videos that are not accessible to the community. Many existing research works which train models using the low-quality WebVid-10M dataset struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work we explore the training scheme of video models extended from Stable Diffusion and investigate the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model. We first analyze the connection between the spatial and temporal modules of video models and the distribution shift to low-quality videos. We observe that full training of all modules results in a stronger coupling between spatial and temporal modules than only training temporal modules. Based on this stronger coupling we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images resulting in a generic high-quality video model. Evaluations are conducted to demonstrate the superiority of the proposed method particularly in picture quality motion and concept composition.

Despite being (pre)trained on a massive amount of data state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video captions. Our work addresses this by identifying a broad spectrum of contrast misalignments such as replacing entities actions and flipping event order which alignment models should be robust against. To this end we introduce the VideoCon a video-language alignment dataset constructed by a large language model that generates plausible contrast video captions and explanations for differences between original and contrast video captions. Then a generative video-language model is finetuned with VideoCon to assess video-language entailment and generate explanations. Our VideoCon-based alignment model significantly outperforms current models. It exhibits a 12-point increase in AUC for the video-language alignment task on human-generated contrast captions. Finally our model sets new state of the art zero-shot performance in temporally-extensive video-language tasks such as text-to-video retrieval (SSv2-Temporal) and video question answering (ATP-Hard). Moreover our model shows superior performance on novel videos and human-crafted captions and explanations.

Text-driven video generation witnesses rapid progress. However merely using text prompts is not enough to depict the desired subject appearance that accurately aligns with users' intents especially for customized content creation. In this paper we study the task of video generation with image prompts which provide more accurate and direct content control beyond the text prompts. Specifically we propose a feed-forward framework VideoBooth with two dedicated designs: 1) We propose to embed image prompts in a coarse-to-fine manner. Coarse visual embeddings from image encoder provide high-level encodings of image prompts while fine visual embeddings from the proposed attention injection module provide multi-scale and detailed encoding of image prompts. These two complementary embeddings can faithfully capture the desired appearance. 2) In the attention injection module at fine level multi-scale image prompts are fed into different cross-frame attention layers as additional keys and values. This extra spatial information refines the details in the first frame and then it is propagated to the remaining frames which maintains temporal consistency. Extensive experiments demonstrate that VideoBooth achieves state-of-the-art performance in generating customized high-quality videos with subjects specified in image prompts. Notably VideoBooth is a generalizable framework where a single model works for a wide range of image prompts with only feed-forward passes.

Creating high-quality and interactive virtual environments such as games and simulators often involves complex and costly manual modeling processes. In this paper we present Video2Game a novel approach that automatically converts videos of real-world scenes into realistic and interactive game environments. At the heart of our system are three core components: (i) a neural radiance fields (NeRF) module that effectively captures the geometry and visual appearance of the scene; (ii) a mesh module that distills the knowledge from NeRF for faster rendering; and (iii) a physics module that models the interactions and physical dynamics among the objects. By following the carefully designed pipeline one can construct an interactable and actionable digital replica of the real world. We benchmark our system on both indoor and large-scale outdoor scenes. We show that we can not only produce highly-realistic renderings in real-time but also build interactive games on top.