CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Dance serves as a powerful medium for expressing human emotions but the lifelike generation of dance is still a considerable challenge. Recently diffusion models have showcased remarkable generative abilities across various domains. They hold promise for human motion generation due to their adaptable many-to-many nature. Nonetheless current diffusion-based motion generation models often create entire motion sequences directly and unidirectionally lacking focus on the motion with local and bidirectional enhancement. When choreographing high-quality dance movements people need to take into account not only the musical context but also the nearby music-aligned dance motions. To authentically capture human behavior we propose a Bidirectional Autoregressive Diffusion Model (BADM) for music-to-dance generation where a bidirectional encoder is built to enforce that the generated dance is harmonious in both the forward and backward directions. To make the generated dance motion smoother a local information decoder is built for local motion enhancement. The proposed framework is able to generate new motions based on the input conditions and nearby motions which foresees individual motion slices iteratively and consolidates all predictions. To further refine the synchronicity between the generated dance and the beat the beat information is incorporated as an input to generate better music-aligned dance movements.
The proposed framework is able to generate new motions based on the input conditions and nearby motions which foresees individual motion slices iteratively and consolidates all predictions. To further refine the synchronicity between the generated dance and the beat the beat information is incorporated as an input to generate better music-aligned dance movements. Experimental results demonstrate that the proposed model achieves state-of-the-art performance compared to existing unidirectional approaches on the prominent benchmark for music-to-dance generation.
Large-scale visual-language pre-trained models have achieved significant success in various video tasks. However most existing methods follow an "adapt then align" paradigm which adapts pre-trained image encoders to model video-level representations and utilizes one-hot or text embedding of the action labels for supervision. This paradigm overlooks the challenge of mapping from static images to complicated activity concepts. In this paper we propose a novel "Align before Adapt" (ALT) paradigm. Prior to adapting to video representation learning we exploit the entity-to-region alignments for each frame. The alignments are fulfilled by matching the region-aware image embeddings to an offline-constructed text corpus. With the aligned entities we feed their text embeddings to a transformer-based video adapter as the queries which can help extract the semantics of the most important entities from a video to a vector. This paradigm reuses the visual-language alignment of VLP during adaptation and tries to explain an action by the underlying entities. This helps understand actions by bridging the gap with complex activity semantics particularly when facing unfamiliar or unseen categories. ALT demonstrates competitive performance while maintaining remarkably low computational costs.
This paradigm reuses the visual-language alignment of VLP during adaptation and tries to explain an action by the underlying entities. This helps understand actions by bridging the gap with complex activity semantics particularly when facing unfamiliar or unseen categories. ALT demonstrates competitive performance while maintaining remarkably low computational costs. In fully supervised experiments it achieves 88.1% top-1 accuracy on Kinetics-400 with only 4947 GFLOPs. Moreover ALT outperforms the previous state-of-the-art methods in both zero-shot and few-shot experiments emphasizing its superior generalizability across various learning scenarios.
Recent advancements in vision-language foundation models have significantly enhanced open-vocabulary 3D scene understanding. However the generalizability of existing methods is constrained due to their framework designs and their reliance on 3D data. We address this limitation by introducing Generalizable Open-Vocabulary Neural Semantic Fields (GOV-NeSF) a novel approach offering a generalizable implicit representation of 3D scenes with open-vocabulary semantics. We aggregate the geometry-aware features using a cost volume and propose a Multi-view Joint Fusion module to aggregate multi-view features through a cross-view attention mechanism which effectively predicts view-specific blending weights for both colors and open-vocabulary features. Remarkably our GOV-NeSF exhibits state-of-the-art performance in both 2D and 3D open-vocabulary semantic segmentation eliminating the need for ground truth semantic labels or depth priors and effectively generalize across scenes and datasets without fine-tuning.
The remarkable efficacy of text-to-image diffusion models has motivated extensive exploration of their potential application in video domains. Zero-shot methods seek to extend image diffusion models to videos without necessitating model training. Recent methods mainly focus on incorporating inter-frame correspondence into attention mechanisms. However the soft constraint imposed on determining where to attend to valid features can sometimes be insufficient resulting in temporal inconsistency. In this paper we introduce FRESCO intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint. This enhancement ensures a more consistent transformation of semantically similar content across frames. Beyond mere attention guidance our approach involves an explicit update of features to achieve high spatial-temporal consistency with the input video significantly improving the visual coherence of the resulting translated videos. Extensive experiments demonstrate the effectiveness of our proposed framework in producing high-quality coherent videos marking a notable improvement over existing zero-shot methods.
Single-pixel imaging (SPI) is a potential computational imaging technique which produces image by solving an ill-posed reconstruction problem from few measurements captured by a single-pixel detector. Deep learning has achieved impressive success on SPI reconstruction. However previous poor reconstruction performance and impractical imaging model limit its real-world applications. In this paper we propose a deep unfolding network with hybrid-attention Transformer on Kronecker SPI model dubbed HATNet to improve the imaging quality of real SPI cameras. Specifically we unfold the computation graph of the iterative shrinkage-thresholding algorithm (ISTA) into two alternative modules: efficient tensor gradient descent and hybrid-attention multi-scale denoising. By virtue of Kronecker SPI the gradient descent module can avoid high computational overheads rooted in previous gradient descent modules based on vectorized SPI. The denoising module is an encoder-decoder architecture powered by dual-scale spatial attention for high- and low-frequency aggregation and channel attention for global information recalibration. Moreover we build a SPI prototype to verify the effectiveness of the proposed method. Extensive experiments on synthetic and real data demonstrate that our method achieves the state-of-the-art performance. The source code and pre-trained models are available at https://github.com/Gang-Qu/HATNet-SPI.
Detecting objects in 3D under various (normal and adverse) weather conditions is essential for safe autonomous driving systems. Recent approaches have focused on employing weather-insensitive 4D radar sensors and leveraging them with other modalities such as LiDAR. However they fuse multi-modal information without considering the sensor characteristics and weather conditions and lose some height information which could be useful for localizing 3D objects. In this paper we propose a novel framework for robust LiDAR and 4D radar-based 3D object detection. Specifically we propose a 3D-LRF module that considers the distinct patterns they exhibit in 3D space (e.g. precise 3D mapping of LiDAR and wide-range weather-insensitive measurement of 4D radar) and extract fusion features based on their 3D spatial relationship. Then our weather-conditional radar-flow gating network modulates the information flow of fusion features depending on weather conditions and obtains enhanced feature that effectively incorporates the strength of two domains under various weather conditions. The extensive experiments demonstrate that our model achieves SoTA performance for 3D object detection under various weather conditions.
Leveraging multi-view diffusion models as priors for 3D optimization have alleviated the problem of 3D consistency e.g. the Janus face problem or the content drift problem in zero-shot text-to-3D models. However the 3D geometric fidelity of the output remains an unresolved issue; albeit the rendered 2D views are realistic the underlying geometry may contain errors such as unreasonable concavities. In this work we propose CorrespondentDream an effective method to leverage annotation-free cross-view correspondences yielded from the diffusion U-Net to provide additional 3D prior to the NeRF optimization process. We find that these correspondences are strongly consistent with human perception and by adopting it in our loss design we are able to produce NeRF models with geometries that are more coherent with common sense e.g. more smoothed object surface yielding higher 3D fidelity. We demonstrate the efficacy of our approach through various comparative qualitative results and a solid user study.
Knowledge of lane topology is a core problem in autonomous driving. Aerial imagery can provide high resolution quickly updatable lane source data but detecting lanes from such data has so far been an expensive manual process or where automated solutions exist undrivable and requiring of downstream processing. We propose a method for large-scale lane topology extraction from aerial imagery while ensuring that the resulting lanes are realistic and drivable by introducing a novel Bezier Graph shared parameterisation of Bezier curves. We develop a transformer-based model to predict these Bezier Graphs from input aerial images demonstrating competitive results on the UrbanLaneGraph dataset. We demonstrate that our method generates realistic lane graphs which require both minimal input and minimal downstream processing. We make our code publicly available at https://github.com/driskai/BGFormer
We present SplattingAvatar a hybrid 3D representation of photorealistic human avatars with Gaussian Splatting embedded on a triangle mesh which renders over 300 FPS on a modern GPU and 30 FPS on a mobile device. We disentangle the motion and appearance of a virtual human with explicit mesh geometry and implicit appearance modeling with Gaussian Splatting. The Gaussians are defined by barycentric coordinates and displacement on a triangle mesh as Phong surfaces. We extend lifted optimization to simultaneously optimize the parameters of the Gaussians while walking on the triangle mesh. SplattingAvatar is a hybrid representation of virtual humans where the mesh represents low-frequency motion and surface deformation while the Gaussians take over the high-frequency geometry and detailed appearance. Unlike existing deformation methods that rely on an MLP-based linear blend skinning (LBS) field for motion we control the rotation and translation of the Gaussians directly by mesh which empowers its compatibility with various animation techniques e.g. skeletal animation blend shapes and mesh editing. Trainable from monocular videos for both full-body and head avatars SplattingAvatar shows state-of-the-art rendering quality across multiple datasets.
Reconstructing an avatar from a portrait image has many applications in multimedia but remains a challenging research problem. Extracting reflectance maps and geometry from one image is ill-posed: recovering geometry is a one-to-many mapping problem and reflectance and light are difficult to disentangle. Accurate geometry and reflectance can be captured under the controlled conditions of a light stage but it is costly to acquire large datasets in this fashion. Moreover training solely with this type of data leads to poor generalization with in-the-wild images. This motivates the introduction of MoSAR a method for 3D avatar generation from monocular images. We propose a semi-supervised training scheme that improves generalization by learning from both light stage and in-the-wild datasets. This is achieved using a novel differentiable shading formulation. We show that our approach effectively disentangles the intrinsic face parameters producing relightable avatars. As a result MoSAR estimates a richer set of skin reflectance maps and generates more realistic avatars than existing state-of-the-art methods. We also release a new dataset that provides intrinsic face attributes (diffuse specular ambient occlusion and translucency maps) for 10k subjects.
In the realm of geospatial analysis the diversity of remote sensors encompassing both optical and microwave technologies offers a wealth of distinct observational capabilities. Recognizing this we present msGFM a multisensor geospatial foundation model that effectively unifies data from four key sensor modalities. This integration spans an expansive dataset of two million multisensor images. msGFM is uniquely adept at handling both paired and unpaired sensor data. For data originating from identical geolocations our model employs an innovative cross-sensor pretraining approach in masked image modeling enabling the synthesis of joint representations from diverse sensors. msGFM incorporating four remote sensors upholds strong performance forming a comprehensive model adaptable to various sensor types. msGFM has demonstrated enhanced proficiency in a range of both single-sensor and multisensor downstream tasks. These include scene classification segmentation cloud removal and pan-sharpening. A key discovery of our research is that representations derived from natural images are not always compatible with the distinct characteristics of geospatial remote sensors underscoring the limitations of existing representations in this field. Our work can serve as a guide for developing multisensor geospatial pretraining models paving the way for more advanced geospatial capabilities.
Our work can serve as a guide for developing multisensor geospatial pretraining models paving the way for more advanced geospatial capabilities. Code can be found at \url https://github.com/boranhan/Geospatial_Foundation_Models
We study visually grounded VideoQA in response to the emerging trends of utilizing pretraining techniques for video- language understanding. Specifically by forcing vision- language models (VLMs) to answer questions and simultane- ously provide visual evidence we seek to ascertain the extent to which the predictions of such techniques are genuinely anchored in relevant video content versus spurious corre- lations from language or irrelevant visual context. Towards this we construct NExT-GQA - an extension of NExT-QA with 10.5K temporal grounding (or location) labels tied to the original QA pairs. With NExT-GQA we scrutinize a series of state-of-the-art VLMs. Through post-hoc atten- tion analysis we find that these models are extremely weak in substantiating the answers despite their strong QA per- formance. This exposes the limitation of current VLMs in making reliable predictions. As a remedy we further explore and propose a grounded-QA method via Gaussian mask optimization and cross-modal learning. Experiments with different backbones demonstrate that this grounding mechanism improves both grounding and QA. With these efforts we aim to push towards trustworthy VLMs in VQA systems.
As a remedy we further explore and propose a grounded-QA method via Gaussian mask optimization and cross-modal learning. Experiments with different backbones demonstrate that this grounding mechanism improves both grounding and QA. With these efforts we aim to push towards trustworthy VLMs in VQA systems. Our dataset and code are available at https://github.com/doc-doc/NExT-GQA.
Detecting edges in images suffers from the problems of (P1) heavy imbalance between positive and negative classes as well as (P2) label uncertainty owing to disagreement between different annotators. Existing solutions address P1 using class-balanced cross-entropy loss and dice loss and P2 by only predicting edges agreed upon by most annotators. In this paper we propose RankED a unified ranking-based approach that addresses both the imbalance problem (P1) and the uncertainty problem (P2). RankED tackles these two problems with two components: One component which ranks positive pixels over negative pixels and the second which promotes high confidence edge pixels to have more label certainty. We show that RankED outperforms previous studies and sets a new state-of-the-art on NYUD-v2 BSDS500 and Multi-cue datasets. Code is available at https://ranked-cvpr24.github.io.
We present DiffHuman a probabilistic method for photorealistic 3D human reconstruction from a single RGB image. Despite the ill-posed nature of this problem most methods are deterministic and output a single solution often resulting in a lack of geometric detail and blurriness in unseen or uncertain regions. In contrast DiffHuman predicts a probability distribution over 3D reconstructions conditioned on an input 2D image which allows us to sample multiple detailed 3D avatars that are consistent with the image. DiffHuman is implemented as a conditional diffusion model that denoises pixel-aligned 2D observations of an underlying 3D shape representation. During inference we may sample 3D avatars by iteratively denoising 2D renders of the predicted 3D representation. Furthermore we introduce a generator neural network that approximates rendering with considerably reduced runtime (55x speed up) resulting in a novel dual-branch diffusion framework. Our experiments show that DiffHuman can produce diverse and detailed reconstructions for the parts of the person that are unseen or uncertain in the input image while remaining competitive with the state-of-the-art when reconstructing visible surfaces.
Owe to the powerful generative priors the pre-trained text-to-image (T2I) diffusion models have become increasingly popular in solving the real-world image super-resolution problem. However as a consequence of the heavy quality degradation of input low-resolution (LR) images the destruction of local structures can lead to ambiguous image semantics. As a result the content of reproduced high-resolution image may have semantic errors deteriorating the super-resolution performance. To address this issue we present a semantics-aware approach to better preserve the semantic fidelity of generative real-world image super-resolution. First we train a degradation-aware prompt extractor which can generate accurate soft and hard semantic prompts even under strong degradation. The hard semantic prompts refer to the image tags aiming to enhance the local perception ability of the T2I model while the soft semantic prompts compensate for the hard ones to provide additional representation information. These semantic prompts encourage the T2I model to generate detailed and semantically accurate results. Furthermore during the inference process we integrate the LR images into the initial sampling noise to mitigate the diffusion model's tendency to generate excessive random details. The experiments show that our method can reproduce more realistic image details and hold better the semantics. The source code of our method can be found at https://github.com/cswry/SeeSR
Revolutionizing the field of deep learning Transformer-based models have achieved remarkable performance in many tasks. Recent research has recognized these models are robust to shuffling but are limited to inter-token permutation in the forward propagation. In this work we propose our definition of permutation equivariance a broader concept covering both inter- and intra- token permutation in the forward and backward propagation of neural networks. We rigorously proved that such permutation equivariance property can be satisfied on most vanilla Transformer-based models with almost no adaptation. We examine the property over a range of state-of-the-art models including ViT Bert GPT and others with experimental validations. Further as a proof-of-concept we explore how real-world applications including privacy-enhancing split learning and model authorization could exploit the permutation equivariance property which implicates wider intriguing application scenarios. The code is available at https://github.com/Doby-Xu/ST
Establishing an automatic evaluation metric that closely aligns with human judgments is essential for effectively developing image captioning models. Recent data-driven metrics have demonstrated a stronger correlation with human judgments than classic metrics such as CIDEr; however they lack sufficient capabilities to handle hallucinations and generalize across diverse images and texts partially because they compute scalar similarities merely using embeddings learned from tasks unrelated to image captioning evaluation. In this study we propose Polos a supervised automatic evaluation metric for image captioning models. Polos computes scores from multimodal inputs using a parallel feature extraction mechanism that leverages embeddings trained through large-scale contrastive learning. To train Polos we introduce Multimodal Metric Learning from Human Feedback (M2LHF) a framework for developing metrics based on human feedback. We constructed the Polaris dataset which comprises 131K human judgments from 550 evaluators which is approximately ten times larger than standard datasets. Our approach achieved state-of-the-art performance on Composite Flickr8K-Expert Flickr8K-CF PASCAL-50S FOIL and the Polaris dataset thereby demonstrating its effectiveness and robustness.
We introduce the video detours problem for navigating instructional videos. Given a source video and a natural language query asking to alter the how-to video's current path of execution in a certain way the goal is to find a related "detour video" that satisfies the requested alteration. To address this challenge we propose VidDetours a novel video-language approach that learns to retrieve the targeted temporal segments from a large repository of how-to's using video-and-text conditioned queries. Furthermore we devise a language-based pipeline that exploits how-to video narration text to create weakly supervised training data. We demonstrate our idea applied to the domain of how-to cooking videos where a user can detour from their current recipe to find steps with alternate ingredients tools and techniques. Validating on a ground truth annotated dataset of 16K samples we show our model's significant improvements over best available methods for video retrieval and question answering with recall rates exceeding the state of the art by 35%.