Resolution-Agnostic Neural Compression for High-Fidelity Portrait Video Conferencing via Implicit Radiance Fields

Video conferencing has caught much more attention recently. High fidelity and low bandwidth are two major objectives of video compression for video conferencing applications. Most pioneering methods rely on classic video compression codec without high-level feature embedding and thus can not reach the extremely low bandwidth. Recent works instead employ model-based neural compression to acquire ultra-low bitrates using sparse representations of each frame such as facial landmark information, while these approaches can not maintain high fidelity due to 2D image-based warping. In this paper, we propose a novel low bandwidth neural compression approach for high-fidelity portrait video conferencing using implicit radiance fields to achieve both major objectives. We leverage dynamic neural radiance fields to reconstruct high-fidelity talking head with expression features, which are represented as frame substitution for transmission. The overall system employs deep model to encode expression features at the sender and reconstruct portrait at the receiver with volume rendering as decoder for ultra-low bandwidth. In particular, with the characteristic of neural radiance fields based model, our compression approach is resolution-agnostic, which means that the low bandwidth achieved by our approach is independent of video resolution, while maintaining fidelity for higher resolution reconstruction. Experimental results demonstrate that our novel framework can (1) construct ultra-low bandwidth video conferencing, (2) maintain high fidelity portrait and (3) have better performance on high-resolution video compression than previous works.

HistoHDR-Net: Histogram Equalization for Single LDR to HDR Image Translation

High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offers various data-driven methods for HDR image reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation of these approaches is missing details in regions of the reconstructed HDR images, which are over- or under-exposed in the input LDR images. To this end, we propose a simple and effective method, HistoHDR-Net, to recover the fine details (e.g., color, contrast, saturation, and brightness) of HDR images via a fusion-based approach utilizing histogram-equalized LDR images along with self-attention guidance. Our experiments demonstrate the efficacy of the proposed approach over the state-of-art methods.

Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis

Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since video content is highly redundant, we argue that naively bringing advances of image models to the video generation domain reduces motion fidelity, visual quality and impairs scalability. In this work, we build Snap Video, a video-first model that systematically addresses these challenges. To do that, we first extend the EDM framework to take into account spatially and temporally redundant pixels and naturally support video generation. Second, we show that a U-Net - a workhorse behind image generation - scales poorly when generating videos, requiring significant computational overhead. Hence, we propose a new transformer-based architecture that trains 3.31 times faster than U-Nets (and is ~4.5 faster at inference). This allows us to efficiently train a text-to-video model with billions of parameters for the first time, reach state-of-the-art results on a number of benchmarks, and generate videos with substantially higher quality, temporal consistency, and motion complexity. The user studies showed that our model was favored by a large margin over the most recent methods. See our website at https://snap-research.github.io/snapvideo/.

Robustly overfitting latents for flexible neural image compression

Neural image compression has made a great deal of progress. State-of-the-art models are based on variational autoencoders and are outperforming classical models. Neural compression models learn to encode an image into a quantized latent representation that can be efficiently sent to the decoder, which decodes the quantized latent into a reconstructed image. While these models have proven successful in practice, they lead to sub-optimal results due to imperfect optimization and limitations in the encoder and decoder capacity. Recent work shows how to use stochastic Gumbel annealing (SGA) to refine the latents of pre-trained neural image compression models. We extend this idea by introducing SGA+, which contains three different methods that build upon SGA. Further, we give a detailed analysis of our proposed methods, show how they improve performance, and show that they are less sensitive to hyperparameter choices. Besides, we show how each method can be extended to three- instead of two-class rounding. Finally, we show how refinement of the latents with our best-performing method improves the compression performance on the Tecnick dataset and how it can be deployed to partly move along the rate-distortion curve.

GenNBV: Generalizable Next-Best-View Policy for Active 3D Reconstruction

While recent advances in neural radiance field enable realistic digitization for large-scale scenes, the image-capturing process is still time-consuming and labor-intensive. Previous works attempt to automate this process using the Next-Best-View (NBV) policy for active 3D reconstruction. However, the existing NBV policies heavily rely on hand-crafted criteria, limited action space, or per-scene optimized representations. These constraints limit their cross-dataset generalizability. To overcome them, we propose GenNBV, an end-to-end generalizable NBV policy. Our policy adopts a reinforcement learning (RL)-based framework and extends typical limited action space to 5D free space. It empowers our agent drone to scan from any viewpoint, and even interact with unseen geometries during training. To boost the cross-dataset generalizability, we also propose a novel multi-source state embedding, including geometric, semantic, and action representations. We establish a benchmark using the Isaac Gym simulator with the Houses3K and OmniObject3D datasets to evaluate this NBV policy. Experiments demonstrate that our policy achieves a 98.26% and 97.12% coverage ratio on unseen building-scale objects from these datasets, respectively, outperforming prior solutions.

Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning

Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models. Since cross-modality medical data exhibit significant intra and inter-domain shifts and most are unlabelled, UDA is more important while challenging in medical image analysis. This paper proposes a simple yet potent contrastive learning framework for UDA to narrow the inter-domain gap between labelled source and unlabelled target distribution. Our method is validated on cerebral vessel datasets. Experimental results show that our approach can learn latent features from labelled 3DRA modality data and improve vessel segmentation performance in unlabelled MRA modality data.

BRI3L: A Brightness Illusion Image Dataset for Identification and Localization of Regions of Illusory Perception

Visual illusions play a significant role in understanding visual perception. Current methods in understanding and evaluating visual illusions are mostly deterministic filtering based approach and they evaluate on a handful of visual illusions, and the conclusions therefore, are not generic. To this end, we generate a large-scale dataset of 22,366 images (BRI3L: BRightness Illusion Image dataset for Identification and Localization of illusory perception) of the five types of brightness illusions and benchmark the dataset using data-driven neural network based approaches. The dataset contains label information - (1) whether a particular image is illusory/nonillusory, (2) the segmentation mask of the illusory region of the image. Hence, both the classification and segmentation task can be evaluated using this dataset. We follow the standard psychophysical experiments involving human subjects to validate the dataset. To the best of our knowledge, this is the first attempt to develop a dataset of visual illusions and benchmark using data-driven approach for illusion classification and localization. We consider five well-studied types of brightness illusions: 1) Hermann grid, 2) Simultaneous Brightness Contrast, 3) White illusion, 4) Grid illusion, and 5) Induced Grating illusion. Benchmarking on the dataset achieves 99.56% accuracy in illusion identification and 84.37% pixel accuracy in illusion localization. The application of deep learning model, it is shown, also generalizes over unseen brightness illusions like brightness assimilation to contrast transitions. We also test the ability of state-of-theart diffusion models to generate brightness illusions. We have provided all the code, dataset, instructions etc in the github repo: https://github.com/aniket004/BRI3L

A Comparison of Deep Learning Models for Proton Background Rejection with the AMS Electromagnetic Calorimeter

The Alpha Magnetic Spectrometer (AMS) is a high-precision particle detector onboard the International Space Station containing six different subdetectors. The Transition Radiation Detector and Electromagnetic Calorimeter (ECAL) are used to separate electrons/positrons from the abundant cosmic-ray proton background. The positron flux measured in space by AMS falls with a power law which unexpectedly softens above 25 GeV and then hardens above 280 GeV. Several theoretical models try to explain these phenomena, and a purer measurement of positrons at higher energies is needed to help test them. The currently used methods to reject the proton background at high energies involve extrapolating shower features from the ECAL to use as inputs for boosted decision tree and likelihood classifiers. We present a new approach for particle identification with the AMS ECAL using deep learning (DL). By taking the energy deposition within all the ECAL cells as an input and treating them as pixels in an image-like format, we train an MLP, a CNN, and multiple ResNets and Convolutional vision Transformers (CvTs) as shower classifiers. Proton rejection performance is evaluated using Monte Carlo (MC) events and ISS data separately. For MC, using events with a reconstructed energy between 0.2 - 2 TeV, at 90% electron accuracy, the proton rejection power of our CvT model is more than 5 times that of the other DL models. Similarly, for ISS data with a reconstructed energy between 50 - 70 GeV, the proton rejection power of our CvT model is more than 2.5 times that of the other DL models.

Key-Graph Transformer for Image Restoration

While it is crucial to capture global information for effective image restoration (IR), integrating such cues into transformer-based methods becomes computationally expensive, especially with high input resolution. Furthermore, the self-attention mechanism in transformers is prone to considering unnecessary global cues from unrelated objects or regions, introducing computational inefficiencies. In response to these challenges, we introduce the Key-Graph Transformer (KGT) in this paper. Specifically, KGT views patch features as graph nodes. The proposed Key-Graph Constructor efficiently forms a sparse yet representative Key-Graph by selectively connecting essential nodes instead of all the nodes. Then the proposed Key-Graph Attention is conducted under the guidance of the Key-Graph only among selected nodes with linear computational complexity within each window. Extensive experiments across 6 IR tasks confirm the proposed KGT's state-of-the-art performance, showcasing advancements both quantitatively and qualitatively.

Decomposition-based and Interference Perception for Infrared and Visible Image Fusion in Complex Scenes

Infrared and visible image fusion has emerged as a prominent research in computer vision. However, little attention has been paid on complex scenes fusion, causing existing techniques to produce sub-optimal results when suffers from real interferences. To fill this gap, we propose a decomposition-based and interference perception image fusion method. Specifically, we classify the pixels of visible image from the degree of scattering of light transmission, based on which we then separate the detail and energy information of the image. This refined decomposition facilitates the proposed model in identifying more interfering pixels that are in complex scenes. To strike a balance between denoising and detail preservation, we propose an adaptive denoising scheme for fusing detail components. Meanwhile, we propose a new weighted fusion rule by considering the distribution of image energy information from the perspective of multiple directions. Extensive experiments in complex scenes fusions cover adverse weathers, noise, blur, overexposure, fire, as well as downstream tasks including semantic segmentation, object detection, salient object detection and depth estimation, consistently indicate the effectiveness and superiority of the proposed method compared with the recent representative methods.