DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous Vehicle Testing

Generating realistic and diverse road scenarios is essential for autonomous vehicle testing and validation. Nevertheless, owing to the complexity and variability of real-world road environments, creating authentic and varied scenarios for intelligent driving testing is challenging. In this paper, we propose DiffRoad, a novel diffusion model designed to produce controllable and high-fidelity 3D road scenarios. DiffRoad leverages the generative capabilities of diffusion models to synthesize road layouts from white noise through an inverse denoising process, preserving real-world spatial features. To enhance the quality of generated scenarios, we design the Road-UNet architecture, optimizing the balance between backbone and skip connections for high-realism scenario generation. Furthermore, we introduce a road scenario evaluation module that screens adequate and reasonable scenarios for intelligent driving testing using two critical metrics: road continuity and road reasonableness. Experimental results on multiple real-world datasets demonstrate DiffRoad's ability to generate realistic and smooth road structures while maintaining the original distribution. Additionally, the generated scenarios can be fully automated into the OpenDRIVE format, facilitating generalized autonomous vehicle simulation testing. DiffRoad provides a rich and diverse scenario library for large-scale autonomous vehicle testing and offers valuable insights for future infrastructure designs that are better suited for autonomous vehicles.

Generative Emotion Cause Explanation in Multimodal Conversations

Multimodal conversation, a crucial form of human communication, carries rich emotional content, making the exploration of the causes of emotions within it a research endeavor of significant importance. However, existing research on the causes of emotions typically uses clause selection methods to locate the reason utterance, without providing a detailed explanation of the emotional causes. In this paper, we propose a new task, \textbf{M}ultimodal \textbf{C}onversation \textbf{E}motion \textbf{C}ause \textbf{E}xplanation (MCECE), aiming to generate a detailed explanation of the emotional cause to the target utterance within a multimodal conversation scenario. Building upon the MELD dataset, we develop a new dataset (ECEM) that integrates video clips with detailed explanations of character emotions, facilitating an in-depth examination of the causal factors behind emotional expressions in multimodal conversations.A novel approach, FAME-Net, is further proposed, that harnesses the power of Large Language Models (LLMs) to analyze visual data and accurately interpret the emotions conveyed through facial expressions in videos. By exploiting the contagion effect of facial emotions, FAME-Net effectively captures the emotional causes of individuals engaged in conversations. Our experimental results on the newly constructed dataset show that FAME-Net significantly outperforms several excellent large language model baselines. Code and dataset are available at \url{https://github.com/3222345200/ECEMdataset.git}

FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation

The increasing concern for data privacy has driven the rapid development of federated learning (FL), a privacy-preserving collaborative paradigm. However, the statistical heterogeneity among clients in FL results in inconsistent performance of the server model across various clients. Server model may show favoritism towards certain clients while performing poorly for others, heightening the challenge of fairness. In this paper, we reconsider the inconsistency in client performance distribution and introduce the concept of adversarial multi-armed bandit to optimize the proposed objective with explicit constraints on performance disparities. Practically, we propose a novel multi-armed bandit-based allocation FL algorithm (FedMABA) to mitigate performance unfairness among diverse clients with different data distributions. Extensive experiments, in different Non-I.I.D. scenarios, demonstrate the exceptional performance of FedMABA in enhancing fairness.

AuthFace: Towards Authentic Blind Face Restoration with Face-oriented Generative Diffusion Prior

Blind face restoration (BFR) is a fundamental and challenging problem in computer vision. To faithfully restore high-quality (HQ) photos from poor-quality ones, recent research endeavors predominantly rely on facial image priors from the powerful pretrained text-to-image (T2I) diffusion models. However, such priors often lead to the incorrect generation of non-facial features and insufficient facial details, thus rendering them less practical for real-world applications. In this paper, we propose a novel framework, namely AuthFace that achieves highly authentic face restoration results by exploring a face-oriented generative diffusion prior. To learn such a prior, we first collect a dataset of 1.5K high-quality images, with resolutions exceeding 8K, captured by professional photographers. Based on the dataset, we then introduce a novel face-oriented restoration-tuning pipeline that fine-tunes a pretrained T2I model. Identifying key criteria of quality-first and photography-guided annotation, we involve the retouching and reviewing process under the guidance of photographers for high-quality images that show rich facial features. The photography-guided annotation system fully explores the potential of these high-quality photographic images. In this way, the potent natural image priors from pretrained T2I diffusion models can be subtly harnessed, specifically enhancing their capability in facial detail restoration. Moreover, to minimize artifacts in critical facial areas, such as eyes and mouth, we propose a time-aware latent facial feature loss to learn the authentic face restoration process. Extensive experiments on the synthetic and real-world BFR datasets demonstrate the superiority of our approach.

CUBE360: Learning Cubic Field Representation for Monocular 360 Depth Estimation for Virtual Reality

Panoramic images provide comprehensive scene information and are suitable for VR applications. Obtaining corresponding depth maps is essential for achieving immersive and interactive experiences. However, panoramic depth estimation presents significant challenges due to the severe distortion caused by equirectangular projection (ERP) and the limited availability of panoramic RGB-D datasets. Inspired by the recent success of neural rendering, we propose a novel method, named $\mathbf{CUBE360}$, that learns a cubic field composed of multiple MPIs from a single panoramic image for $\mathbf{continuous}$ depth estimation at any view direction. Our CUBE360 employs cubemap projection to transform an ERP image into six faces and extract the MPIs for each, thereby reducing the memory consumption required for MPI processing of high-resolution data. Additionally, this approach avoids the computational complexity of handling the uneven pixel distribution inherent to equirectangular projectio. An attention-based blending module is then employed to learn correlations among the MPIs of cubic faces, constructing a cubic field representation with color and density information at various depth levels. Furthermore, a novel sampling strategy is introduced for rendering novel views from the cubic field at both cubic and planar scales. The entire pipeline is trained using photometric loss calculated from rendered views within a self-supervised learning approach, enabling training on 360 videos without depth annotations. Experiments on both synthetic and real-world datasets demonstrate the superior performance of CUBE360 compared to prior SSL methods. We also highlight its effectiveness in downstream applications, such as VR roaming and visual effects, underscoring CUBE360's potential to enhance immersive experiences.

Path-adaptive Spatio-Temporal State Space Model for Event-based Recognition with Arbitrary Duration

Event cameras are bio-inspired sensors that capture the intensity changes asynchronously and output event streams with distinct advantages, such as high temporal resolution. To exploit event cameras for object/action recognition, existing methods predominantly sample and aggregate events in a second-level duration at every fixed temporal interval (or frequency). However, they often face difficulties in capturing the spatiotemporal relationships for longer, e.g., minute-level, events and generalizing across varying temporal frequencies. To fill the gap, we present a novel framework, dubbed PAST-SSM, exhibiting superior capacity in recognizing events with arbitrary duration (e.g., 0.1s to 4.5s) and generalizing to varying inference frequencies. Our key insight is to learn the spatiotemporal relationships from the encoded event features via the state space model (SSM) -- whose linear complexity makes it ideal for modeling high temporal resolution events with longer sequences. To achieve this goal, we first propose a Path-Adaptive Event Aggregation and Scan (PEAS) module to encode events of varying duration into features with fixed dimensions by adaptively scanning and selecting aggregated event frames. On top of PEAS, we introduce a novel Multi-faceted Selection Guiding (MSG) loss to minimize the randomness and redundancy of the encoded features. This subtly enhances the model generalization across different inference frequencies. Lastly, the SSM is employed to better learn the spatiotemporal properties from the encoded features. Moreover, we build a minute-level event-based recognition dataset, named ArDVS100, with arbitrary duration for the benefit of the community. Extensive experiments prove that our method outperforms prior arts by +3.45%, +0.38% and +8.31% on the DVS Action, SeAct and HARDVS datasets, respectively.

ExFMan: Rendering 3D Dynamic Humans with Hybrid Monocular Blurry Frames and Events

Recent years have witnessed tremendous progress in the 3D reconstruction of dynamic humans from a monocular video with the advent of neural rendering techniques. This task has a wide range of applications, including the creation of virtual characters for virtual reality (VR) environments. However, it is still challenging to reconstruct clear humans when the monocular video is affected by motion blur, particularly caused by rapid human motion (e.g., running, dancing), as often occurs in the wild. This leads to distinct inconsistency of shape and appearance for the rendered 3D humans, especially in the blurry regions with rapid motion, e.g., hands and legs. In this paper, we propose ExFMan, the first neural rendering framework that unveils the possibility of rendering high-quality humans in rapid motion with a hybrid frame-based RGB and bio-inspired event camera. The ``out-of-the-box'' insight is to leverage the high temporal information of event data in a complementary manner and adaptively reweight the effect of losses for both RGB frames and events in the local regions, according to the velocity of the rendered human. This significantly mitigates the inconsistency associated with motion blur in the RGB frames. Specifically, we first formulate a velocity field of the 3D body in the canonical space and render it to image space to identify the body parts with motion blur. We then propose two novel losses, i.e., velocity-aware photometric loss and velocity-relative event loss, to optimize the neural human for both modalities under the guidance of the estimated velocity. In addition, we incorporate novel pose regularization and alpha losses to facilitate continuous pose and clear boundary. Extensive experiments on synthetic and real-world datasets demonstrate that ExFMan can reconstruct sharper and higher quality humans.

BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance

Can we directly visualize what we imagine in our brain together with what we describe? The inherent nature of human perception reveals that, when we think, our body can combine language description and build a vivid picture in our brain. Intuitively, generative models should also hold such versatility. In this paper, we introduce BrainDreamer, a novel end-to-end language-guided generative framework that can mimic human reasoning and generate high-quality images from electroencephalogram (EEG) brain signals. Our method is superior in its capacity to eliminate the noise introduced by non-invasive EEG data acquisition and meanwhile achieve a more precise mapping between the EEG and image modality, thus leading to significantly better-generated images. Specifically, BrainDreamer consists of two key learning stages: 1) modality alignment and 2) image generation. In the alignment stage, we propose a novel mask-based triple contrastive learning strategy to effectively align EEG, text, and image embeddings to learn a unified representation. In the generation stage, we inject the EEG embeddings into the pre-trained Stable Diffusion model by designing a learnable EEG adapter to generate high-quality reasoning-coherent images. Moreover, BrainDreamer can accept textual descriptions (e.g., color, position, etc.) to achieve controllable image generation. Extensive experiments show that our method significantly outperforms prior arts in terms of generating quality and quantitative performance.

G-NeLF: Memory- and Data-Efficient Hybrid Neural Light Field for Novel View Synthesis

Following the burgeoning interest in implicit neural representation, Neural Light Field (NeLF) has been introduced to predict the color of a ray directly. Unlike Neural Radiance Field (NeRF), NeLF does not create a point-wise representation by predicting color and volume density for each point in space. However, the current NeLF methods face a challenge as they need to train a NeRF model first and then synthesize over 10K views to train NeLF for improved performance. Additionally, the rendering quality of NeLF methods is lower compared to NeRF methods. In this paper, we propose G-NeLF, a versatile grid-based NeLF approach that utilizes spatial-aware features to unleash the potential of the neural network's inference capability, and consequently overcome the difficulties of NeLF training. Specifically, we employ a spatial-aware feature sequence derived from a meticulously crafted grid as the ray's representation. Drawing from our empirical studies on the adaptability of multi-resolution hash tables, we introduce a novel grid-based ray representation for NeLF that can represent the entire space with a very limited number of parameters. To better utilize the sequence feature, we design a lightweight ray color decoder that simulates the ray propagation process, enabling a more efficient inference of the ray's color. G-NeLF can be trained without necessitating significant storage overhead and with the model size of only 0.95 MB to surpass previous state-of-the-art NeLF. Moreover, compared with grid-based NeRF methods, e.g., Instant-NGP, we only utilize one-tenth of its parameters to achieve higher performance. Our code will be released upon acceptance.

EvLight++: Low-Light Video Enhancement with an Event Camera: A Large-Scale Real-World Dataset, Novel Method, and More

Event cameras offer significant advantages for low-light video enhancement, primarily due to their high dynamic range. Current research, however, is severely limited by the absence of large-scale, real-world, and spatio-temporally aligned event-video datasets. To address this, we introduce a large-scale dataset with over 30,000 pairs of frames and events captured under varying illumination. This dataset was curated using a robotic arm that traces a consistent non-linear trajectory, achieving spatial alignment precision under 0.03mm and temporal alignment with errors under 0.01s for 90% of the dataset. Based on the dataset, we propose \textbf{EvLight++}, a novel event-guided low-light video enhancement approach designed for robust performance in real-world scenarios. Firstly, we design a multi-scale holistic fusion branch to integrate structural and textural information from both images and events. To counteract variations in regional illumination and noise, we introduce Signal-to-Noise Ratio (SNR)-guided regional feature selection, enhancing features from high SNR regions and augmenting those from low SNR regions by extracting structural information from events. To incorporate temporal information and ensure temporal coherence, we further introduce a recurrent module and temporal loss in the whole pipeline. Extensive experiments on our and the synthetic SDSD dataset demonstrate that EvLight++ significantly outperforms both single image- and video-based methods by 1.37 dB and 3.71 dB, respectively. To further explore its potential in downstream tasks like semantic segmentation and monocular depth estimation, we extend our datasets by adding pseudo segmentation and depth labels via meticulous annotation efforts with foundation models. Experiments under diverse low-light scenes show that the enhanced results achieve a 15.97% improvement in mIoU for semantic segmentation.