pwc-archive/papers-with-abstracts huggingface dataset:
Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Control
Authors: Zhengfei Kuang, Shengqu Cai, Hao He, Yinghao Xu, Hongsheng Li, Leonidas Guibas, Gordon Wetzstein
Date: 2024-05-27 00:00:00
Tasks: Scene Generation, Video Generation, Video Synchronization

Research on video generation has recently made tremendous progress, enabling high-quality videos to be generated from text prompts or images. Adding control to the video generation process is an important goal moving forward and recent approaches that condition video generation models on camera trajectories make strides towards it. Yet, it remains challenging to generate a video of the same scene from multiple different camera trajectories. Solutions to this multi-video generation problem could enable large-scale 3D scene generation with editable camera trajectories, among other applications. We introduce collaborative video diffusion (CVD) as an important step towards this vision. The CVD framework includes a novel cross-video synchronization module that promotes consistency between corresponding frames of the same video rendered from different camera poses using an epipolar attention mechanism. Trained on top of a state-of-the-art camera-control module for video generation, CVD generates multiple videos rendered from different camera trajectories with significantly better consistency than baselines, as shown in extensive experiments. Project page: https://collaborativevideodiffusion.github.io/.
Human4DiT: 360-degree Human Video Generation with 4D Diffusion Transformer
Authors: Ruizhi Shao, Youxin Pang, Zerong Zheng, Jingxiang Sun, Yebin Liu
Date: 2024-05-27 00:00:00
Tasks: Video Generation

We present a novel approach for generating 360-degree high-quality, spatio-temporally coherent human videos from a single image. Our framework combines the strengths of diffusion transformers for capturing global correlations across viewpoints and time, and CNNs for accurate condition injection. The core is a hierarchical 4D transformer architecture that factorizes self-attention across views, time steps, and spatial dimensions, enabling efficient modeling of the 4D space. Precise conditioning is achieved by injecting human identity, camera parameters, and temporal signals into the respective transformers. To train this model, we collect a multi-dimensional dataset spanning images, videos, multi-view data, and limited 4D footage, along with a tailored multi-dimensional training strategy. Our approach overcomes the limitations of previous methods based on generative adversarial networks or vanilla diffusion models, which struggle with complex motions, viewpoint changes, and generalization. Through extensive experiments, we demonstrate our method's ability to synthesize 360-degree realistic, coherent human motion videos, paving the way for advanced multimedia applications in areas such as virtual reality and animation.
RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control
Authors: Litu Rout, Yujia Chen, Nataniel Ruiz, Abhishek Kumar, Constantine Caramanis, Sanjay Shakkottai, Wen-Sheng Chu
Date: 2024-05-27 00:00:00

We propose Reference-Based Modulation (RB-Modulation), a new plug-and-play solution for training-free personalization of diffusion models. Existing training-free approaches exhibit difficulties in (a) style extraction from reference images in the absence of additional style or content text descriptions, (b) unwanted content leakage from reference style images, and (c) effective composition of style and content. RB-Modulation is built on a novel stochastic optimal controller where a style descriptor encodes the desired attributes through a terminal cost. The resulting drift not only overcomes the difficulties above, but also ensures high fidelity to the reference style and adheres to the given text prompt. We also introduce a cross-attention-based feature aggregation scheme that allows RB-Modulation to decouple content and style from the reference image. With theoretical justification and empirical evidence, our framework demonstrates precise extraction and control of content and style in a training-free manner. Further, our method allows a seamless composition of content and style, which marks a departure from the dependency on external adapters or ControlNets.
Occlusion Handling in 3D Human Pose Estimation with Perturbed Positional Encoding
Authors: Niloofar Azizi, Mohsen Fayyaz, Horst Bischof
Date: 2024-05-27 00:00:00
Tasks: 3D Human Pose Estimation, Occlusion Handling, Pose Estimation

Understanding human behavior fundamentally relies on accurate 3D human pose estimation. Graph Convolutional Networks (GCNs) have recently shown promising advancements, delivering state-of-the-art performance with rather lightweight architectures. In the context of graph-structured data, leveraging the eigenvectors of the graph Laplacian matrix for positional encoding is effective. Yet, the approach does not specify how to handle scenarios where edges in the input graph are missing. To this end, we propose a novel positional encoding technique, PerturbPE, that extracts consistent and regular components from the eigenbasis. Our method involves applying multiple perturbations and taking their average to extract the consistent and regular component from the eigenbasis. PerturbPE leverages the Rayleigh-Schrodinger Perturbation Theorem (RSPT) for calculating the perturbed eigenvectors. Employing this labeling technique enhances the robustness and generalizability of the model. Our results support our theoretical findings, e.g. our experimental analysis observed a performance enhancement of up to $12\%$ on the Human3.6M dataset in instances where occlusion resulted in the absence of one edge. Furthermore, our novel approach significantly enhances performance in scenarios where two edges are missing, setting a new benchmark for state-of-the-art.
EASI-Tex: Edge-Aware Mesh Texturing from Single Image
Authors: Sai Raj Kishore Perla, Yizhi Wang, Ali Mahdavi-Amiri, Hao Zhang
Date: 2024-05-27 00:00:00

We present a novel approach for single-image mesh texturing, which employs a diffusion model with judicious conditioning to seamlessly transfer an object's texture from a single RGB image to a given 3D mesh object. We do not assume that the two objects belong to the same category, and even if they do, there can be significant discrepancies in their geometry and part proportions. Our method aims to rectify the discrepancies by conditioning a pre-trained Stable Diffusion generator with edges describing the mesh through ControlNet, and features extracted from the input image using IP-Adapter to generate textures that respect the underlying geometry of the mesh and the input texture without any optimization or training. We also introduce Image Inversion, a novel technique to quickly personalize the diffusion model for a single concept using a single image, for cases where the pre-trained IP-Adapter falls short in capturing all the details from the input image faithfully. Experimental results demonstrate the efficiency and effectiveness of our edge-aware single-image mesh texturing approach, coined EASI-Tex, in preserving the details of the input texture on diverse 3D objects, while respecting their geometry.
Predict joint angle of body parts based on sequence pattern recognition
Authors: Amin Ahmadi Kasani, Hedieh Sajedi
Date: 2024-05-27 00:00:00

The way organs are positioned and moved in the workplace can cause pain and physical harm. Therefore, ergonomists use ergonomic risk assessments based on visual observation of the workplace, or review pictures and videos taken in the workplace. Sometimes the workers in the photos are not in perfect condition. Some parts of the workers' bodies may not be in the camera's field of view, could be obscured by objects, or by self-occlusion, this is the main problem in 2D human posture recognition. It is difficult to predict the position of body parts when they are not visible in the image, and geometric mathematical methods are not entirely suitable for this purpose. Therefore, we created a dataset with artificial images of a 3D human model, specifically for painful postures, and real human photos from different viewpoints. Each image we captured was based on a predefined joint angle for each 3D model or human model. We created various images, including images where some body parts are not visible. Nevertheless, the joint angle is estimated beforehand, so we could study the case by converting the input images into the sequence of joint connections between predefined body parts and extracting the desired joint angle with a convolutional neural network. In the end, we obtained root mean square error (RMSE) of 12.89 and mean absolute error (MAE) of 4.7 on the test dataset.
Fusing uncalibrated IMUs and handheld smartphone video to reconstruct knee kinematics
Authors: J. D. Peiffer, Kunal Shah, Shawana Anarwala, Kayan Abdou, R. James Cotton
Date: 2024-05-27 00:00:00
Tasks: Markerless Motion Capture, Sensor Fusion

Video and wearable sensor data provide complementary information about human movement. Video provides a holistic understanding of the entire body in the world while wearable sensors provide high-resolution measurements of specific body segments. A robust method to fuse these modalities and obtain biomechanically accurate kinematics would have substantial utility for clinical assessment and monitoring. While multiple video-sensor fusion methods exist, most assume that a time-intensive, and often brittle, sensor-body calibration process has already been performed. In this work, we present a method to combine handheld smartphone video and uncalibrated wearable sensor data at their full temporal resolution. Our monocular, video-only, biomechanical reconstruction already performs well, with only several degrees of error at the knee during walking compared to markerless motion capture. Reconstructing from a fusion of video and wearable sensor data further reduces this error. We validate this in a mixture of people with no gait impairments, lower limb prosthesis users, and individuals with a history of stroke. We also show that sensor data allows tracking through periods of visual occlusion.
DOF-GS: Adjustable Depth-of-Field 3D Gaussian Splatting for Post-Capture Refocusing, Defocus Rendering and Blur Removal
Authors: Yujie Wang, PRANEETH CHAKRAVARTHULA, Baoquan Chen
Date: 2024-05-27 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3DGS, 3D Scene Reconstruction, Novel View Synthesis

3D Gaussian Splatting (3DGS) techniques have recently enabled high-quality 3D scene reconstruction and real-time novel view synthesis. These approaches, however, are limited by the pinhole camera model and lack effective modeling of defocus effects. Departing from this, we introduce DOF-GS--a new 3DGS-based framework with a finite-aperture camera model and explicit, differentiable defocus rendering, enabling it to function as a post-capture control tool. By training with multi-view images with moderate defocus blur, DOF-GS learns inherent camera characteristics and reconstructs sharp details of the underlying scene, particularly, enabling rendering of varying DOF effects through on-demand aperture and focal distance control, post-capture and optimization. Additionally, our framework extracts circle-of-confusion cues during optimization to identify in-focus regions in input views, enhancing the reconstructed 3D scene details. Experimental results demonstrate that DOF-GS supports post-capture refocusing, adjustable defocus and high-quality all-in-focus rendering, from multi-view images with uncalibrated defocus blur.
Tracking Small Birds by Detection Candidate Region Filtering and Detection History-aware Association
Authors: TingWei Liu, Yasutomo Kawanishi, Takahiro Komamizu, Ichiro Ide
Date: 2024-05-27 00:00:00
Tasks: Object, object-detection, Object Detection, Object Tracking

This paper focuses on tracking birds that appear small in a panoramic video. When the size of the tracked object is small in the image (small object tracking) and move quickly, object detection and association suffers. To address these problems, we propose Adaptive Slicing Aided Hyper Inference (Adaptive SAHI), which reduces the candidate regions to apply detection, and Detection History-aware Similarity Criterion (DHSC), which accurately associates objects in consecutive frames based on the detection history. Experiments on the NUBird2022 dataset verifies the effectiveness of the proposed method by showing improvements in both accuracy and speed.
All-day Depth Completion
Authors: Vadim Ezhov, Hyoungseob Park, Zhaoyang Zhang, Rishi Upadhyay, Howard Zhang, Chethan Chinder Chandrappa, Achuta Kadambi, Yunhao Ba, Julie Dorsey, Alex Wong
Date: 2024-05-27 00:00:00
Tasks: All, Depth Completion, Depth Estimation, Sensor Fusion

We propose a method for depth estimation under different illumination conditions, i.e., day and night time. As photometry is uninformative in regions under low-illumination, we tackle the problem through a multi-sensor fusion approach, where we take as input an additional synchronized sparse point cloud (i.e., from a LiDAR) projected onto the image plane as a sparse depth map, along with a camera image. The crux of our method lies in the use of the abundantly available synthetic data to first approximate the 3D scene structure by learning a mapping from sparse to (coarse) dense depth maps along with their predictive uncertainty - we term this, SpaDe. In poorly illuminated regions where photometric intensities do not afford the inference of local shape, the coarse approximation of scene depth serves as a prior; the uncertainty map is then used with the image to guide refinement through an uncertainty-driven residual learning (URL) scheme. The resulting depth completion network leverages complementary strengths from both modalities - depth is sparse but insensitive to illumination and in metric scale, and image is dense but sensitive with scale ambiguity. SpaDe can be used in a plug-and-play fashion, which allows for 25% improvement when augmented onto existing methods to preprocess sparse depth. We demonstrate URL on the nuScenes dataset where we improve over all baselines by an average 11.65% in all-day scenarios, 11.23% when tested specifically for daytime, and 13.12% for nighttime scenes.
Controllable Longer Image Animation with Diffusion Models
Authors: Qiang Wang, Minghua Liu, Junjun Hu, Fan Jiang, Mu Xu
Date: 2024-05-27 00:00:00
Tasks: Image Animation, motion prediction, Video Generation

Generating realistic animated videos from static images is an important area of research in computer vision. Methods based on physical simulation and motion prediction have achieved notable advances, but they are often limited to specific object textures and motion trajectories, failing to exhibit highly complex environments and physical dynamics. In this paper, we introduce an open-domain controllable image animation method using motion priors with video diffusion models. Our method achieves precise control over the direction and speed of motion in the movable region by extracting the motion field information from videos and learning moving trajectories and strengths. Current pretrained video generation models are typically limited to producing very short videos, typically less than 30 frames. In contrast, we propose an efficient long-duration video generation method based on noise reschedule specifically tailored for image animation tasks, facilitating the creation of videos over 100 frames in length while maintaining consistency in content scenery and motion coordination. Specifically, we decompose the denoise process into two distinct phases: the shaping of scene contours and the refining of motion details. Then we reschedule the noise to control the generated frame sequences maintaining long-distance noise correlation. We conducted extensive experiments with 10 baselines, encompassing both commercial tools and academic methodologies, which demonstrate the superiority of our method. Our project page: https://wangqiang9.github.io/Controllable.github.io/
EF-Calib: Spatiotemporal Calibration of Event- and Frame-Based Cameras Using Continuous-Time Trajectories
Authors: Shaoan Wang, Zhanhua Xin, Yaoqing Hu, Dongyue Li, Mingzhu Zhu, Junzhi Yu
Date: 2024-05-27 00:00:00

Event camera, a bio-inspired asynchronous triggered camera, offers promising prospects for fusion with frame-based cameras owing to its low latency and high dynamic range. However, calibrating stereo vision systems that incorporate both event and frame-based cameras remains a significant challenge. In this letter, we present EF-Calib, a spatiotemporal calibration framework for event- and frame-based cameras using continuous-time trajectories. A novel calibration pattern applicable to both camera types and the corresponding event recognition algorithm is proposed. Leveraging the asynchronous nature of events, a derivable piece-wise B-spline to represent camera pose continuously is introduced, enabling calibration for intrinsic parameters, extrinsic parameters, and time offset, with analytical Jacobians provided. Various experiments are carried out to evaluate the calibration performance of EF-Calib, including calibration experiments for intrinsic parameters, extrinsic parameters, and time offset. Experimental results show that EF-Calib achieves the most accurate intrinsic parameters compared to current SOTA, the close accuracy of the extrinsic parameters compared to the frame-based results, and accurate time offset estimation. EF-Calib provides a convenient and accurate toolbox for calibrating the system that fuses events and frames. The code of this paper will also be open-sourced at: https://github.com/wsakobe/EF-Calib.
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
Authors: Yuting Ma, Lechao Cheng, Yaxiong Wang, Zhun Zhong, Xiaohua Xu, Meng Wang
Date: 2024-05-27 00:00:00
Tasks: Federated Learning, Privacy Preserving

Federated learning (FL) is a popular privacy-preserving paradigm that enables distributed clients to collaboratively train models with a central server while keeping raw data locally. In practice, distinct model architectures, varying data distributions, and limited resources across local clients inevitably cause model performance degradation and a slowdown in convergence speed. However, existing FL methods can only solve some of the above heterogeneous challenges and have obvious performance limitations. Notably, a unified framework has not yet been explored to overcome these challenges. Accordingly, we propose FedHPL, a parameter-efficient unified $\textbf{Fed}$erated learning framework for $\textbf{H}$eterogeneous settings based on $\textbf{P}$rompt tuning and $\textbf{L}$ogit distillation. Specifically, we employ a local prompt tuning scheme that leverages a few learnable visual prompts to efficiently fine-tune the frozen pre-trained foundation model for downstream tasks, thereby accelerating training and improving model performance under limited local resources and data heterogeneity. Moreover, we design a global logit distillation scheme to handle the model heterogeneity and guide the local training. In detail, we leverage logits to implicitly capture local knowledge and design a weighted knowledge aggregation mechanism to generate global client-specific logits. We provide a theoretical guarantee on the generalization error bound for FedHPL. The experiments on various benchmark datasets under diverse settings of models and data demonstrate that our framework outperforms state-of-the-art FL approaches, with less computation overhead and training rounds.
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction
Authors: ShengJie Liu, Lu Zhang
Date: 2024-05-27 00:00:00
Tasks: Gaussian Processes, Representation Learning

Remote sensing data provide a low-cost solution for large-scale monitoring of air pollution via the retrieval of aerosol optical depth (AOD), but is often limited by cloud contamination. Existing methods for AOD reconstruction rely on temporal information. However, for remote sensing data at high spatial resolution, multi-temporal observations are often unavailable. In this letter, we take advantage of deep representation learning from convolutional neural networks and propose Deep Feature Gaussian Processes (DFGP) for single-scene AOD reconstruction. By using deep learning, we transform the variables to a feature space with better explainable power. By using Gaussian processes, we explicitly consider the correlation between observed AOD and missing AOD in spatial and feature domains. Experiments on two AOD datasets with real-world cloud patterns showed that the proposed method outperformed deep CNN and random forest, achieving R$^2$ of 0.7431 on MODIS AOD and R$^2$ of 0.9211 on EMIT AOD, compared to deep CNN's R$^2$ of 0.6507 and R$^2$ of 0.8619. The proposed methods increased R$^2$ by over 0.35 compared to the popular random forest in AOD reconstruction. The data and code used in this study are available at \url{https://skrisliu.com/dfgp}.
Does Diffusion Beat GAN in Image Super Resolution?
Authors: Denis Kuznedelev, Valerii Startsev, Daniil Shlenskii, Sergey Kastryulin
Date: 2024-05-27 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

There is a prevalent opinion that diffusion-based models outperform GAN-based counterparts in the Image Super Resolution (ISR) problem. However, in most studies, diffusion-based ISR models employ larger networks and are trained longer than the GAN baselines. This raises the question of whether the high performance stems from the superiority of the diffusion paradigm or if it is a consequence of the increased scale and the greater computational resources of the contemporary studies. In our work, we thoroughly compare diffusion-based and GAN-based Super Resolution models under controlled settings, with both approaches having matched architecture, model and dataset sizes, and computational budget. We show that a GAN-based model can achieve results comparable or superior to a diffusion-based model. Additionally, we explore the impact of popular design choices, such as text conditioning and augmentation on the performance of ISR models, showcasing their effect in several downstream tasks. We will release the inference code and weights of our scaled GAN.
Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogates
Authors: Yoeri Poels, Koen Minartz, Harshit Bansal, Vlado Menkovski
Date: 2024-05-27 00:00:00

Simulation is a powerful tool to better understand physical systems, but generally requires computationally expensive numerical methods. Downstream applications of such simulations can become computationally infeasible if they require many forward solves, for example in the case of inverse design with many degrees of freedom. In this work, we investigate and extend neural PDE solvers as a tool to aid in scaling simulations for two-phase flow problems, and simulations of oil expulsion from a pore specifically. We extend existing numerical methods for this problem to a more complex setting involving varying geometries of the domain to generate a challenging dataset. Further, we investigate three prominent neural PDE solver methods, namely the UNet, DRN, and U-FNO, and extend them for characteristics of the oil-expulsion problem: (1) spatial conditioning on the geometry; (2) periodicity in the boundary; (3) approximate mass conservation. We scale all methods and benchmark their speed-accuracy trade-off, evaluate qualitative properties, and perform an ablation study. We find that the investigated methods can accurately model the droplet dynamics with up to three orders of magnitude speed-up, that our extensions improve performance over the baselines, and that the introduced varying geometries constitute a significantly more challenging setting over the previously considered oil expulsion problem.