GenAI4ELab/papercli-papers huggingface dataset:
Producing and Leveraging Online Map Uncertainty in Trajectory Prediction
Authors: Xunjiang Gu; Guanyu Song; Igor Gilitschenski; Marco Pavone; Boris Ivanovic
Venue: CVPR 2024 (main)

Abstract: High-definition (HD) maps have played an integral role in the development of modern autonomous vehicle (AV) stacks albeit with high associated labeling and maintenance costs. As a result many recent works have proposed methods for estimating HD maps online from sensor data enabling AVs to operate outside of previously-mapped regions. However current online map estimation approaches are developed in isolation of their downstream tasks complicating their integration in AV stacks. In particular they do not produce uncertainty or confidence estimates. In this work we extend multiple state-of-the-art online map estimation methods to additionally estimate uncertainty and show how this enables more tightly integrating online mapping with trajectory forecasting. In doing so we find that incorporating uncertainty yields up to 50% faster training convergence and up to 15% better prediction performance on the real-world nuScenes driving dataset.
PerceptionGPT: Effectively Fusing Visual Perception into LLM
Authors: Renjie Pi; Lewei Yao; Jiahui Gao; Jipeng Zhang; Tong Zhang
Venue: CVPR 2024 (main)

Abstract: The integration of visual inputs with large language models (LLMs) has led to remarkable advancements in multi-modal capabilities giving rise to vision large language models (VLLMs). However effectively harnessing LLMs for intricate visual perception tasks such as detection and segmentation remains a challenge. Conventional approaches achieve this by transforming perception signals (e.g. bounding boxes segmentation masks) into sequences of discrete tokens which struggle with the precision errors and introduces further complexities for training. In this paper we present a novel end-to-end framework named PerceptionGPT which represent the perception signals using LLM's dynamic token embedding. Specifically we leverage lightweight encoders and decoders to handle the perception signals in LLM's embedding space which takes advantage of the representation power of the high-dimensional token embeddings. Our approach significantly eases the training difficulties associated with the discrete representations in prior methods. Furthermore owing to our compact representation the inference speed is also greatly boosted. Consequently PerceptionGPT enables accurate flexible and efficient handling of complex perception signals. We validate the effectiveness of our approach through extensive experiments. The results demonstrate significant improvements over previous methods with only 4% trainable parameters and less than 25% training time.
Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks Methods and Applications
Authors: Karren D. Yang; Anurag Ranjan; Jen-Hao Rick Chang; Raviteja Vemulapalli; Oncel Tuzel
Venue: CVPR 2024 (main)

Abstract: We consider the task of animating 3D facial geometry from speech signal. Existing works are primarily deterministic focusing on learning a one-to-one mapping from speech signal to 3D face meshes on small datasets with limited speakers. While these models can achieve high-quality lip articulation for speakers in the training set they are unable to capture the full and diverse distribution of 3D facial motions that accompany speech in the real world. Importantly the relationship between speech and facial motion is one-to-many containing both inter-speaker and intra-speaker variations and necessitating a probabilistic approach. In this paper we identify and address key challenges that have so far limited the development of probabilistic models: lack of datasets and metrics that are suitable for training and evaluating them as well as the difficulty of designing a model that generates diverse results while remaining faithful to a strong conditioning signal as speech. We first propose large-scale benchmark datasets and metrics suitable for probabilistic modeling. Then we demonstrate a probabilistic model that achieves both diversity and fidelity to speech outperforming other methods across the proposed benchmarks. Finally we showcase useful applications of probabilistic models trained on these large-scale datasets: we can generate diverse speech-driven 3D facial motion that matches unseen speaker styles extracted from reference clips; and our synthetic meshes can be used to improve the performance of downstream audio-visual models.
LASO: Language-guided Affordance Segmentation on 3D Object
Authors: Yicong Li; Na Zhao; Junbin Xiao; Chun Feng; Xiang Wang; Tat-seng Chua
Venue: CVPR 2024 (main)

Abstract: Segmenting affordance in 3D data is key for bridging perception and action in robots. Existing efforts mostly focus on the visual side and overlook the affordance knowledge from a semantic aspect. This oversight not only limits their generalization to unseen objects but more importantly hinders their synergy with large language models (LLMs) which are excellent task planners that can decompose an overarching command into agent-actionable instructions. With this regard we propose a novel task Language-guided Affordance Segmentation on 3D Object (LASO) which challenges a model to segment a 3D object's part relevant to a given affordance question. To facilitate the task we contribute a dataset comprising 19751 point-question pairs covering 8434 object shapes and 870 expert-crafted questions. As a pioneer solution we further propose PointRefer which highlights an adaptive fusion module to identify target affordance regions at different scales. To ensure a text-aware segmentation we adopt a set of affordance queries conditioned on linguistic cues to generate dynamic kernels. These kernels are further used to convolute with point features and generate a segmentation mask. Comprehensive experiments and analyses validate PointRefer's effectiveness. With these efforts We hope that LASO can steer the direction of 3D affordance guiding it towards enhanced integration with the evolving capabilities of LLMs.
Riemannian Multinomial Logistics Regression for SPD Neural Networks
Authors: Ziheng Chen; Yue Song; Gaowen Liu; Ramana Rao Kompella; Xiao-Jun Wu; Nicu Sebe
Venue: CVPR 2024 (main)

Abstract: Deep neural networks for learning Symmetric Positive Definite (SPD) matrices are gaining increasing attention in machine learning. Despite the significant progress most existing SPD networks use traditional Euclidean classifiers on an approximated space rather than intrinsic classifiers that accurately capture the geometry of SPD manifolds. Inspired by Hyperbolic Neural Networks (HNNs) we propose Riemannian Multinomial Logistics Regression (RMLR) for the classification layers in SPD networks. We introduce a unified framework for building Riemannian classifiers under the metrics pulled back from the Euclidean space and showcase our framework under the parameterized Log-Euclidean Metric (LEM) and Log-Cholesky Metric (LCM). Besides our framework offers a novel intrinsic explanation for the most popular LogEig classifier in existing SPD networks. The effectiveness of our method is demonstrated in three applications: radar recognition human action recognition and electroencephalography (EEG) classification. The code is available at https://github.com/GitZH-Chen/SPDMLR.git.
FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization
Authors: Jiahui Zhang; Fangneng Zhan; Muyu Xu; Shijian Lu; Eric Xing
Venue: CVPR 2024 (main)

Abstract: 3D Gaussian splatting has achieved very impressive performance in real-time novel view synthesis. However it often suffers from over-reconstruction during Gaussian densification where high-variance image regions are covered by a few large Gaussians only leading to blur and artifacts in the rendered images. We design a progressive frequency regularization (FreGS) technique to tackle the over-reconstruction issue within the frequency space. Specifically FreGS performs coarse-to-fine Gaussian densification by exploiting low-to-high frequency components that can be easily extracted with low-pass and high-pass filters in the Fourier space. By minimizing the discrepancy between the frequency spectrum of the rendered image and the corresponding ground truth it achieves high-quality Gaussian densification and alleviates the over-reconstruction of Gaussian splatting effectively. Experiments over multiple widely adopted benchmarks (e.g. Mip-NeRF360 Tanks-and-Temples and Deep Blending) show that FreGS achieves superior novel view synthesis and outperforms the state-of-the-art consistently.
Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning
Authors: Rashindrie Perera; Saman Halgamuge
Venue: CVPR 2024 (main)

Abstract: In this paper we look at cross-domain few-shot classification which presents the challenging task of learning new classes in previously unseen domains with few labelled examples. Existing methods though somewhat effective encounter several limitations which we alleviate through two significant improvements. First we introduce a lightweight parameter-efficient adaptation strategy to address overfitting associated with fine-tuning a large number of parameters on small datasets. This strategy employs a linear transformation of pre-trained features significantly reducing the trainable parameter count. Second we replace the traditional nearest centroid classifier with a discriminative sample-aware loss function enhancing the model's sensitivity to the inter- and intra-class variances within the training set for improved clustering in feature space. Empirical evaluations on the Meta-Dataset benchmark showcase that our approach not only improves accuracy up to 7.7% and 5.3% on previously seen and unseen datasets respectively but also achieves the above performance while being at least  3x more parameter-efficient than existing methods establishing a new state-of-the-art in cross-domain few-shot learning. Our code is available at https://github.com/rashindrie/DIPA.
What Sketch Explainability Really Means for Downstream Tasks?
Authors: Hmrishav Bandyopadhyay; Pinaki Nath Chowdhury; Ayan Kumar Bhunia; Aneeshan Sain; Tao Xiang; Yi-Zhe Song
Venue: CVPR 2024 (main)

Abstract: In this paper we explore the unique modality of sketch for explainability emphasising the profound impact of human strokes compared to conventional pixel-oriented studies. Beyond explanations of network behavior we discern the genuine implications of explainability across diverse downstream sketch-related tasks. We propose a lightweight and portable explainability solution -- a seamless plugin that integrates effortlessly with any pre-trained model eliminating the need for re-training. Demonstrating its adaptability we present four applications: highly studied retrieval and generation and completely novel assisted drawing and sketch adversarial attacks. The centrepiece to our solution is a stroke-level attribution map that takes different forms when linked with downstream tasks. By addressing the inherent non-differentiability of rasterisation we enable explanations at both coarse stroke level (SLA) and partial stroke level (P-SLA) each with its advantages for specific downstream tasks.
Neural Exposure Fusion for High-Dynamic Range Object Detection
Authors: Emmanuel Onzon; Maximilian Bömer; Fahim Mannan; Felix Heide
Venue: CVPR 2024 (main)

Abstract: Computer vision in unconstrained outdoor scenarios must tackle challenging high dynamic range (HDR) scenes and rapidly changing illumination conditions. Existing methods address this problem with multi-capture HDR sensors and a hardware image signal processor (ISP) that produces a single fused image as input to a downstream neural network. The output of the HDR sensor is a set of low dynamic range (LDR) exposures and the fusion in the ISP is performed in image space and typically optimized for human perception on a display. Preferring tonemapped content with smooth transition regions over detail (and noise) in the resulting image this image fusion does typically not preserve all information from the LDR exposures that may be essential for downstream computer vision tasks. In this work we depart from conventional HDR image fusion and propose a learned task-driven fusion in the feature domain. Instead of using a single companded image we introduce a novel local cross-attention fusion mechanism that exploits semantic features from all exposures learned in an end-to-end fashion with supervision from downstream detection losses. The proposed method outperforms all tested conventional HDR exposure fusion and auto-exposure methods in challenging automotive HDR scenarios.
EfficientDreamer: High-Fidelity and Robust 3D Creation via Orthogonal-view Diffusion Priors
Authors: Zhipeng Hu; Minda Zhao; Chaoyi Zhao; Xinyue Liang; Lincheng Li; Zeng Zhao; Changjie Fan; Xiaowei Zhou; Xin Yu
Venue: CVPR 2024 (main)

Abstract: While image diffusion models have made significant progress in text-driven 3D content creation they often fail to accurately capture the intended meaning of text prompts especially for view information. This limitation leads to the Janus problem where multi-faced 3D models are generated under the guidance of such diffusion models. In this paper we propose a robust high-quality 3D content generation pipeline by exploiting orthogonal-view image guidance. First we introduce a novel 2D diffusion model that generates an image consisting of four orthogonal-view sub-images based on the given text prompt. Then the 3D content is created using this diffusion model. Notably the generated orthogonal-view image provides strong geometric structure priors and thus improves 3D consistency. As a result it effectively resolves the Janus problem and significantly enhances the quality of 3D content creation. Additionally we present a 3D synthesis fusion network that can further improve the details of the generated 3D contents. Both quantitative and qualitative evaluations demonstrate that our method surpasses previous text-to-3D techniques. Project page: https://efficientdreamer.github.io.
HOIAnimator: Generating Text-prompt Human-object Animations using Novel Perceptive Diffusion Models
Authors: Wenfeng Song; Xinyu Zhang; Shuai Li; Yang Gao; Aimin Hao; Xia Hou; Chenglizhao Chen; Ning Li; Hong Qin
Venue: CVPR 2024 (main)

Abstract: To date the quest to rapidly and effectively produce human-object interaction (HOI) animations directly from textual descriptions stands at the forefront of computer vision research. The underlying challenge demands both a discriminating interpretation of language and a comprehensive physics-centric model supporting real-world dynamics. To ameliorate this paper advocates HOIAnimator a novel and interactive diffusion model with perception ability and also ingeniously crafted to revolutionize the animation of complex interactions from linguistic narratives. The effectiveness of our model is anchored in two ground-breaking innovations: (1) Our Perceptive Diffusion Models (PDM) brings together two types of models: one focused on human movements and the other on objects. This combination allows for animations where humans and objects move in concert with each other making the overall motion more realistic. Additionally we propose a Perceptive Message Passing (PMP) mechanism to enhance the communication bridging the two models ensuring that the animations are smooth and unified; (2) We devise an Interaction Contact Field (ICF) a sophisticated model that implicitly captures the essence of HOIs. Beyond mere predictive contact points the ICF assesses the proximity of human and object to their respective environment informed by a probabilistic distribution of interactions learned throughout the denoising phase. Our comprehensive evaluation showcases HOIanimator's superior ability to produce dynamic context-aware animations that surpass existing benchmarks in text-driven animation synthesis.
SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis
Authors: Ziqiao Peng; Wentao Hu; Yue Shi; Xiangyu Zhu; Xiaomei Zhang; Hao Zhao; Jun He; Hongyan Liu; Zhaoxin Fan
Venue: CVPR 2024 (main)

Abstract: Achieving high synchronization in the synthesis of realistic speech-driven talking head videos presents a significant challenge. Traditional Generative Adversarial Networks (GAN) struggle to maintain consistent facial identity while Neural Radiance Fields (NeRF) methods although they can address this issue often produce mismatched lip movements inadequate facial expressions and unstable head poses. A lifelike talking head requires synchronized coordination of subject identity lip movements facial expressions and head poses. The absence of these synchronizations is a fundamental flaw leading to unrealistic and artificial outcomes. To address the critical issue of synchronization identified as the "devil" in creating realistic talking heads we introduce SyncTalk. This NeRF-based method effectively maintains subject identity enhancing synchronization and realism in talking head synthesis. SyncTalk employs a Face-Sync Controller to align lip movements with speech and innovatively uses a 3D facial blendshape model to capture accurate facial expressions. Our HeadSync Stabilizer optimizes head poses achieving more natural head movements. The Portrait-Sync Generator restores hair details and blends the generated head with the torso for a seamless visual experience. Extensive experiments and user studies demonstrate that SyncTalk outperforms state-of-the-art methods in synchronization and realism. We recommend watching the supplementary video: https://ziqiaopeng.github.io/synctalk
SFOD: Spiking Fusion Object Detector
Authors: Yimeng Fan; Wei Zhang; Changsong Liu; Mingyang Li; Wenrui Lu
Venue: CVPR 2024 (main)

Abstract: Event cameras characterized by high temporal resolution high dynamic range low power consumption and high pixel bandwidth offer unique capabilities for object detection in specialized contexts. Despite these advantages the inherent sparsity and asynchrony of event data pose challenges to existing object detection algorithms. Spiking Neural Networks (SNNs) inspired by the way the human brain codes and processes information offer a potential solution to these difficulties. However their performance in object detection using event cameras is limited in current implementations. In this paper we propose the Spiking Fusion Object Detector (SFOD) a simple and efficient approach to SNN-based object detection. Specifically we design a Spiking Fusion Module achieving the first-time fusion of feature maps from different scales in SNNs applied to event cameras. Additionally through integrating our analysis and experiments conducted during the pretraining of the backbone network on the NCAR dataset we delve deeply into the impact of spiking decoding strategies and loss functions on model performance. Thereby we establish state-of-the-art classification results based on SNNs achieving 93.7% accuracy on the NCAR dataset. Experimental results on the GEN1 detection dataset demonstrate that the SFOD achieves a state-of-the-art mAP of 32.1% outperforming existing SNN-based approaches. Our research not only underscores the potential of SNNs in object detection with event cameras but also propels the advancement of SNNs. Code is available at https://github.com/yimeng-fan/SFOD.
Detector-Free Structure from Motion
Authors: Xingyi He; Jiaming Sun; Yifan Wang; Sida Peng; Qixing Huang; Hujun Bao; Xiaowei Zhou
Venue: CVPR 2024 (main)

Abstract: We propose a structure-from-motion framework to recover accurate camera poses and point clouds from unordered images. Traditional SfM systems typically rely on the successful detection of repeatable keypoints across multiple views as the first step which is difficult for texture-poor scenes and poor keypoint detection may break down the whole SfM system. We propose a detector-free SfM framework to draw benefits from the recent success of detector-free matchers to avoid the early determination of keypoints while solving the multi-view inconsistency issue of detector-free matchers. Specifically our framework first reconstructs a coarse SfM model from quantized detector-free matches. Then it refines the model by a novel iterative refinement pipeline which iterates between an attention-based multi-view matching module to refine feature tracks and a geometry refinement module to improve the reconstruction accuracy. Experiments demonstrate that the proposed framework outperforms existing detector-based SfM systems on common benchmark datasets. We also collect a texture-poor SfM dataset to demonstrate the capability of our framework to reconstruct texture-poor scenes. Based on this framework we take first place in Image Matching Challenge 2023.
CG-HOI: Contact-Guided 3D Human-Object Interaction Generation
Authors: Christian Diller; Angela Dai
Venue: CVPR 2024 (main)

Abstract: We propose CG-HOI the first method to address the task of generating dynamic 3D human-object interactions (HOIs) from text. We model the motion of both human and object in an interdependent fashion as semantically rich human motion rarely happens in isolation without any interactions. Our key insight is that explicitly modeling contact between the human body surface and object geometry can be used as strong proxy guidance both during training and inference. Using this guidance to bridge human and object motion enables generating more realistic and physically plausible interaction sequences where the human body and corresponding object move in a coherent manner. Our method first learns to model human motion object motion and contact in a joint diffusion process inter-correlated through cross-attention. We then leverage this learned contact for guidance during inference to synthesize realistic and coherent HOIs. Extensive evaluation shows that our joint contact-based human-object interaction approach generates realistic and physically plausible sequences and we show two applications highlighting the capabilities of our method. Conditioned on a given object trajectory we can generate the corresponding human motion without re-training demonstrating strong human-object interdependency learning. Our approach is also flexible and can be applied to static real-world 3D scene scans.
Towards Surveillance Video-and-Language Understanding: New Dataset Baselines and Challenges
Authors: Tongtong Yuan; Xuange Zhang; Kun Liu; Bo Liu; Chen Chen; Jian Jin; Zhenzhen Jiao
Venue: CVPR 2024 (main)

Abstract: Surveillance videos are important for public security. However current surveillance video tasks mainly focus on classifying and localizing anomalous events. Existing methods are limited to detecting and classifying the predefined events with unsatisfactory semantic understanding although they have obtained considerable performance. To address this issue we propose a new research direction of surveillance video-and-language understanding(VALU) and construct the first multimodal surveillance video dataset. We manually annotate the real-world surveillance dataset UCF-Crime with fine-grained event content and timing. Our newly annotated dataset UCA (UCF-Crime Annotation) contains 23542 sentences with an average length of 20 words and its annotated videos are as long as 110.7 hours. Furthermore we benchmark SOTA models for four multimodal tasks on this newly created dataset which serve as new baselines for surveillance VALU. Through experiments we find that mainstream models used in previously public datasets perform poorly on surveillance video demonstrating new challenges in surveillance VALU. We also conducted experiments on multimodal anomaly detection. These results demonstrate that our multimodal surveillance learning can improve the performance of anomaly detection. All the experiments highlight the necessity of constructing this dataset to advance surveillance AI.