GenAI4ELab/papercli-papers huggingface dataset:
From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations
Authors: Evonne Ng; Javier Romero; Timur Bagautdinov; Shaojie Bai; Trevor Darrell; Angjoo Kanazawa; Alexander Richard
Venue: CVPR 2024 (main)

Abstract: We present a framework for generating full-bodied photorealistic avatars that gesture according to the conversational dynamics of a dyadic interaction. Given speech audio we output multiple possibilities of gestural motion for an individual including face body and hands. The key behind our method is in combining the benefits of sample diversity from vector quantization with the high-frequency details obtained through diffusion to generate more dynamic expressive motion. We visualize the generated motion using highly photorealistic avatars that can express crucial nuances in gestures (e.g. sneers and smirks). To facilitate this line of research we introduce a first-of-its-kind multi-view conversational dataset that allows for photorealistic reconstruction. Experiments show our model generates appropriate and diverse gestures outperforming both diffusion- and VQ-only methods. Furthermore our perceptual evaluation highlights the importance of photorealism (vs. meshes) in accurately assessing subtle motion details in conversational gestures. Code and dataset available on project page.
Single-View Scene Point Cloud Human Grasp Generation
Authors: Yan-Kang Wang; Chengyi Xing; Yi-Lin Wei; Xiao-Ming Wu; Wei-Shi Zheng
Venue: CVPR 2024 (main)

Abstract: In this work we explore a novel task of generating human grasps based on single-view scene point clouds which more accurately mirrors the typical real-world situation of observing objects from a single viewpoint. Due to the incompleteness of object point clouds and the presence of numerous scene points the generated hand is prone to penetrating into the invisible parts of the object and the model is easily affected by scene points. Thus we introduce S2HGrasp a framework composed of two key modules: the Global Perception module that globally perceives partial object point clouds and the DiffuGrasp module designed to generate high-quality human grasps based on complex inputs that include scene points. Additionally we introduce S2HGD dataset which comprises approximately 99000 single-object single-view scene point clouds of 1668 unique objects each annotated with one human grasp. Our extensive experiments demonstrate that S2HGrasp can not only generate natural human grasps regardless of scene points but also effectively prevent penetration between the hand and invisible parts of the object. Moreover our model showcases strong generalization capability when applied to unseen objects. Our code and dataset are available at https://github.com/iSEE-Laboratory/S2HGrasp.
One-step Diffusion with Distribution Matching Distillation
Authors: Tianwei Yin; Michaël Gharbi; Richard Zhang; Eli Shechtman; Frédo Durand; William T. Freeman; Taesung Park
Venue: CVPR 2024 (main)

Abstract: Diffusion models generate high-quality images but require dozens of forward passes. We introduce Distribution Matching Distillation (DMD) a procedure to transform a diffusion model into a one-step image generator with minimal impact on image quality. We enforce the one-step image generator match the diffusion model at distribution level by minimizing an approximate KL divergence whose gradient can be expressed as the difference between 2 score functions one of the target distribution and the other of the synthetic distribution being produced by our one-step generator. The score functions are parameterized as two diffusion models trained separately on each distribution. Combined with a simple regression loss matching the large-scale structure of the multi-step diffusion outputs our method outperforms all published few-step diffusion approaches reaching 2.62 FID on ImageNet 64x64 and 11.49 FID on zero-shot COCO-30k comparable to Stable Diffusion but orders of magnitude faster. Utilizing FP16 inference our model can generate images at 20 FPS on modern hardware.
Cyclic Learning for Binaural Audio Generation and Localization
Authors: Zhaojian Li; Bin Zhao; Yuan Yuan
Venue: CVPR 2024 (main)

Abstract: Binaural audio is obtained by simulating the biological structure of human ears which plays an important role in artificial immersive spaces. A promising approach is to utilize mono audio and corresponding vision to synthesize binaural audio thereby avoiding expensive binaural audio recording. However most existing methods directly use the entire scene as a guide ignoring the correspondence between sounds and sounding objects. In this paper we advocate generating binaural audio using fine-grained raw waveform and object-level visual information as guidance. Specifically we propose a Cyclic Locating-and-UPmixing (CLUP) framework that jointly learns visual sounding object localization and binaural audio generation. Visual sounding object localization establishes the correspondence between specific visual objects and sound modalities which provides object-aware guidance to improve binaural generation performance. Meanwhile the spatial information contained in the generated binaural audio can further improve the performance of sounding object localization. In this case visual sounding object localization and binaural audio generation can achieve cyclic learning and benefit from each other. Experimental results demonstrate that on the FAIR-Play benchmark dataset our method is significantly ahead of the existing baselines in multiple evaluation metrics (STFT\downarrow: 0.787 vs. 0.851 ENV\downarrow: 0.128 vs. 0.134 WAV\downarrow: 5.244 vs. 5.684 SNR\uparrow: 7.546 vs. 7.044).
Neighbor Relations Matter in Video Scene Detection
Authors: Jiawei Tan; Hongxing Wang; Jiaxin Li; Zhilong Ou; Zhangbin Qian
Venue: CVPR 2024 (main)

Abstract: Video scene detection aims to temporally link shots for obtaining semantically compact scenes. It is essential for this task to capture scene-distinguishable affinity among shots by similarity assessment. However most methods relies on ordinary shot-to-shot similarities which may inveigle similar shots into being linked even though they are from different scenes and meanwhile hinder dissimilar shots from being blended into a complete scene. In this paper we propose NeighborNet to inject shot contexts into shot-to-shot similarities through carefully exploring the relations between semantic/temporal neighbors of shots over a local time period. In this way shot-to-shot similarities are remeasured as semantic/temporal neighbor-aware similarities so that NeighborNet can learn context embedding into shot features using graph convolutional network. As a result not only do the learned shot features suppress the affinity among similar shots from different scenes but they also promote the affinity among dissimilar shots in the same scene. Experimental results on public benchmark datasets show that our proposed NeighborNet yields substantial improvements in video scene detection especially outperforms released state-of-the-arts by at least 6% in Average Precision (AP). The code is available at https://github.com/ExMorgan-Alter/NeighborNet.
Rethinking Human Motion Prediction with Symplectic Integral
Authors: Haipeng Chen; Kedi Lyu; Zhenguang Liu; Yifang Yin; Xun Yang; Yingda Lyu
Venue: CVPR 2024 (main)

Abstract: Long-term and accurate forecasting is the long-standing pursuit of the human motion prediction task. Existing methods typically suffer from dramatic degradation in prediction accuracy with the increasing prediction horizon. It comes down to two reasons:1? Insufficient numerical stability.Unforeseen high noise and complex feature relationships in the data. 2? Inadequate modeling stability. Unreasonable step sizes and undesirable parameter updates in the prediction.In this paper we design a novel and symplectic integral-inspired framework named symplectic integral neural network (SINN) which engages symplectic trajectories to optimize the pose representation and employs a stable symplectic operator to alternately model the dynamic context. Specifically we design a Symplectic Representation Encoder that performs on enhanced human pose representation to obtain trajectories on the symplectic manifold ensuring numerical stability based on Hamiltonian mechanics and symplectic spatial splitting algorithm. We further present the Symplectic Temporal Aggregation module in the light of the symplectic temporal splitting algorithm which splits the long-term prediction into multiple accurate short-term predictions generated by a symplectic operator to secure modeling stability. Moreover our approach is model-agnostic and can be efficiently integrated with different physical dynamics models.The experimental results demonstrate that our method achieves the new state-of-the-art outperforming existing methods by large margins:20.1%on Human3.6M16.7%on CUM Mocap and 10.2% on 3DPW.
Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers
Authors: Subhadeep Koley; Ayan Kumar Bhunia; Aneeshan Sain; Pinaki Nath Chowdhury; Tao Xiang; Yi-Zhe Song
Venue: CVPR 2024 (main)

Abstract: This paper for the first time explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR). We highlight a pivotal discovery: the capacity of text-to-image diffusion models to seamlessly bridge the gap between sketches and photos. This proficiency is underpinned by their robust cross-modal capabilities and shape bias findings that are substantiated through our pilot studies. In order to harness pre-trained diffusion models effectively we introduce a straightforward yet powerful strategy focused on two key aspects: selecting optimal feature layers and utilising visual and textual prompts. For the former we identify which layers are most enriched with information and are best suited for the specific retrieval requirements (category-level or fine-grained). Then we employ visual and textual prompts to guide the model's feature extraction process enabling it to generate more discriminative and contextually relevant cross-modal representations. Extensive experiments on several benchmark datasets validate significant performance improvements.
Mudslide: A Universal Nuclear Instance Segmentation Method
Authors: Jun Wang
Venue: CVPR 2024 (main)

Abstract: Nuclear instance segmentation has played a critical role in pathology image analysis. The main challenges arise from the difficulty in accurately segmenting densely overlapping instances and the high cost of precise mask-level annotations. Existing fully-supervised nuclear instance segmentation methods such as boundary-based methods struggle to capture differences between overlapping instances and thus fail in densely distributed blurry regions. They also face challenges transitioning to point supervision where annotations are simple and effective. Inspired by natural mudslides we propose a universal method called Mudslide that uses simple representations to characterize differences between different instances and can easily be extended from fully-supervised to point-supervised. oncretely we introduce a collapse field and leverage it to construct a force map and initial boundary enabling a distinctive representation for each instance. Each pixel is assigned a collapse force with distinct directions between adjacent instances. Starting from the initial boundary Mudslide executes a pixel-by-pixel collapse along various force directions. Pixels that collapse into the same region are considered as one instance concurrently accounting for both inter-instance distinctions and intra-instance coherence. Experiments on public datasets show superior performance in both fully-supervised and point-supervised tasks.
CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement
Authors: Qiang Zhu; Jinhua Hao; Yukang Ding; Yu Liu; Qiao Mo; Ming Sun; Chao Zhou; Shuyuan Zhu
Venue: CVPR 2024 (main)

Abstract: Recently numerous approaches have achieved notable success in compressed video quality enhancement (VQE). However these methods usually ignore the utilization of valuable coding priors inherently embedded in compressed videos such as motion vectors and residual frames which carry abundant temporal and spatial information. To remedy this problem we propose the Coding Priors-Guided Aggregation (CPGA) network to utilize temporal and spatial information from coding priors. The CPGA mainly consists of an inter-frame temporal aggregation (ITA) module and a multi-scale non-local aggregation (MNA) module. Specifically the ITA module aggregates temporal information from consecutive frames and coding priors while the MNA module globally captures spatial information guided by residual frames. In addition to facilitate research in VQE task we newly construct the Video Coding Priors (VCP) dataset comprising 300 videos with various coding priors extracted from corresponding bitstreams. It remedies the shortage of previous datasets on the lack of coding information. Experimental results demonstrate the superiority of our method compared to existing state-of-the-art methods. The code and dataset will be released at https://github.com/VQE-CPGA/CPGA.
MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation
Authors: Yanhui Wang; Jianmin Bao; Wenming Weng; Ruoyu Feng; Dacheng Yin; Tao Yang; Jingxu Zhang; Qi Dai; Zhiyuan Zhao; Chunyu Wang; Kai Qiu; Yuhui Yuan; Xiaoyan Sun; Chong Luo; Baining Guo
Venue: CVPR 2024 (main)

Abstract: We present MicroCinema a straightforward yet effective framework for high-quality and coherent text-to-video generation. Unlike existing approaches that align text prompts with video directly MicroCinema introduces a Divide-and-Conquer strategy which divides the text-to-video into a two-stage process: text-to-image generation and image&text-to-video generation. This strategy offers two significant advantages. a) It allows us to take full advantage of the recent advances in text-to-image models such as Stable Diffusion Midjourney and DALLE to generate photorealistic and highly detailed images. b) Leveraging the generated image the model can allocate less focus to fine-grained appearance details prioritizing the efficient learning of motion dynamics. To implement this strategy effectively we introduce two core designs. First we propose the Appearance Injection Network enhancing the preservation of the appearance of the given image. Second we introduce the Appearance Noise Prior a novel mechanism aimed at maintaining the capabilities of pre-trained 2D diffusion models. These design elements empower MicroCinema to generate high-quality videos with precise motion guided by the provided text prompts. Extensive experiments demonstrate the superiority of the proposed framework. Concretely MicroCinema achieves SOTA zero-shot FVD of 342.86 on UCF-101 and 377.40 on MSR-VTT.
Learning Instance-Aware Correspondences for Robust Multi-Instance Point Cloud Registration in Cluttered Scenes
Authors: Zhiyuan Yu; Zheng Qin; Lintao Zheng; Kai Xu
Venue: CVPR 2024 (main)

Abstract: Multi-instance point cloud registration estimates the poses of multiple instances of a model point cloud in a scene point cloud. Extracting accurate point correspondences is to the center of the problem. Existing approaches usually treat the scene point cloud as a whole overlooking the separation of instances. Therefore point features could be easily polluted by other points from the background or different instances leading to inaccurate correspondences oblivious to separate instances especially in cluttered scenes. In this work we propose MIRETR Multi-Instance REgistration TRansformer a coarse-to-fine approach to the extraction of instance-aware correspondences. At the coarse level it jointly learns instance-aware superpoint features and predicts per-instance masks. With instance masks the influence from outside of the instance being concerned is minimized such that highly reliable superpoint correspondences can be extracted. The superpoint correspondences are then extended to instance candidates at the fine level according to the instance masks. At last an efficient candidate selection and refinement algorithm is devised to obtain the final registrations. Extensive experiments on three public benchmarks demonstrate the efficacy of our approach. In particular MIRETR outperforms the state of the arts by 16.6 points on F1 score on the challenging ROBI benchmark. Code and models are available at https://github.com/zhiyuanYU134/MIRETR
Structure Matters: Tackling the Semantic Discrepancy in Diffusion Models for Image Inpainting
Authors: Haipeng Liu; Yang Wang; Biao Qian; Meng Wang; Yong Rui
Venue: CVPR 2024 (main)

Abstract: Denoising diffusion probabilistic models (DDPMs) for image inpainting aim to add the noise to the texture of the image during the forward process and recover the masked regions with the unmasked ones of the texture via the reverse denoising process. Despite the meaningful semantics generation the existing arts suffer from the semantic discrepancy between the masked and unmasked regions since the semantically dense unmasked texture fails to be completely degraded while the masked regions turn to the pure noise in diffusion process leading to the large discrepancy between them. In this paper we aim to answer how the unmasked semantics guide the texture denoising process; together with how to tackle the semantic discrepancy to facilitate the consistent and meaningful semantics generation. To this end we propose a novel structure-guided diffusion model for image inpainting named StrDiffusion to reformulate the conventional texture denoising process under the structure guidance to derive a simplified denoising objective for image inpainting while revealing: 1) the semantically sparse structure is beneficial to tackle the semantic discrepancy in the early stage while the dense texture generates the reasonable semantics in the late stage; 2) the semantics from the unmasked regions essentially offer the time-dependent structure guidance for the texture denoising process benefiting from the time-dependent sparsity of the structure semantics. For the denoising process a structure-guided neural network is trained to estimate the simplified denoising objective by exploiting the consistency of the denoised structure between masked and unmasked regions. Besides we devise an adaptive resampling strategy as a formal criterion as whether the structure is competent to guide the texture denoising process while regulate their semantic correlations. Extensive experiments validate the merits of StrDiffusion over the state-of-the-arts. Our code is available at https://github.com/htyjers/StrDiffusion.
Modeling Multimodal Social Interactions: New Challenges and Baselines with Densely Aligned Representations
Authors: Sangmin Lee; Bolin Lai; Fiona Ryan; Bikram Boote; James M. Rehg
Venue: CVPR 2024 (main)

Abstract: Understanding social interactions involving both verbal and non-verbal cues is essential for effectively interpreting social situations. However most prior works on multimodal social cues focus predominantly on single-person behaviors or rely on holistic visual representations that are not aligned to utterances in multi-party environments. Consequently they are limited in modeling the intricate dynamics of multi-party interactions. In this paper we introduce three new challenging tasks to model the fine-grained dynamics between multiple people: speaking target identification pronoun coreference resolution and mentioned player prediction. We contribute extensive data annotations to curate these new challenges in social deduction game settings. Furthermore we propose a novel multimodal baseline that leverages densely aligned language-visual representations by synchronizing visual features with their corresponding utterances. This facilitates concurrently capturing verbal and non-verbal cues pertinent to social reasoning. Experiments demonstrate the effectiveness of the proposed approach with densely aligned multimodal representations in modeling fine-grained social interactions. Project website: https://sangmin-git.github.io/projects/MMSI.
COCONut: Modernizing COCO Segmentation
Authors: Xueqing Deng; Qihang Yu; Peng Wang; Xiaohui Shen; Liang-Chieh Chen
Venue: CVPR 2024 (main)

Abstract: In recent decades the vision community has witnessed remarkable progress in visual recognition partially owing to advancements in dataset benchmarks. Notably the established COCO benchmark has propelled the development of modern detection and segmentation systems. However the COCO segmentation benchmark has seen comparatively slow improvement over the last decade. Originally equipped with coarse polygon annotations for thing instances it gradually incorporated coarse superpixel annotations for stuff regions which were subsequently heuristically amalgamated to yield panoptic segmentation annotations. These annotations executed by different groups of raters have resulted not only in coarse segmentation masks but also in inconsistencies between segmentation types. In this study we undertake a comprehensive reevaluation of the COCO segmentation annotations. By enhancing the annotation quality and expanding the dataset to encompass 383K images with more than 5.18M panoptic masks we introduce COCONut the COCO Next Universal segmenTation dataset. COCONut harmonizes segmentation annotations across semantic instance and panoptic segmentation with meticulously crafted high-quality masks and establishes a robust benchmark for all segmentation tasks. To our knowledge COCONut stands as the inaugural large-scale universal segmentation dataset verified by human raters. We anticipate that the release of COCONut will significantly contribute to the community's ability to assess the progress of novel neural networks.
Semantic Line Combination Detector
Authors: Jinwon Ko; Dongkwon Jin; Chang-Su Kim
Venue: CVPR 2024 (main)

Abstract: A novel algorithm called semantic line combination detector (SLCD) to find an optimal combination of semantic lines is proposed in this paper. It processes all lines in each line combination at once to assess the overall harmony of the lines. First we generate various line combinations from reliable lines. Second we estimate the score of each line combination and determine the best one. Experimental results demonstrate that the proposed SLCD outperforms existing semantic line detectors on various datasets. Moreover it is shown that SLCD can be applied effectively to three vision tasks of vanishing point detection symmetry axis detection and composition-based image retrieval. Our codes are available at https://github.com/Jinwon-Ko/SLCD.
Prompt-Driven Dynamic Object-Centric Learning for Single Domain Generalization
Authors: Deng Li; Aming Wu; Yaowei Wang; Yahong Han
Venue: CVPR 2024 (main)

Abstract: Single-domain generalization aims to learn a model from single source domain data attaining generalized performance on other unseen target domains. Existing works primarily focus on improving the generalization ability of static networks. However static networks are unable to dynamically adapt to the diverse variations in different image scenes leading to limited generalization capability. Different scenes exhibit varying levels of complexity and the complexity of images further varies significantly in cross-domain scenarios. In this paper we propose a dynamic object-centric perception network based on prompt learning aiming to adapt to the variations in image complexity. Specifically we propose an object-centric gating module based on prompt learning to focus attention on the object-centric features guided by the various scene prompts. Then with the object-centric gating masks the dynamic selective module dynamically selects highly correlated feature regions in both spatial and channel dimensions enabling the model to adaptively perceive object-centric relevant features thereby enhancing the generalization capability. Extensive experiments were conducted on single-domain generalization tasks in image classification and object detection. The experimental results demonstrate that our approach outperforms state-of-the-art methods which validates the effectiveness and versatility of our proposed method.