mtybilly/Instruct2DS huggingface dataset:
Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers
Authors: Chi-Pin Huang*, Kai-Po Chang, Chung-Ting Tsai, Yung-Hsuan Lai, Fu-En Yang, Yu-Chiang Frank Wang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Concept erasure in text-to-image diffusion models aims to disable pre-trained diffusion models from generating images related to a target concept. To perform reliable concept erasure, the properties of robustness and locality are desirable. The former refrains the model from producing images associated with the target concept for any paraphrased or learned prompts, while the latter preserves its ability in generating images with non-target concepts. In this paper, we propose Reliable Concept Erasing via Lightweight Erasers (Receler). It learns a lightweight Eraser to perform concept erasing while satisfying the above desirable properties through the proposed concept-localized regularization and adversarial prompt learning scheme. Experiments with various concepts verify the superiority of Receler over previous methods. Code is available at https: //github.com/jasper0314-huang/Receler."
EINet: Point Cloud Completion via Extrapolation and Interpolation
Authors: Pingping Cai*, Canyu Zhang, LINGJIA SHI, Lili Wang, Nasrin Imanpour, Song Wang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Scanned point clouds are often sparse and incomplete due to the limited field of view of sensing devices, significantly impeding the performance of downstream applications. Therefore, the task of point cloud completion is introduced to obtain a dense and complete point cloud from the incomplete input. The fundamental challenges in tackling this task involve accurately inferring the missing shapes and upsampling them to higher densities. In this paper, we propose a novel approach to address this task, which formulates the completion task as a dual problem: a feature-wise extrapolation problem where the shape features of the partial point cloud are extrapolated to outlier regions for the recovery of missing portions, and a feature-wise interpolation problem to achieve point cloud upsampling. Based on these, we propose the EINet, a new point cloud completion paradigm with a novel Extrapolation module that can predict the missing shapes for the partial point cloud and a newly designed Interpolation module to upsample the point cloud. Extensive evaluation results demonstrate that EINet achieves compelling performance compared to previous state-of-the-art methods. The code is open-sourced at https://github.com/corecai163/EINet."
Personalized Video Relighting With an At-Home Light Stage
Authors: Jun Myeong Choi*, Max Christman, Roni Sengupta
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In this paper, we develop a personalized video relighting algorithm that produces high-quality and temporally consistent relit videos under any pose, expression, and lighting condition in real-time. Existing relighting algorithms typically rely either on publicly available synthetic data, which yields poor relighting results, or on actual light stage data which is difficult to acquire. We show that by just capturing recordings of a user watching YouTube videos on a monitor we can train a personalized algorithm capable of performing high-quality relighting under any condition. Our key contribution is a novel image-based neural relighting architecture that effectively separates the intrinsic appearance features - the geometry and reflectance of the face - from the source lighting and then combines them with the target lighting to generate a relit image. This neural architecture enables smoothing of intrinsic appearance features leading to temporally stable video relighting. Both qualitative and quantitative evaluations show that our architecture improves portrait image relighting quality and temporal consistency over state-of-the-art approaches on both casually captured ‘Light Stage at Your Desk’ (LSYD) and light-stage-captured ‘One Light At a Time’ (OLAT) datasets. Source code is available at https: //github.com/chedgekorea/relighting"
Temporal Residual Guided Diffusion Framework for Event-Driven Video Reconstruction
Authors: Lin Zhu*, Yunlong Zheng, Yijun Zhang, Xiao Wang, Lizhi Wang, Hua Huang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Event-based video reconstruction has garnered increasing attention due to its advantages, such as high dynamic range and rapid motion capture capabilities. However, current methods often prioritize the extraction of temporal information from continuous event flow, leading to an overemphasis on low-frequency texture features in the scene, resulting in over-smoothing and blurry artifacts. Addressing this challenge necessitates the integration of conditional information, encompassing temporal features, low-frequency texture, and high-frequency events, to guide the Denoising Diffusion Probabilistic Model (DDPM) in producing accurate and natural outputs. To tackle this issue, we introduce a novel approach, the Temporal Residual Guided Diffusion Framework, which effectively leverages both temporal and frequency-based event priors. Our framework incorporates three key conditioning modules: a pre-trained low-frequency intensity estimation module, a temporal recurrent encoder module, and an attention-based high-frequency prior enhancement module. In order to capture temporal scene variations from the events at the current moment, we employ a temporal-domain residual image as the target for the diffusion model. Through the combination of these three conditioning paths and the temporal residual framework, our framework excels in reconstructing high-quality videos from event flow, mitigating issues such as artifacts and over-smoothing commonly observed in previous approaches. Extensive experiments conducted on multiple benchmark datasets validate the superior performance of our framework compared to prior event-based reconstruction methods."
A Secure Image Watermarking Framework with Statistical Guarantees via Adversarial Attacks on Secret Key Networks
Authors: Feiyu CHEN*, Wei Lin, Ziquan Liu, Antoni Chan
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Imperceptible watermarks are essential in safeguarding the content authenticity and the rights of creators in imagery. Recently, several leading approaches, notably zero-bit watermarking, have demonstrated impressive imperceptibility and robustness in image watermarking. However, these methods have security weaknesses, e.g., the risk of counterfeiting and the ease of erasing an existing watermark with another watermark, while also lacking a statistical guarantee regarding the detection performance. To address this issue, we propose a novel framework to train a secret key network (SKN), which serves as a non-duplicable safeguard for securing the embedded watermark. The SKN is trained so that natural images’ output obeys a standard multi-variate normal distribution. To embed a watermark, we apply an adversarial attack (a modified PGD attack) on the image such that the SKN produces a secret key signature (SKS) with a longer length. We then derive two hypothesis tests to detect the presence of the watermark in an image via the SKN response magnitude and the SKS angle, which offer a statistical guarantee of the false positive rate. Our extensive empirical study demonstrates that our framework maintains robustness comparable to existing methods and excels in security and imperceptibility."
SPIRE: Semantic Prompt-Driven Image Restoration
Authors: Chenyang QI*, Zhengzhong Tu, Keren Ye, Mauricio Delbracio, Peyman Milanfar, Qifeng Chen, Hossein Talebi
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Text-driven diffusion models have become increasingly popular for various image editing tasks, including inpainting, stylization, and object replacement. However, it still remains an open research problem to adopt this language-vision paradigm for more fine-level image processing tasks, such as denoising, super-resolution, deblurring, and compression artifact removal. In this paper, we develop , a Semantic and restoration Prompt-driven Image framework that leverages natural language as a user-friendly interface to control the image restoration process. We consider the capacity of prompt information in two dimensions. First, we use content-related prompts to enhance the semantic alignment, effectively alleviating identity ambiguity in the restoration outcomes. Second, our approach is the first framework that supports fine-level instruction through language-based quantitative specification of the restoration strength, without the need for explicit task-specific design. In addition, we introduce a novel fusion mechanism that augments the existing ControlNet architecture by learning to rescale the generative prior, thereby achieving better restoration fidelity. Our extensive experiments demonstrate the superior restoration performance of compared to the state of the arts, alongside offering the flexibility of text-based control over the restoration effects."
Free-ATM: Harnessing Free Attention Masks for Representation Learning on Diffusion-Generated Images
Authors: David Junhao Zhang*, Mutian Xu, Jay Zhangjie Wu, Chuhui Xue, Wenqing Zhang, Xiaoguang Han, Song Bai, Mike Zheng Shou*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This paper studies visual representation learning with diffusion-generated synthetic images. We start by uncovering that diffusion models’ cross-attention layers inherently provide annotation-free attention masks aligned with corresponding text inputs on generated images. We then investigate the problems of three prevalent representation learning methods (i.e., contrastive learning, masked modeling, and vision-language pretraining) on diffusion-generated synthetic data and introduce customized solutions by fully exploiting the aforementioned free attention masks, namely Free-ATM. Comprehensive experiments demonstrate Free-ATM’s ability to enhance the performance of various representation learning frameworks when utilizing synthetic data. This improvement is consistent across diverse downstream tasks including image classification, detection, segmentation and image-text retrieval. Meanwhile, by utilizing Free-ATM, we can accelerate the pretraining on synthetic images significantly and close the performance gap between representation learning on synthetic data and real-world scenarios."
HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution
Authors: XIANG ZHANG*, Yulun Zhang, Fisher Yu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Transformers have exhibited promising performance in computer vision tasks including image super-resolution (SR). However, popular transformer-based SR methods often employ window self-attention with quadratic computational complexity to window sizes, resulting in fixed small windows with limited receptive fields. In this paper, we present a general strategy to convert transformer-based SR networks to hierarchical transformers (HiT-SR), boosting SR performance with multi-scale features while maintaining an efficient design. Specifically, we first replace the commonly used fixed small windows with expanding hierarchical windows to aggregate features at different scales and establish long-range dependencies. Considering the intensive computation required for large windows, we further design a spatial-channel correlation method with linear complexity to window sizes, efficiently gathering spatial and channel information from hierarchical windows. Extensive experiments verify the effectiveness and efficiency of our HiT-SR, and our improved versions of SwinIR-Light, SwinIR-NG, and SRFormer-Light yield state-of-the-art SR results with fewer parameters, FLOPs, and faster speeds (∼ 7×)."
Audio-Synchronized Visual Animation
Authors: Lin Zhang, Shentong Mo, Yijing Zhang, Pedro Morgado*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Current visual generation methods can produce high-quality videos guided by text prompts. However, effectively controlling object dynamics remains a challenge. This work explores audio as a cue to generate temporally synchronized image animations. We introduce Audio-Synchronized Visual Animation (), a task that aims to animate a static image of an object with motions temporally guided by audio clips. To this end, we present , a dataset curated from VGGSound with videos featuring synchronized audio-visual events across 15 categories. We also present a diffusion model, , capable of generating audio-guided animations. Extensive evaluations validate as a reliable benchmark for synchronized generation and demonstrate our model’s superior performance. We further explore ’s potential in a variety of audio-synchronized generation tasks, from generating full videos without a base image to controlling object motions with various sounds. We hope our established benchmark can open new avenues for controllable visual generation."
Expressive Whole-Body 3D Gaussian Avatar
Authors: Gyeongsik Moon*, Takaaki Shiratori, Shunsuke Saito
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Facial expression and hand motions are necessary to express our emotions and interact with the world. Nevertheless, most of the 3D human avatars modeled from a casually captured video only support body motions without facial expressions and hand motions. In this work, we present ExAvatar, an expressive whole-body 3D human avatar learned from a short monocular video. We design ExAvatar as a combination of the whole-body parametric mesh model (SMPL-X) and 3D Gaussian Splatting (3DGS). The main challenges are 1) a limited diversity of facial expressions and poses in the video and 2) the absence of 3D observations, such as 3D scans and RGBD images. The limited diversity in the video makes animations with novel facial expressions and poses non-trivial. In addition, the absence of 3D observations could cause significant ambiguity in human parts that are not observed in the video, which can result in noticeable artifacts under novel motions. To address them, we introduce our hybrid representation of the mesh and 3D Gaussians. Our hybrid representation treats each 3D Gaussian as a vertex on the surface with pre-defined connectivity information (i.e., triangle faces) between them following the mesh topology of SMPL-X. It makes our ExAvatar animatable with novel facial expressions by driven by the facial expression space of SMPL-X. In addition, by using connectivity-based regularizers, we significantly reduce artifacts in novel facial expressions and poses."
Canonical Shape Projection is All You Need for 3D Few-shot Class Incremental Learning
Authors: Ali Cheraghian*, Zeeshan Hayder, Sameeea Ramasinghe, Shafin Rahman, Javad Jafaryahya, Lars Petersson, Mehrtash Harandi
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "In recent years, robust pre-trained foundation models have been successfully used in many downstream tasks. Here, we would like to use such powerful models to address the problem of few-shot class incremental learning (FSCIL) tasks on 3D point cloud objects. Our approach is to reprogram the well-known CLIP-based foundation model (trained on 2D images and text pairs) for this purpose. The CLIP model works by ingesting 2D images, so to leverage it in our context, we project the 3D object point cloud onto 2D image space to create proper depth maps. For this, prior works consider a fixed and non-trainable set of camera poses. In contrast, we propose to train the network to find a projection that best describes the object and is appropriate for extracting 2D image features from the CLIP vision encoder. Directly using the generated depth map is not suitable for the CLIP model, so we apply the model reprogramming paradigm to the depth map to augment the foreground and background to adapt it. This removes the need for modification or fine-tuning of the foundation model. In the setting we have investigated, we have limited access to data from novel classes, resulting in a problem with overfitting. Here, we address this problem via the use of a prompt engineering approach using multiple GPT-generated text descriptions. Our method, C3PR, successfully outperforms existing FSCIL methods on ModelNet, ShapeNet, ScanObjectNN, and CO3D datasets. The code is available at https://github.com/alichr/C3PR."
Controllable Human-Object Interaction Synthesis
Authors: Jiaman Li*, Alexander Clegg, Roozbeh Mottaghi, Jiajun Wu, Xavier Puig, C. Karen Liu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Synthesizing semantic-aware, long-horizon, human-object interaction is critical to simulate realistic human behaviors. In this work, we address the challenging problem of generating synchronized object motion and human motion guided by language descriptions in 3D scenes. We propose Controllable Human-Object Interaction Synthesis (CHOIS), an approach that generates object motion and human motion simultaneously using a conditional diffusion model given a language description, initial object and human states, and sparse object waypoints. Here, language descriptions inform style and intent, and waypoints, which can be effectively extracted from high-level planning, ground the motion in the scene. Naively applying a diffusion model fails to predict object motion aligned with the input waypoints; it also cannot ensure the realism of interactions that require precise hand-object and human-floor contact. To overcome these problems, we introduce an object geometry loss as additional supervision to improve the matching between generated object motion and input object waypoints; we also design guidance terms to enforce contact constraints during the sampling process of the trained diffusion model. We demonstrate that our learned interaction module can synthesize realistic human-object interactions, adhering to provided textual descriptions and sparse waypoint conditions. Additionally, our module seamlessly integrates with a path planning module, enabling the generation of long-term interactions in 3D environments. Please refer to our magentaproject page for the qualitative results."
High-Fidelity and Transferable NeRF Editing by Frequency Decomposition
Authors: Yisheng He*, Weihao Yuan*, Siyu Zhu, Zilong Dong, Liefeng Bo, Qixing Huang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This paper enables high-fidelity, transferable NeRF editing by frequency decomposition. Recent NeRF editing pipelines lift 2D stylization results to 3D scenes while suffering from blurry results, and fail to capture detailed structures caused by the inconsistency between 2D editings. Our critical insight is that low-frequency components of images are more multiview-consistent after editing compared with their high-frequency parts. Moreover, the appearance style is mainly exhibited on the low-frequency components, and the content details especially reside in high-frequency parts. This motivates us to perform editing on low-frequency components, which results in high-fidelity edited scenes. In addition, the editing is performed in the low-frequency feature space, enabling stable intensity control and novel scene transfer. Comprehensive experiments conducted on photorealistic datasets demonstrate the superior performance of high-fidelity and transferable NeRF editing. The project page is at https: //aigc3d.github.io/freditor."
DoughNet: A Visual Predictive Model for Topological Manipulation of Deformable Objects
Authors: Dominik Bauer*, Zhenjia Xu, Shuran Song
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Manipulation of elastoplastic objects like dough often involves topological changes such as splitting and merging. The ability to accurately predict these topological changes that a specific action might incur is critical for planning interactions with elastoplastic objects. We present DoughNet, a Transformer-based architecture for handling these challenges, consisting of two components. First, a denoising autoencoder represents deformable objects of varying topology as sets of latent codes. Second, a visual predictive model performs autoregressive set prediction to determine long-horizon geometrical deformation and topological changes purely in latent space. Given a partial initial state and desired manipulation trajectories, it infers all resulting object geometries and topologies at each step. Our experiments in simulated and real environments show that DoughNet is able to significantly outperform related approaches that consider deformation only as geometrical change. Our code, data and videos are available at https: //dough-net.github.io."
PAV: Personalized Head Avatar from Unstructured Video Collection
Authors: Akin Caliskan*, Berkay Kicanaoglu, Hyeongwoo Kim
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We propose PAV, Personalized Head Avatar for the synthesis of human faces under arbitrary viewpoints and facial expressions. PAV introduces a method that learns a dynamic deformable neural radiance field (NeRF), in particular from a collection of monocular talking face videos of the same character under various appearance and shape changes. Unlike existing head NeRF methods that are limited to modeling such input videos on a per-appearance basis, our method allows for learning multi-appearance NeRFs, introducing appearance embedding for each input video via learnable latent neural features attached to the underlying geometry. Furthermore, the proposed appearance-conditioned density formulation facilitates the shape variation of the character, such as facial hair and soft tissues, in the radiance field prediction. To the best of our knowledge, our approach is the first dynamic deformable NeRF framework to model appearance and shape variations in a single unified network for multi-appearances of the same subject. We demonstrate experimentally that PAV outperforms the baseline method in terms of visual rendering quality in our quantitative and qualitative studies on various subjects."
Strike a Balance in Continual Panoptic Segmentation
Authors: Jinpeng Chen, Runmin Cong*, Yuxuan Luo, Horace Ho Shing Ip, Sam Kwong*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "This study explores the emerging area of continual panoptic segmentation, highlighting three key balances. First, we introduce past-class backtrace distillation to balance the stability of existing knowledge with the adaptability to new information. This technique retraces the features associated with past classes based on the final label assignment results, performing knowledge distillation targeting these specific features from the previous model while allowing other features to flexibly adapt to new information. Additionally, we introduce a class-proportional memory strategy, which aligns the class distribution in the replay sample set with that of the historical training data. This strategy maintains a balanced class representation during replay, enhancing the utility of the limited-capacity replay sample set in recalling prior classes. Moreover, recognizing that replay samples are annotated only for the classes of their original step, we devise balanced anti-misguidance losses, which combat the impact of incomplete annotations without incurring classification bias. Building upon these innovations, we present a new method named Balanced Continual Panoptic Segmentation (BalConpas). Our evaluation on the challenging ADE20K dataset demonstrates its superior performance compared to existing state-of-the-art methods. The official code is available at https://github.com/jinpeng0528/ BalConpas"
In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation
Authors: Dahyun Kang, Minsu Cho*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We present Lazy Visual Grounding for open-vocabulary semantic segmentation, which decouples unsupervised object mask discovery from object grounding. Plenty of the previous art casts this task as pixel-to-text classification without object-level comprehension, leveraging the image-to-text classification capability of pretrained vision-and-language models. We argue that visual objects are distinguishable without the prior text information as segmentation is essentially a visual understanding task. Lazy visual grounding first discovers object masks covering an image with iterative Normalized cuts and then later assigns text on the discovered objects in a late interaction manner. Our model requires no additional training yet shows great performance on five public datasets: Pascal VOC, Pascal Context, COCO-object, COCO-stuff, and ADE 20K. Especially, the visually appealing segmentation results demonstrate the model capability to localize objects precisely."
MultiDelete for Multimodal Machine Unlearning
Authors: Jiali Cheng*, Hadi Amiri
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Machine Unlearning removes specific knowledge about training data samples from an already trained model. It has significant practical benefits, such as purging private, inaccurate, or outdated information from trained models without the need for complete re-training. Unlearning within a multimodal setting presents unique challenges due to the complex dependencies between different data modalities and the expensive cost of training on large multimodal datasets and architectures. This paper presents the first machine unlearning approach for multimodal data and models, titled , which is designed to decouple associations between unimodal data points during unlearning without losing the overall representation strength of the trained model. advocates for three key properties for effective multimodal unlearning: (a): , which effectively decouples the association between individual unimodal data points marked for deletion, rendering them as unrelated data points, (b): , which retains the multimodal representation post-unlearning, and (c): , which retains the unimodal representation post-unlearning. is efficient to train and is not constrained by using a strongly convex loss–a common restriction among existing baselines. Experiments on two architectures and four datasets, including image-text and graph-text datasets, show that gains an average improvement of 17.6 points over best performing baseline in unlearning multimodal samples, can maintain the multimodal and unimodal knowledge of the original model post unlearning, and can provide better protection to unlearned data against adversarial attacks1 . 1 Code and data is available at https://github.com/CLU-UML/MultiDelete"
Unified Local-Cloud Decision-Making via Reinforcement Learning
Authors: Kathakoli Sengupta, Zhongkai Shangguan, Sandesh Bharadwaj, Sanjay Arora, Eshed Ohn-Bar*, Renato Mancuso
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Embodied vision-based real-world systems, such as mobile robots, require a careful balance between energy consumption, compute latency, and safety constraints to optimize operation across dynamic tasks and contexts. As local computation tends to be restricted, offloading the computation, , to a remote server, can save local resources while providing access to high-quality predictions from powerful and large models. However, the resulting communication and latency overhead has led to limited usability of cloud models in dynamic, safety-critical, real-time settings. To effectively address this trade-off, we introduce UniLCD, a novel hybrid inference framework for enabling flexible local-cloud collaboration. By efficiently optimizing a flexible routing module via reinforcement learning and a suitable multi-task objective, UniLCD is specifically designed to support the multiple constraints of safety-critical end-to-end mobile systems. We validate the proposed approach using a challenging, crowded navigation task requiring frequent and timely switching between local and cloud operations. UniLCD demonstrates improved overall performance and efficiency, by over 23% compared to state-of-the-art baselines based on various split computing and early exit strategies. Our code is available at https://unilcd.github.io/."
UniTalker: Scaling up Audio-Driven 3D Facial Animation through A Unified Model
Authors: Xiangyu Fan*, Jiaqi Li, Zhiqian Lin, Weiye Xiao, Lei Yang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Audio-driven 3D facial animation aims to map input audio to realistic facial motion. Despite significant progress, limitations arise from inconsistent 3D annotations, restricting previous models to training on specific annotations and thereby constraining the training scale. In this work, we present UniTalker, a unified model featuring a multi-head architecture designed to effectively leverage datasets with varied annotations. To enhance training stability and ensure consistency among multi-head outputs, we employ three training strategies, namely, PCA, model warm-up, and pivot identity embedding. To expand the training scale and diversity, we assemble A2F-Bench, comprising five publicly available datasets and three newly curated datasets. These datasets contain a wide range of audio domains, covering multilingual speech voices and songs, thereby scaling the training data from commonly employed datasets, typically less than 1 hour, to 18.5 hours. With a single trained UniTalker model, we achieve substantial lip vertex error reductions of 9.2% for BIWI dataset and 13.7% for Vocaset. Additionally, the pre-trained UniTalker exhibits promise as the foundation model for audio-driven facial animation tasks. Fine-tuning the pre-trained UniTalker on seen datasets further enhances performance on each dataset, with an average error reduction of 6.3% on A2F-Bench. Moreover, fine-tuning UniTalker on an unseen dataset with only half the data surpasses prior state-of-the-art models trained on the full dataset. The code and dataset are available at the project page1 . 1 Homepage: https://github.com/X-niper/UniTalker"
Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation
Authors: Yuanchen Ju, Kaizhe Hu, Guowei Zhang, Gu Zhang, Mingrun Jiang, Huazhe Xu*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Enabling robotic manipulation that generalizes to out-of-distribution scenes is a crucial step toward the open-world embodied intelligence. For human beings, this ability is rooted in the understanding of semantic correspondence among different objects, which helps to naturally transfer the interaction experience of familiar objects to novel ones. Although robots lack such a reservoir of interaction experience, the vast availability of human videos on the Internet may serve as a resource, from which we extract an affordance memory of contact points. Inspired by the natural way humans think, we propose : when confronted with unfamiliar objects that require generalization, the robot can acquire affordance by retrieving objects that share visual and semantic similarities from the memory, then mapping the contact points of the retrieved objects to the new object. While such correspondence may present formidable challenges at first glance, recent research finds it naturally arises from pre-trained diffusion models, enabling affordance mapping even across disparate categories. Through the framework, robots can generalize to manipulate out-of-category objects in a zero-shot manner without any manual annotation, additional training, part segmentation, pre-coded knowledge, or viewpoint restrictions. Quantitatively, significantly enhances the accuracy of visual affordance inference by a large margin of 28.7% compared to state-of-the-art (SOTA) end-to-end affordance models. We also conduct real-world experiments of cross-category object-grasping and achieve a success rate of 85.7%, proving ’s capacity for real-world tasks."
Efficient Frequency-Domain Image Deraining with Contrastive Regularization
Authors: Ning Gao, Xingyu Jiang, Xiuhui Zhang, Yue Deng*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Most current single image-deraining (SID) methods are based on the Transformer with global modeling for high-quality reconstruction. However, their architectures only build long-range features from the spatial domain, which suffers from a significant computational burden to keep effectiveness. Besides, these methods either overlook negative sample information in training or underutilize the rain streak patterns present in the negative ones. To tackle these problems, we propose a Frequency-Aware Deraining Transformer Framework (FADformer) that fully captures frequency domain features for efficient rain removal. Specifically, we construct the FADBlock, including the Fused Fourier Convolution Mixer (FFCM) and Prior-Gated Feed-forward Network (PGFN). Unlike self-attention mechanisms, the FFCM conducts convolution operations in both spatial and frequency domains, endowing it with local-global capturing capabilities and efficiency. Simultaneously, the PGFN introduces residue channel prior in a gating manner to enhance local details and retain feature structure. Furthermore, we introduce a Frequency-domain Contrastive Regularization (FCR) during training. The FCR facilitates contrastive learning in the frequency domain and leverages rain streak patterns in negative samples to improve performance. Extensive experiments show the efficiency and effectiveness of our FADformer. The source code is available at https://github.com/deng-ai-lab/FADformer."
Stitched ViTs are Flexible Vision Backbones
Authors: Zizheng Pan*, Jing Liu, Haoyu He, Jianfei Cai, Bohan Zhuang*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Large pretrained plain vision Transformers (ViTs) have been the workhorse for many downstream tasks. However, existing works utilizing off-the-shelf ViTs are inefficient in terms of training and deployment, because adopting ViTs with individual sizes requires separate trainings and is restricted by fixed performance-efficiency trade-offs. In this paper, we are inspired by stitchable neural networks (SN-Net), which is a new framework that cheaply produces a single model that covers rich subnetworks by stitching pretrained model families, supporting diverse performance-efficiency trade-offs at runtime. Building upon this foundation, we introduce SN-Netv2, a systematically improved model stitching framework to facilitate downstream task adaptation. Specifically, we first propose a two-way stitching scheme to enlarge the stitching space. We then design a resource-constrained sampling strategy that takes into account the underlying FLOPs distributions in the space for better sampling. Finally, we observe that learning stitching layers as a low-rank update plays an essential role on downstream tasks to stabilize training and ensure a good Pareto frontier. With extensive experiments on ImageNet-1K, ADE20K, COCO-Stuff-10K and NYUv2, SN-Netv2 demonstrates superior performance over SN-Netv1 on downstream dense predictions and shows strong ability as a flexible vision backbone, achieving great advantages in both training efficiency and deployment flexibility."
TrajPrompt: Aligning Color Trajectory with Vision-Language Representations
Authors: Li-Wu Tsao*, Hao-Tang Tsui, Yu-Rou Tuan, Pei-Chi Chen, Kuan-Lin Wang, Jhih-Ciang Wu, Hong-Han Shuai*, Wen-Huang Cheng
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Cross-modal learning shows promising potential to overcome the limitations of single-modality tasks. However, without proper design for representation alignment between different data sources, the external modality cannot fully exhibit its value. For example, recent trajectory prediction approaches incorporate the Bird’s-Eye-View (BEV) scene as an additional source but do not significantly improve performance compared to single-source strategies, indicating that the BEV scene and trajectory representations are not effectively combined. To overcome this problem, we propose TrajPrompt, a prompt-based approach that seamlessly incorporates trajectory representation into the vision-language framework, CLIP, for the BEV scene understanding and future forecasting. We discover that CLIP can attend to the local area of the BEV scene by utilizing our innovative design of text prompts and colored lines. Comprehensive results demonstrate TrajPrompt’s effectiveness via outperforming the state-of-the-art trajectory predictors by a significant margin (over 35% improvement for ADE and FDE metrics on SDD and DroneCrowd dataset), using fewer learnable parameters than the previous trajectory modeling approaches with scene information included. Project page: https://trajprompt.github.io/"
SemReg: Semantics Constrained Point Cloud Registration
Authors: Sheldon Fung, Xuequan Lu*, Dasith de Silva Edirimuni, Wei Pan, Xiao Liu, HONGDONG LI
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Despite the recent success of Transformers in point cloud registration, the cross-attention mechanism, while enabling point-wise feature exchange between point clouds, suffers from redundant feature interactions among semantically unrelated regions. Additionally, recent methods rely only on 3D information to extract robust feature representations, while overlooking the rich semantic information in 2D images. In this paper, we propose SemReg, a novel 2D-3D cross-modal framework that exploits semantic information in 2D images to enhance the learning of rich and robust feature representations for point cloud registration. In particular, we design a Gaussian Mixture Semantic Prior that fuses 2D semantic features across RGB frames to reveal semantic correlations between regions across the point cloud pair. Subsequently, we propose the Semantics Guided Feature Interaction module that uses this prior to emphasize the feature interactions between the semantically similar regions while suppressing superfluous interactions during the cross-attention stage. In addition, we design a Semantics Aware Focal Loss that facilitates the learning of robust features, and a Semantics Constrained Matching module that performs matching only between the regions sharing similar semantics. We evaluate our proposed SemReg on the public indoor (3DMatch) and outdoor (KITTI) datasets, and experimental results show that it produces superior registration performance to state-of-the-art techniques. Code is available at: https://github.com/SheldonFung98/SemReg. git"
Cascade-Zero123: One Image to Highly Consistent 3D with Self-Prompted Nearby Views
Authors: Yabo Chen, Jiemin Fang, Yuyang Huang, Taoran Yi, Xiaopeng Zhang*, Lingxi Xie, Xinggang Wang, Wenrui Dai*, Hongkai Xiong, Qi Tian
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Synthesizing multi-view 3D from one single image is a significant but challenging task. Zero-1-to-3 methods have achieved great success by lifting a 2D latent diffusion model to the 3D scope. The target-view image is generated with a single-view source image and the camera pose as condition information. However, due to the high sparsity of the single input image, Zero-1-to-3 tends to produce geometry and appearance inconsistency across views, especially for complex objects. To tackle this issue, we propose to supply more condition information for the generation model but in a self-prompt way. A cascade framework is constructed with two Zero-1-to-3 models, named , which progressively extract 3D information from the source image. Specifically, several nearby views are first generated by the first model and then fed into the second-stage model along with the source image as generation conditions. With amplified self-prompted condition images, our generates more consistent novel-view images than Zero-1-to-3. Experiment results demonstrate remarkable promotion, especially for various complex and challenging scenes, involving insects, humans, transparent objects, and stacked multiple objects . More demos and code are available at https: //cascadezero123.github.io."
RoScenes: A Large-scale Multi-view 3D Dataset for Roadside Perception
Authors: Xiaosu Zhu, Hualian Sheng, Sijia Cai, Bing Deng, Shaopeng Yang, Qiao Liang, Ken Chen, Lianli Gao, Jingkuan Song*, Jieping Ye*
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "We introduce RoScenes, the largest multi-view roadside perception dataset, which aims to shed light on the development of vision-centric Bird’s Eye View (BEV) approaches for more challenging traffic scenes. The highlights of RoScenes include significantly large perception area, full scene coverage and crowded traffic. More specifically, our dataset achieves surprising 21.13M 3D annotations within 64,000 m2 . To relieve the expensive costs of roadside 3D labeling, we present a novel BEV-to-3D joint annotation pipeline to efficiently collect such a large volume of data. After that, we organize a comprehensive study for current BEV methods on RoScenes in terms of effectiveness and efficiency. Tested methods suffer from the vast perception area and variation of sensor layout across scenes, resulting in performance levels falling below expectations. To this end, we propose RoBEV that incorporates feature-guided position embedding for effective 2D-3D feature assignment. With its help, our method outperforms state-of-the-art by a large margin without extra computational overhead on validation set. Our dataset and devkit are at https://roscenes.github.io."
ReSyncer: Rewiring Style-based Generator for Unified Audio-Visually Synced Facial Performer
Authors: Jiazhi Guan*, Zhiliang Xu, Hang Zhou, Kaisiyuan Wang, Shengyi He, Zhanwang Zhang, Borong Liang, Haocheng Feng, Errui Ding, Jingtuo Liu, Jingdong Wang, Youjian Zhao, Ziwei Liu
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Lip-syncing videos with given audio is the foundation for various applications including the creation of virtual presenters or performers. While recent studies explore high-fidelity lip-sync with different techniques, their task-orientated models either require long-term videos for clip-specific training or retain visible artifacts. In this paper, we propose a unified and effective framework ReSyncer, that synchronizes generalized audio-visual facial information. The key design is revisiting and rewiring the Style-based generator to efficiently adopt 3D facial dynamics predicted by a principled style-injected Transformer. By simply re-configuring the information insertion mechanisms within the noise and style space, our framework fuses motion and appearance with unified training. Extensive experiments demonstrate that ReSyncer not only produces high-fidelity lip-synced videos according to audio, but also supports multiple appealing properties that are suitable for creating virtual presenters and performers, including fast personalized fine-tuning, video-driven lip-syncing, the transfer of speaking styles, and even face swapping."
Language-Driven Physics-Based Scene Synthesis and Editing via Feature Splatting
Authors: Ri-Zhao Qiu*, Ge Yang, Weijia Zeng, Xiaolong Wang
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Scene representations using 3D Gaussian primitives have produced excellent results in modeling the appearance of static and dynamic 3D scenes. Many graphics applications, however, demand the ability to manipulate both the appearance and the physical properties of objects. We introduce Feature Splatting, an approach that unifies physics-based dynamic scene synthesis with rich semantics from vision language foundation models that are grounded by natural language. Our first contribution is a way to distill high-quality, object-centric vision-language features into 3D Gaussians, that enables semi-automatic scene decomposition using text queries. Our second contribution is a way to synthesize physics-based dynamics from an otherwise static scene using a particle-based simulator, in which material properties are assigned automatically via text queries. We ablate key techniques used in this pipeline, to illustrate the challenge and opportunities in using feature-carrying 3D Gaussians as a unified format for appearance, geometry, material properties and semantics grounded on natural language."
AlignDiff: Aligning Diffusion Models for General Few-Shot Segmentation
Authors: Ri-Zhao Qiu*, Yu-Xiong Wang, Kris Hauser
Published in: European Conference on Computer Vision (ECCV), 2024 (Main Conference Track), 2024

Abstract: "Text-to-image diffusion models have shown remarkable success in synthesizing photo-realistic images. Apart from creative applications, can we use such models to synthesize samples that aid the few-shot training of discriminative models? In this work, we propose AlignDiff, a general framework for synthesizing training images and masks for few-shot segmentation. We identify two crucial misalignments that arise when utilizing pre-trained diffusion models in segmentation tasks, which need to be addressed to create realistic training samples and align the synthetic data distribution with the real training distribution: 1) instance-level misalignment, where generated samples of rare categories are often misaligned with target tasks) and 2) annotation-level misalignment, where diffusion models are limited to generating images without pixel-level annotations. AlignDiff overcomes both challenges by leveraging a few real samples to guide the generation, thus improving novel IoU over baseline methods in few-shot segmentation and generalized few-shot segmentation on Pascal-5i and COCO-20i by up to 80%. Notably, AlignDiff is capable of augmenting the learning of out-of-distribution uncommon categories on FSS-1000, while naı̈ve diffusion model generates samples that diminish segmentation performance."