GenAI4ELab/papercli-papers huggingface dataset:
DreamDrone: Text-to-Image Diffusion Models are Zero-shot Perpetual View Generators
Authors: Hanyang Kong; Dongze Lian; Michael Bi Mi; Xinchao Wang
Venue: ECCV 2024 (main)

Abstract: "We introduce DreamDrone, a novel zero-shot and training-free pipeline for generating unbounded flythrough scenes from textual prompts. Different from other methods that focus on warping images frame by frame, we advocate explicitly warping the intermediate latent code of the pre-trained text-to-image diffusion model for high-quality image generation and generalization ability. To further enhance the fidelity of the generated images, we also propose a feature-correspondence-guidance diffusion process and a high-pass filtering strategy to promote geometric consistency and high-frequency detail consistency, respectively. Extensive experiments reveal that DreamDrone significantly surpasses existing methods, delivering highly authentic scene generation with exceptional visual quality, without training or fine-tuning on datasets or reconstructing 3D point clouds in advance."
Harnessing Text-to-Image Diffusion Models for Category-Agnostic Pose Estimation
Authors: Duo Peng; Zhengbo Zhang; Ping Hu; Qiuhong Ke; David Yau; Jun Liu
Venue: ECCV 2024 (main)

Abstract: "Category-Agnostic Pose Estimation (CAPE) aims to detect keypoints of an arbitrary unseen category in images, based on several provided examples of that category. This is a challenging task, as the limited data of unseen categories makes it difficult for models to generalize effectively. To address this challenge, previous methods typically train models on a set of predefined base categories with extensive annotations. In this work, we propose to harness rich knowledge in the off-the-shelf text-to-image diffusion model to effectively address CAPE, without training on carefully prepared base categories. To this end, we propose a Prompt Pose Matching (PPM) framework, which learns pseudo prompts corresponding to the keypoints in the provided few-shot examples via the text-to-image diffusion model. These learned pseudo prompts capture semantic information of keypoints, which can then be used to locate the same type of keypoints from images. We also design a Category-shared Prompt Training (CPT) scheme, to further boost our PPM’s performance. Extensive experiments demonstrate the efficacy of our approach."
SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer
Authors: Zijie Wu; Chaohui Yu; Yanqin Jiang; Chenjie Cao; Fan Wang; Xiang Bai
Venue: ECCV 2024 (main)

Abstract: "Recent advances in 2D/3D generative models enable the generation of dynamic 3D objects from a single-view video. Existing approaches utilize score distillation sampling to form the dynamic scene as dynamic NeRF or dense 3D Gaussians. However, these methods struggle to strike a balance among reference view alignment, spatio-temporal consistency, and motion fidelity under single-view conditions due to the implicit nature of NeRF or the intricate dense Gaussian motion prediction. To address these issues, this paper proposes an efficient, sparse-controlled video-to-4D framework named SC4D, that decouples motion and appearance to achieve superior video-to-4D generation. Moreover, we introduce Adaptive Gaussian (AG) initialization and Gaussian Alignment (GA) loss to mitigate shape degeneration issue, ensuring the fidelity of the learned motion and shape. Comprehensive experimental results demonstrate that our method surpasses existing methods in both quality and efficiency. In addition, facilitated by the disentangled modeling of motion and appearance of SC4D, we devise a novel application that seamlessly transfers the learned motion onto a diverse array of 4D entities according to textual descriptions."
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution Networks
Authors: Cheeun Hong; Kyoung Mu Lee
Venue: ECCV 2024 (main)

Abstract: "Although quantization has emerged as a promising approach to reducing computational complexity across various high-level vision tasks, it inevitably leads to accuracy loss in image super-resolution (SR) networks. This is due to the significantly divergent feature distributions across different channels and input images of the SR networks, which complicates the selection of a fixed quantization range. Existing works address this distribution mismatch problem by dynamically adapting quantization ranges to the varying distributions during test time. However, such a dynamic adaptation incurs additional computational costs during inference. In contrast, we propose a new quantization-aware training scheme that effectively Overcomes the Distribution Mismatch problem in SR networks without the need for dynamic adaptation. Intuitively, this mismatch can be mitigated by regularizing the distance between the feature and a fixed quantization range. However, we observe that such regularization can conflict with the reconstruction loss during training, negatively impacting SR accuracy. Therefore, we opt to regularize the mismatch only when the gradients of the regularization are aligned with those of the reconstruction loss. Additionally, we introduce a layer-wise weight clipping correction scheme to determine a more suitable quantization range for layer-wise weights. Experimental results demonstrate that our framework effectively reduces the distribution mismatch and achieves state-of-the-art performance with minimal computational overhead. Codes are available at https://github.com/Cheeun/ODM."
Large Motion Model for Unified Multi-Modal Motion Generation
Authors: Mingyuan Zhang; Daisheng Jin; Chenyang Gu; Fangzhou Hong; Zhongang Cai; Jingfang Huang; Chongzhi Zhang; Xinying Guo; Lei Yang; Ying He; Ziwei Liu
Venue: ECCV 2024 (main)

Abstract: "Human motion generation, a cornerstone technique in animation and video production, has widespread applications in various tasks like text-to-motion and music-to-dance. Previous works focus on developing specialist models tailored for each task without scalability. In this work, we present Large Motion Model (LMM), a motion-centric, multi-modal framework that unifies mainstream motion generation tasks into a generalist model. A unified motion model is appealing since it can leverage a wide range of motion data to achieve broad generalization beyond a single task. However, it is also challenging due to the heterogeneous nature of substantially different motion data and tasks. LMM tackles these challenges from three principled aspects: 1) Data: We consolidate datasets with different modalities, formats and tasks into a comprehensive yet unified motion generation dataset, MotionVerse, comprising 10 tasks, 16 datasets, a total of 320k sequences, and 100 million frames. 2) Architecture: We design an articulated attention mechanism that incorporates body part-aware modeling into Diffusion Transformer backbone. 3) Pre-Training: We propose a novel pre-training strategy for LMM, which employs variable frame rates and masking forms, to better exploit knowledge from diverse training data. Extensive experiments demonstrate that our generalist LMM achieves competitive performance across various standard motion generation tasks over state-of-the-art specialist models. Notably, LMM exhibits strong generalization capabilities and emerging properties across many unseen tasks. Additionally, our ablation studies reveal valuable insights about training and scaling up large motion models for future research."
FisherRF: Active View Selection and Mapping with Radiance Fields using Fisher Information
Authors: Wen Jiang; BOSHU LEI; Kostas Daniilidis
Venue: ECCV 2024 (main)

Abstract: "This study addresses the challenging problem of active view selection and uncertainty quantification within the domain of Radiance Fields. Neural Radiance Fields (NeRF) have greatly advanced image rendering and reconstruction, but the cost of acquiring images poses the need to select the most informative viewpoints efficiently. Existing approaches depend on modifying the model architecture or hypothetical perturbation field to indirectly approximate the model uncertainty. However, selecting views from indirect approximation does not guarantee optimal information gain for the model. By leveraging Fisher Information, we directly quantify observed information on the parameters of Radiance Fields and select candidate views by maximizing the Expected Information Gain (EIG). Our method achieves state-of-the-art results on multiple tasks, including view selection, active mapping, and uncertainty quantification, demonstrating its potential to advance the field of Radiance Fields."
Occlusion Handling in 3D Human Pose Estimation with Perturbed Positional Encoding
Authors: Niloofar Azizi; Mohsen Fayyaz; Horst Bischof
Venue: ECCV 2024 (main)

Abstract: "Understanding human behavior fundamentally relies on accurate 3D human pose estimation. Graph Convolutional Networks (GCNs) have recently shown promising advancements, delivering state-of-the-art performance with rather lightweight architectures. In the context of graph-structured data, leveraging the eigenvectors of the graph Laplacian matrix for positional encoding is effective. Yet, the approach does not specify how to handle scenarios where edges in the input graph are missing. To this end, we propose a novel positional encoding technique, PerturbPE, that extracts consistent and regular components from the eigenbasis. Our method involves applying multiple perturbations and taking their average to extract the consistent and regular component from the eigenbasis. PerturbPE leverages the Rayleigh-Schrodinger Perturbation Theorem (RSPT) for calculating the perturbed eigenvectors. Employing this labeling technique enhances the robustness and generalizability of the model. Our results support our theoretical findings, e.g. our experimental analysis observed a performance enhancement of up to 12% on the Human3.6M dataset in instances where occlusion resulted in the absence of one edge. Furthermore, our novel approach significantly enhances performance in scenarios where two edges are missing, setting a new benchmark for state-of-the-art."
Gradient-based Out-of-Distribution Detection
Authors: Taha Entesari; Sina Sharifi; Bardia Safaei; Vishal Patel; Mahyar Fazlyab
Venue: ECCV 2024 (main)

Abstract: "One of the challenges for neural networks in real-life applications is the overconfident errors these models make when the data is not from the original training distribution. Addressing this issue is known as Out-of-Distribution (OOD) detection. Many state-of-the-art OOD methods employ an auxiliary dataset as a surrogate for OOD data during training to achieve improved performance. However, these methods fail to fully exploit the local information embedded in the auxiliary dataset. In this work, we propose the idea of leveraging the information embedded in the gradient of the loss function during training to enable the network to not only learn a desired OOD score for each sample but also to exhibit similar behavior in a local neighborhood around each sample. We also develop a novel energy-based sampling method to allow the network to be exposed to more informative OOD samples during the training phase. This is especially important when the auxiliary dataset is large. We demonstrate the effectiveness of our method through extensive experiments on several OOD benchmarks, improving the existing state-of-the-art FPR95 by 4% on our ImageNet experiment. We further provide a theoretical analysis through the lens of certified robustness and Lipschitz analysis to showcase the theoretical foundation of our work. Our code is available at https://github.com/o4lc/Greg-OOD."
Event-based Mosaicing Bundle Adjustment
Authors: Shuang Guo; Guillermo Gallego
Venue: ECCV 2024 (main)

Abstract: "We tackle the problem of mosaicing bundle adjustment (i.e., simultaneous refinement of camera orientations and scene map) for a purely rotating event camera. We formulate the problem as a regularized non-linear least squares optimization. The objective function is defined using the linearized event generation model in the camera orientations and the panoramic gradient map of the scene. We show that this BA optimization has an exploitable block-diagonal sparsity structure, so that the problem can be solved efficiently. To the best of our knowledge, this is the first work to leverage such sparsity to speed up the optimization in the context of event-based cameras, without the need to convert events into image-like representations. We evaluate our method, called EMBA, on both synthetic and real-world datasets to show its effectiveness (50% photometric error decrease), yielding results of unprecedented quality. In addition, we demonstrate EMBA using high spatial resolution event cameras, yielding delicate panoramas in the wild, even without an initial map. The code is available at https: //github.com/tub-rip/emba."
ProMerge: Prompt and Merge for Unsupervised Instance Segmentation
Authors: Dylan J Li; Gyungin Shin
Venue: ECCV 2024 (main)

Abstract: "Unsupervised instance segmentation aims to segment distinct object instances in an image without relying on human-labeled data. This field has recently seen significant advancements, partly due to the strong local correspondences afforded by rich visual feature representations from self-supervised models (e.g., DINO). Recent state-of-the-art approaches use self-supervised features to represent images as graphs and solve a generalized eigenvalue system (i.e., normalized-cut) to generate foreground masks. While effective, this strategy is limited by its attendant computational demands, leading to slow inference speeds. In this paper, we propose Prompt and Merge (), which leverages self-supervised visual features to obtain initial groupings of patches and applies a strategic merging to these segments, aided by a sophisticated background-based mask pruning technique. ProMerge not only yields competitive results but also offers a significant reduction in inference time compared to state-of-the-art normalized-cut-based approaches. Furthermore, when training an object detector using our mask predictions as pseudo-labels, the resulting detector surpasses the current leading unsupervised model on various challenging instance segmentation benchmarks. Keywords: Unsupervised Instance Segmentation · Prompt and Merge"
M2D2M: Multi-Motion Generation from Text with Discrete Diffusion Models
Authors: Seunggeun Chi; Hyung-gun Chi; Hengbo Ma; Nakul Agarwal; Faizan Siddiqui; Karthik Ramani; Kwonjoon Lee
Venue: ECCV 2024 (main)

Abstract: "We introduce the Multi-Motion Discrete Diffusion Models (M2D2M), a novel approach for human motion generation from textual descriptions of multiple actions, utilizing the strengths of discrete diffusion models. This approach adeptly addresses the challenge of generating multi-motion sequences, ensuring seamless transitions of motions and coherence across a series of actions. The strength of M2D2M lies in its dynamic transition probability within the discrete diffusion model, which adapts transition probabilities based on the proximity between motion tokens, encouraging mixing between different modes. Complemented by a two-phase sampling strategy that includes independent and joint denoising steps, M2D2M effectively generates long-term, smooth, and contextually coherent human motion sequences, utilizing a model trained for single-motion generation. Extensive experiments demonstrate that M2D2M surpasses current state-of-the-art benchmarks for motion generation from text descriptions, showcasing its efficacy in interpreting language semantics and generating dynamic, realistic motions."
The Hard Positive Truth about Vision-Language Compositionality
Authors: Amita Kamath; Cheng-Yu Hsieh; Kai-Wei Chang; Ranjay Krishna
Venue: ECCV 2024 (main)

Abstract: "Several benchmarks have concluded that our best vision-language models (, CLIP) are lacking in compositionality. Given an image, these benchmarks probe a model’s ability to identify its associated caption amongst a set of compositional distractors. In response, a surge of recent proposals show improvements by finetuning CLIP with distractors as hard negatives. Our investigations reveal that these improvements have, in fact, been overstated — because existing benchmarks do not probe whether finetuned models remain invariant to hard positives. By curating an evaluation dataset with 112, 382 hard negatives and hard positives, we uncover that including hard positives decreases CLIP’s performance by 12.9%, while humans perform effortlessly at 99%. CLIP finetuned with hard negatives results in an even larger decrease, up to 38.7%. With this finding, we then produce a 1,775,259 image-text training set with both hard negative and hard positive captions. By training with both, we see improvements on existing benchmarks while simultaneously improving performance on hard positives, indicating a more robust improvement in compositionality. Our work suggests the need for future research to rigorously test and improve CLIP’s understanding of semantic relationships between related “positive” concepts."
GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editing
Authors: Jing Wu; Jia-Wang Bian; Xinghui Li; Guangrun Wang; Ian Reid; Philip Torr; Victor Adrian Prisacariu
Venue: ECCV 2024 (main)

Abstract: "We propose , a text-driven method to edit a 3D scene reconstructed by the 3D Gaussian Splatting (3DGS). Our method first renders a collection of images by using the 3DGS and edits them by using a pre-trained 2D diffusion model (ControlNet) based on the input prompt, which is then used to optimise the 3D model. Our key contribution is multi-view consistent editing, which enables editing all images together instead of iteratively editing one image while updating the 3D model as in previous works. It leads to faster editing as well as higher visual quality. This is achieved by the two terms: (a) depth-conditioned editing that enforces geometric consistency across multi-view images by leveraging naturally consistent depth maps. (b) attention-based latent code alignment that unifies the appearance of edited images by conditioning their editing to several reference views through self and cross-view attention between images’ latent representations. Experiments demonstrate that our method achieves faster editing and better visual results than previous state-of-the-art methods. Project website: https://gaussctrl.active.vision/"
Shapefusion: 3D localized human diffusion models
Authors: Rolandos Alexandros Potamias; Michael Tarasiou; Stylianos Ploumpis; Stefanos Zafeiriou
Venue: ECCV 2024 (main)

Abstract: "In the realm of 3D computer vision, parametric models have emerged as a ground-breaking methodology for the creation of realistic and expressive 3D avatars. Traditionally, they rely on Principal Component Analysis (PCA), given its ability to decompose data to an orthonormal space that maximally captures shape variations. However, due to the orthogonality constraints and the global nature of PCA’s decomposition, these models struggle to perform localized and disentangled editing of 3D shapes, which severely affects their use in applications requiring fine control such as face sculpting. In this paper, we leverage diffusion models to enable diverse and fully localized edits on 3D meshes, while completely preserving the un-edited regions. We propose an effective diffusion masking training strategy that, by design, facilitates localized manipulation of any shape region, without being limited to predefined regions or to sparse sets of predefined control vertices. Following our framework, a user can explicitly set their manipulation region of choice and define an arbitrary set of vertices as handles to edit a 3D mesh. Compared to the current state-of-the-art our method leads to more interpretable shape manipulations than methods relying on latent code state, greater localization and generation diversity while offering faster inference than optimization based approaches. Project page: https://rolpotamias.github.io/Shapefusion/."
Eta Inversion: Designing an Optimal Eta Function for Diffusion-based Real Image Editing
Authors: Wonjun Kang; Kevin Galim; Hyung Il Koo
Venue: ECCV 2024 (main)

Abstract: "Diffusion models have achieved remarkable success in the domain of text-guided image generation and, more recently, in text-guided image editing. A commonly adopted strategy for editing real images involves inverting the diffusion process to obtain a noisy representation of the original image, which is then denoised to achieve the desired edits. However, current methods for diffusion inversion often struggle to produce edits that are both faithful to the specified text prompt and closely resemble the source image. To overcome these limitations, we introduce a novel and adaptable diffusion inversion technique for real image editing, which is grounded in a theoretical analysis of the role of η in the DDIM sampling equation for enhanced editability. By designing a universal diffusion inversion method with a time- and region-dependent η function, we enable flexible control over the editing extent. Through a comprehensive series of quantitative and qualitative assessments, involving a comparison with a broad array of recent methods, we demonstrate the superiority of our approach. Our method not only sets a new benchmark in the field but also significantly outperforms existing strategies."
Prompting Language-Informed Distribution for Compositional Zero-Shot Learning
Authors: Wentao Bao; Lichang Chen; Heng Huang; Yu Kong
Venue: ECCV 2024 (main)

Abstract: "Compositional zero-shot learning (CZSL) task aims to recognize unseen compositional visual concepts, , sliced tomatoes, where the model is learned only from the seen compositions, , sliced potatoes and red tomatoes. Thanks to the prompt tuning on large pre-trained visual language models such as CLIP, recent literature shows impressively better CZSL performance than traditional vision-based methods. However, the key aspects that impact the generalization to unseen compositions, including the diversity and informativeness of class context, and the entanglement between visual primitives, , state and object, are not properly addressed in existing CLIP-based CZSL literature. In this paper, we propose a model by prompting the language-informed distribution, aka., P LID, for the CZSL task. Specifically, the P LID leverages pre-trained large language models (LLM) to (i ) formulate the language-informed class distributions which are diverse and informative, and (ii ) enhance the compositionality of the class embedding. Moreover, a visual-language primitive decomposition (VLPD) module is proposed to dynamically fuse the classification decisions from the compositional and the primitive space. Orthogonal to the existing literature of soft, hard, or distributional prompts, our method advocates prompting the LLM-supported class distributions, leading to a better zero-shot generalization. Experimental results on MIT-States, UT-Zappos, and C-GQA datasets show the superior performance of the P LID to the prior arts. Our code and models are released: https://github.com/Cogito2012/PLID."