GenAI4ELab/papercli-papers huggingface dataset:
CLR-GAN: Improving GANs Stability and Quality via Consistent Latent Representation and Reconstruction
Authors: Shengke Sun; Ziqian Luan; Zhanshan Zhao; Shijie Luo; Shuzhen Han
Venue: ECCV 2024 (main)

Abstract: "Generative Adversarial Networks(GANs) have received considerable attention due to its outstanding ability to generate images. However, training a GAN is hard since the game between the Generator(G) and the Discriminator(D) is unfair. Towards making the competition fairer, we propose a new perspective of training GANs, named Consistent Latent Representation and Reconstruction(CLR-GAN). In this paradigm, we treat the G and D as an inverse process, the discriminator has an additional task to restore the pre-defined latent code while the generator also needs to reconstruct the real input, thus obtaining a relationship between the latent space of G and the out-features of D. Based on this prior, we can put D and G on an equal position during training using a new criterion. Experimental results on various datasets and architectures prove our paradigm can make GANs more stable and generate better quality images(31.22% gain of FID on CIFAR10 and 39.5% on AFHQ-Cat, respectively). We hope that the proposed perspective can inspire researchers to explore different ways of viewing GANs training, rather than being limited to a two-player game. The code is publicly available at https://github.com/Petecheco/CLR-GAN."
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence
Authors: Mengyao Lyu; Tianxiang Hao; Xinhao Xu; Hui Chen; Zijia Lin; Jungong Han; Guiguang Ding
Venue: ECCV 2024 (main)

Abstract: "Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Active Domain Adaptation (SFADA), where source data becomes inaccessible during adaptation, and a minimum amount of annotation budget is available in the target domain. Without referencing the source data, new challenges emerge in identifying the most informative target samples for labeling, establishing cross-domain alignment during adaptation, and ensuring continuous performance improvements through the iterative query-and-adaptation process. In response, we present learn from the learnt (LFTL), a novel paradigm for SFADA to leverage the learnt knowledge from the source pretrained model and actively iterated models without extra overhead. We propose Contrastive Active Sampling to learn from the hypotheses of the preceding model, thereby querying target samples that are both informative to the current model and persistently challenging throughout active learning. During adaptation, we learn from features of actively selected anchors obtained from previous intermediate models, so that the Visual Persistence-guided Adaptation can facilitate feature distribution alignment and active sample exploitation. Extensive experiments on three widely-used benchmarks show that our LFTL achieves state-of-the-art performance, superior computational efficiency and continuous improvements as the annotation budget increases. Our code is available at https://github.com/lyumengyao/lftl."
PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts
Authors: Zewen Chen; Haina Qin; Juan Wang; Chunfeng Yuan; Bing Li; Weiming Hu; Leon Wang
Venue: ECCV 2024 (main)

Abstract: "Due to the diversity of assessment requirements in various application scenarios for the IQA task, existing IQA methods struggle to directly adapt to these varied requirements after training. Thus, when facing new requirements, a typical approach is fine-tuning these models on datasets specifically created for those requirements. However, it is time-consuming to establish IQA datasets. In this work, we propose a Prompt-based IQA (PromptIQA) that can fast adapt to new requirements without fine-tuning after training. On one hand, it utilizes a short sequence of Image-Score Pairs (ISP) as prompts for targeted predictions, which significantly reduces the dependency on the data requirements. On the other hand, PromptIQA is trained on a mixed dataset with two proposed data augmentation strategies to learn diverse requirements, thus enabling it to fast adapt to new requirements. Experiments indicate that the PromptIQA outperforms SOTA methods with higher performance and better generalization. The code is available at the link."
Motion Mamba: Efficient and Long Sequence Motion Generation
Authors: Zeyu Zhang; Akide Liu; Ian Reid; RICHARD HARTLEY; Bohan Zhuang; Hao Tang
Venue: ECCV 2024 (main)

Abstract: "Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging. Recent advancements in state space models (SSMs), notably Mamba, have showcased considerable promise in long sequence modeling with an efficient hardware-aware design, which appears to be a significant direction upon building motion generation model. Nevertheless, adapting SSMs to motion generation faces hurdles since the lack of a specialized design architecture to model motion sequence. To address these challenges, we propose Motion Mamba, a simple yet efficient approach that presents the pioneering motion generation model utilized SSMs. Specifically, we design a Hierarchical Temporal Mamba (HTM) block to process temporal data by ensemble varying numbers of isolated SSM modules across a symmetric U-Net architecture aimed at preserving motion consistency between frames. We also design a Bidirectional Spatial Mamba (BSM) block to bidirectionally process latent poses, to enhance accurate motion generation within a temporal frame. Our proposed method achieves up to 50% FID improvement and up to 4 times faster on the HumanML3D and KIT-ML datasets compared to the previous best diffusion-based method, which demonstrates strong capabilities of high-quality long sequence motion modeling and real-time human motion generation."
Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis
Authors: Yuanhao Cai; Yixun Liang; Jiahao Wang; Angtian Wang; Yulun Zhang; Xiaokang Yang; Zongwei Zhou; Alan Yuille
Venue: ECCV 2024 (main)

Abstract: "X-ray is widely applied for transmission imaging due to its stronger penetration than natural light. When rendering novel view X-ray projections, existing methods mainly based on NeRF suffer from long training time and slow inference speed. In this paper, we propose a 3D Gaussian splatting-based method, namely X-Gaussian, for X-ray novel view synthesis. Firstly, we redesign a radiative Gaussian point cloud model inspired by the isotropic nature of X-ray imaging. Our model excludes the influence of view direction when learning to predict the radiation intensity of 3D points. Based on this model, we develop a Differentiable Radiative Rasterization (DRR) with CUDA implementation. Secondly, we customize an Angle-pose Cuboid Uniform Initialization (ACUI) strategy that directly uses the parameters of the X-ray scanner to compute the camera information and then uniformly samples point positions within a cuboid enclosing the scanned object. Experiments show that our X-Gaussian outperforms state-of-the-art methods by 6.5 dB while enjoying less than 15% training time and over 73× inference speed. The application on CT reconstruction also reveals the practical values of our method. Code is at https://github.com/caiyuanhao1998/X-Gaussian"
"Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance"
Authors: Liting Lin; Heng Fan; Zhipeng Zhang; Yaowei Wang; Yong Xu; Haibin Ling
Venue: ECCV 2024 (main)

Abstract: "Motivated by the Parameter-Efficient Fine-Tuning (PEFT) in large language models, we propose LoRAT, a method that unveils the power of larger Vision Transformers (ViT) for tracking within laboratory-level resources. The essence of our work lies in adapting LoRA, a technique that fine-tunes a small subset of model parameters without adding inference latency, to the domain of visual tracking. However, unique challenges and potential domain gaps make this transfer not as easy as the first intuition. Firstly, a transformer-based tracker constructs unshared position embedding for template and search image. This poses a challenge for the transfer of LoRA, usually requiring consistency in the design when applied to the pre-trained backbone, to downstream tasks. Secondly, the inductive bias inherent in convolutional heads diminishes the effectiveness of parameter-efficient fine-tuning in tracking models. To overcome these limitations, we first decouple the position embeddings in transformer-based trackers into shared spatial ones and independent type ones. The shared embeddings, which describe the absolute coordinates of multi-resolution images (namely, the template and search images), are inherited from the pre-trained backbones. In contrast, the independent embeddings indicate the sources of each token and are learned from scratch. Furthermore, we design an anchor-free head solely based on a multilayer perceptron (MLP) to adapt PETR, enabling better performance with less computational overhead. With our design, 1) it becomes practical to train trackers with the ViT-g backbone on GPUs with only memory of 25.8GB (batch size of 16); 2) we reduce the training time of the L-224 variant from 35.0 to 10.8 GPU hours; 3) we improve the LaSOT SUC score from 0.703 to 0.742 with the L-224 variant; 4) we fast the inference speed of the L-224 variant from 52 to 119 FPS. Code and models are available at https://github.com/LitingLin/LoRAT."
A Direct Approach to Viewing Graph Solvability
Authors: Federica Arrigoni; Andrea Fusiello; Tomas Pajdla
Venue: ECCV 2024 (main)

Abstract: "The viewing graph is a useful way to represent uncalibrated cameras and their geometric relationships: nodes correspond to cameras and edges represent fundamental matrices. By analyzing this graph, it is possible to establish if the problem is “solvable” in the sense that there exists a unique (up to a single projective transformation) set of cameras that are compliant with the given fundamental matrices. In this paper, we take several steps forward in the study of viewing graph solvability: we propose a new formulation of the problem that is more direct than previous literature, based on a formula that explicitly links pairs of cameras via their fundamental matrix; we introduce the new concept of “”, demonstrating its usefulness in understanding real structure from motion graphs; we propose an algorithm for testing and extracting components of unsolvable cases, that is more efficient than previous work; we set up an open question on the connection between and solvability."
CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularization
Authors: Jiawei Zhang; Jiahe Li; Xiaohan Yu; Lei Huang; Lin Gu; Jin Zheng; Xiao Bai
Venue: ECCV 2024 (main)

Abstract: "3D Gaussian Splatting (3DGS) creates a radiance field consisting of 3D Gaussians to represent a scene. With sparse training views, 3DGS easily suffers from overfitting, negatively impacting rendering. This paper introduces a new co-regularization perspective for improving sparse-view 3DGS. When training two 3D Gaussian radiance fields, we observe that the two radiance fields exhibit point disagreement and rendering disagreement that can unsupervisedly predict reconstruction quality, stemming from the randomness of densification implementation. We further quantify the two disagreements and demonstrate the negative correlation between them and accurate reconstruction, which allows us to identify inaccurate reconstruction without accessing ground-truth information. Based on the study, we propose CoR-GS, which identifies and suppresses inaccurate reconstruction based on the two disagreements: (i) Co-pruning considers Gaussians that exhibit high point disagreement in inaccurate positions and prunes them. (ii) Pseudo-view co-regularization considers pixels that exhibit high rendering disagreement are inaccurate and suppress the disagreement. Results on LLFF, Mip-NeRF360, DTU, and Blender demonstrate that CoR-GS effectively regularizes the scene geometry, reconstructs the compact representations, and achieves state-of-the-art novel view synthesis quality under sparse training views. Project page: https: //jiaw-z.github.io/CoR-GS"
SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
Authors: Qingwen Zhang; Yi Yang; Peizheng Li; Olov Andersson; Patric Jensfelt
Venue: ECCV 2024 (main)

Abstract: "Scene flow estimation predicts the 3D motion at each point in successive LiDAR scans. This detailed, point-level, information can help autonomous vehicles to accurately predict and understand dynamic changes in their surroundings. Current state-of-the-art methods require annotated data to train scene flow networks and the expense of labeling inherently limits their scalability. Self-supervised approaches can overcome the above limitations, yet face two principal challenges that hinder optimal performance: point distribution imbalance and disregard for object-level motion constraints. In this paper, we propose SeFlow, a self-supervised method that integrates efficient dynamic classification into a learning-based scene flow pipeline. We demonstrate that classifying static and dynamic points helps design targeted objective functions for different motion patterns. We also emphasize the importance of internal cluster consistency and correct object point association to refine the scene flow estimation, in particular on object details. Our real-time capable method achieves state-of-the-art performance on the self-supervised scene flow task on Argoverse 2 and Waymo datasets. The code is open-sourced at https://github.com/KTH-RPL/SeFlow."
ZeST: Zero-Shot Material Transfer from a Single Image
Authors: Ta-Ying Cheng; Prafull Sharma; Andrew Markham; Niki Trigoni; Varun Jampani
Venue: ECCV 2024 (main)

Abstract: "We propose , a method for zero-shot material transfer to an object in the input image given a material exemplar image. leverages existing diffusion adapters to extract implicit material representation from the exemplar image. This representation is used to transfer the material using pre-trained inpainting diffusion model on the object in the input image using depth estimates as geometry cue and grayscale object shading as illumination cues. The method works on real images without any training resulting a zero-shot approach. Both qualitative and quantitative results on real and synthetic datasets demonstrate that outputs photorealistic images with transferred materials. We also show the application of to perform multiple edits and robust material assignment under different illuminations. Project Page: https://ttchengab.github.io/zest"
3D Congealing: 3D-Aware Image Alignment in the Wild
Authors: Yunzhi Zhang; Zizhang Li; Amit Raj; Andreas Engelhardt; Yuanzhen Li; Tingbo Hou; Jiajun Wu; Varun Jampani
Venue: ECCV 2024 (main)

Abstract: "We propose , a novel problem of 3D-aware alignment for 2D images capturing semantically similar objects. Given a collection of unlabeled Internet images, our goal is to associate the shared semantic parts from the inputs and aggregate the knowledge from 2D images to a shared 3D canonical space. We introduce a general framework that tackles the task without assuming shape templates, poses, or any camera parameters. At its core is a canonical 3D representation that encapsulates geometric and semantic information. The framework optimizes for the canonical representation together with the pose for each input image, and a per-image coordinate map that warps 2D pixel coordinates to the 3D canonical frame to account for the shape matching. The optimization procedure fuses prior knowledge from a pre-trained image generative model and semantic information from input images. The former provides strong knowledge guidance for this under-constraint task, while the latter provides the necessary information to mitigate the training data bias from the pre-trained model. Our framework can be used for various tasks such as pose estimation and image editing, achieving strong results on real-world image datasets under challenging illumination conditions and on in-the-wild online image collections. Project page at https://ai.stanford. edu/~yzzhang/projects/3d-congealing/."
SMooDi: Stylized Motion Diffusion Model
Authors: Lei Zhong; Yiming Xie; Varun Jampani; Deqing Sun; Huaizu Jiang
Venue: ECCV 2024 (main)

Abstract: "We introduce a novel Stylized Motion Diffusion model, dubbed , to generate stylized motion driven by content texts and style motion sequences. Unlike existing methods that either generate motion of various content or transfer style from one sequence to another, can rapidly generate motion across a broad range of content and diverse styles. To this end, we tailor a pre-trained text-to-motion model for stylization. Specifically, we propose style guidance to ensure that the generated motion closely matches the reference style, alongside a lightweight style adaptor that directs the motion towards the desired style while ensuring realism. Experiments across various applications demonstrate that our proposed framework outperforms existing methods in stylized motion generation. Project Page: https://neu-vi.github.io/SMooDi/"
ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs
Authors: Viraj Shah; Nataniel Ruiz; Forrester Cole; Erika Lu; Svetlana Lazebnik; Yuanzhen Li; Varun Jampani
Venue: ECCV 2024 (main)

Abstract: "Methods for finetuning generative models for concept-driven personalization generally achieve strong results for subject-driven or style-driven generation. Recently, low-rank adaptations () have been proposed as a parameter-efficient way of achieving concept-driven personalization. While recent work explores the combination of separate LoRAs to achieve joint generation of learned styles and subjects, existing techniques do not reliably address the problem, so that either subject fidelity or style fidelity are compromised. We propose , a method to cheaply and effectively merge independently trained style and subject LoRAs in order to achieve generation of any user-provided subject in any user-provided style. Experiments on a wide range of subject and style combinations show that can generate compelling results with meaningful improvements over baselines in subject and style fidelity while preserving the ability to recontextualize."
SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion
Authors: Vikram Voleti; Chun-Han Yao; Mark Boss; Adam Letts; David Pankratz; Dmitrii Tochilkin; Christian Laforte; Robin Rombach; Varun Jampani
Venue: ECCV 2024 (main)

Abstract: "We present Stable Video 3D (SV3D) — a latent video diffusion model for high-resolution, image-to-multi-view generation of orbital videos around a 3D object. Recent works propose to adapt 2D generative models for novel view synthesis (NVS) and 3D optimization. However, these methods have several disadvantages due to limited views or inconsistent NVS, affecting the performance of 3D object generation. In this work, we propose SV3D that adapts image-to-video diffusion model for novel multi-view synthesis and 3D generation, thereby leveraging the generalization and multi-view consistency of the video models, while further adding explicit camera control for NVS. We also propose improved 3D optimization techniques for image-to-3D generation using SV3D and its NVS outputs. Extensive experiments on multiple datasets with 2D and 3D metrics and user study demonstrate SV3D’s state-of-the-art performance on NVS as well as 3D reconstruction compared to prior works."
WordRobe: Text-Guided Generation of Textured 3D Garments
Authors: Astitva Srivastava; Pranav Manu; Amit Raj; Varun Jampani; Avinash Sharma
Venue: ECCV 2024 (main)

Abstract: "In this paper, we tackle a new and challenging problem of text-driven generation of 3D garments with high-quality textures. We propose, WordRobe, a novel framework for the generation of unposed & textured 3D garment meshes from user-friendly text prompts. We achieve this by first learning a latent representation of 3D garments using a novel coarse-to-fine training strategy and a loss for latent disentanglement, promoting better latent interpolation. Subsequently, we align the garment latent space to the CLIP embedding space in a weakly supervised manner, enabling text-driven 3D garment generation and editing. For appearance modeling, we leverage the zero-shot generation capability of ControlNet to synthesize view-consistent texture maps in a single feed-forward inference step, thereby drastically decreasing the generation time as compared to existing methods. We demonstrate superior performance over current SOTAs for learning 3D garment latent space, garment interpolation, and text-driven texture synthesis, supported by quantitative evaluation and qualitative user study. The unposed 3D garment meshes generated using WordRobe can be directly fed to standard cloth simulation & animation pipelines without any post-processing."
Learning to Generate Conditional Tri-plane for 3D-aware Expression Controllable Portrait Animation
Authors: Taekyung Ki; Dongchan Min; Gyeongsu Chae
Venue: ECCV 2024 (main)

Abstract: "In this paper, we present , a one-shot 3D-aware portrait animation method that is able to control the facial expression and camera view of a given portrait image. To achieve this, we introduce a tri-plane generator with an effective expression conditioning method, which directly generates a tri-plane of 3D prior by transferring the expression parameter of 3DMM into the source image. The tri-plane is then decoded into the image of different view through a differentiable volume rendering. Existing portrait animation methods heavily rely on image warping to transfer the expression in the motion space, challenging on disentanglement of appearance and expression. In contrast, we propose a contrastive pre-training framework for appearance-free expression parameter, eliminating undesirable appearance swap when transferring a cross-identity expression. Extensive experiments show that our pre-training framework can learn the appearance-free expression representation hidden in 3DMM, and our model can generate 3D-aware expression controllable portrait images without appearance swap in the cross-identity manner."
SimPB: A Single Model for 2D and 3D Object Detection from Multiple Cameras
Authors: Yingqi Tang; Zhaotie Meng; Guoliang Chen; Erkang Cheng
Venue: ECCV 2024 (main)

Abstract: "The field of autonomous driving has attracted considerable interest in approaches that directly infer 3D objects in the Bird’s Eye View (BEV) from multiple cameras. Some attempts have also explored utilizing 2D detectors from single images to enhance the performance of 3D detection. However, these approaches rely on a two-stage process with separate detectors, where the 2D detection results are utilized only once for token selection or query initialization. In this paper, we present a single model termed SimPB, which Simultaneously detects 2D objects in the Perspective view and 3D objects in the BEV space from multiple cameras. To achieve this, we introduce a hybrid decoder consisting of several multi-view 2D decoder layers and several 3D decoder layers, specifically designed for their respective detection tasks. A Dynamic Query Allocation module and an Adaptive Query Aggregation module are proposed to continuously update and refine the interaction between 2D and 3D results, in a cyclic 3D-2D-3D manner. Additionally, Query-group Attention is utilized to strengthen the interaction among 2D queries within each camera group. In the experiments, we evaluate our method on the nuScenes dataset and demonstrate promising results for both 2D and 3D detection tasks. Our code is available at: https: //github.com/nullmax-vision/SimPB."