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DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior

DreamControl: Control-Based Text-to-3D Generation with 3D Se...

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conference on computer vision and pattern recognition (CVPR)

作者： Tianyu Huang Yihan Zeng Zhilu Zhang Wan Xu Hang Xu Songcen Xu Rynson W. H. Lau Wangmeng Zuo Harbin Institute of Technology City University of Hong Kong Huawei Noah's Ark Lab

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

3D generation has raised great attention in recent years. With the success of text-to-image diffusion models, the 2D-lifting technique becomes a promising route to controllable 3D generation. However, these methods tend to present inconsistent geometry, which is also known as the Janus problem. We observe that the problem is caused mainly by two aspects, i.e., viewpoint bias in 2D diffusion models and overfitting of the optimization objective. To address it, we propose a two-stage 2D-lifting framework, namely DreamControl, which optimizes coarse NeRF scenes as 3D self-prior and then generates fine-grained objects with control-based score distillation. Specifically, adaptive viewpoint sampling and boundary integrity metric are proposed to ensure the consistency of generated priors. The priors are then regarded as input conditions to maintain reasonable geometries, in which conditional LoRA and weighted score are further proposed to optimize detailed textures. DreamControl can generate high-quality 3D content in terms of both geometry consistency and texture fidelity. Moreover, our control-based optimization guidance is applicable to more downstream tasks, including user-guided generation and 3D animation. The project page is available at https://***/tyhuang0428/DreamControl.

关键词： Geometry Measurement computer vision Three-dimensional displays Text to image Neural radiance field Diffusion models

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Structure Matters: Tackling the Semantic Discrepancy in Diffusion Models for Image Inpainting

Structure Matters: Tackling the Semantic Discrepancy in Diff...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Haipeng Liu Yang Wang Biao Qian Meng Wang Yong Rui Hefei University of Technology China Lenovo Research China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Denoising diffusion probabilistic models (DDPMs) for image inpainting aim to add the noise to the texture of the image during the forward process and recover the masked regions with the unmasked ones of the texture via the reverse denoising process. Despite the meaningful semantics gen-eration, the existing arts suffer from the semantic discrep-ancy between the masked and unmasked regions, since the semantically dense unmasked texture fails to be completely degraded while the masked regions turn to the pure noise in diffusion process, leading to the large discrepancy between them. In this paper, we aim to answer how the unmasked se-mantics guide the texture denoising process; together with how to tackle the semantic discrepancy, to facilitate the con-sistent and meaningful semantics generation. To this end, we propose a novel structure-guided diffusion model for image inpainting named StrDiffusion, to reformulate the conventional texture denoising process under the structure guidance to derive a simplified denoising objective for im-age inpainting, while revealing: 1) the semantically sparse structure is beneficial to tackle the semantic discrepancy in the early stage, while the dense texture generates the rea-sonable semantics in the late stage; 2) the semantics from the unmasked regions essentially offer the time-dependent structure guidance for the texture denoising process, ben-efiting from the time-dependent sparsity of the structure semantics. For the denoising process, a structure-guided neural network is trained to estimate the simplified denoising objective by exploiting the consistency of the denoised structure between masked and unmasked regions. Besides, we devise an adaptive resampling strategy as aformal criterion as whether the structure is competent to guide the texture denoising process, while regulate their semantic corre-lations. Extensive experiments validate the merits of StrDif-fusion over the state-of-the-arts. Our code is available at https:/

关键词： computer vision Codes Art Semantics Noise reduction Noise Neural networks

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Benchmarking Audio Visual Segmentation for Long-Untrimmed Videos

Benchmarking Audio Visual Segmentation for Long-Untrimmed Vi...

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conference on computer vision and pattern recognition (CVPR)

作者： Chen Liu Peike Patrick Li Qingtao Yu Hongwei Sheng Dadong Wang Lincheng Li Xin Yu The University of Queensland NetEase Fuxi AI Lab CSIRO DATA61 Matrix Verse Australian National University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing audio-visual segmentation datasets typically focus on short-trimmed videos with only one pixel-map annotation for a per-second video clip. In contrast, for untrimmed videos, the sound duration, start- and end-sounding time positions, and visual deformation of audible objects vary significantly. Therefore, we observed that current AVS models trained on trimmed videos might struggle to segment sounding objects in long videos. To investigate the feasibility of grounding audible objects in videos along both temporal and spatial dimensions, we introduce the Long-Untrimmed Audio-Visual Segmentation dataset (LU-AVS), which includes precise frame-level annotations of sounding emission times and provides exhaustive mask annotations for all frames. Considering that pixel-level annotations are difficult to achieve in some complex scenes, we also provide the bounding boxes to indicate the sounding regions. Specifically, LU-AVS contains 10M mask annotations across 6.6K videos, and 11M bounding box annotations across 7K videos. Compared with the existing datasets, LU-AVS videos are on average 4~8 times longer, with the silent duration being 3~15 times greater. Furthermore, we try our best to adapt some baseline models that were originally designed for audio-visual-relevant tasks to examine the challenges of our newly curated LU-AVS. Through comprehensive evaluation, we demonstrate the challenges of LU-AVS compared to the ones containing trimmed videos. Therefore, LU-AVS provides an ideal yet challenging platform for evaluating audio-visual segmentation and localization on untrimmed long videos. The dataset is publicly available at: https://***/LU-AVS/.

关键词： Location awareness Visualization computer vision Adaptation models Annotations Grounding Deformation

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Mosaic-SDF for 3D Generative Models

Mosaic-SDF for 3D Generative Models

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conference on computer vision and pattern recognition (CVPR)

作者： Lior Yariv Omri Puny Oran Gafni Yaron Lipman GenAI Meta Weizmann Institute of Science FAIR Meta

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes. When training a diffusion or flow models on 3D shapes a crucial design choice is the shape representation. An effective shape representation needs to adhere three design principles: it should allow an efficient conversion of large 3D datasets to the representation form; it should provide a good tradeoff of approximation power versus number of parameters; and it should have a simple tensorial form that is compatible with existing powerful neural architectures. While standard 3D shape representations such as volumetric grids and point clouds do not adhere to all these principles simultaneously, we advocate in this paper a new representation that does. We introduce Mosaic-SDF (M-SDF): a simple 3D shape representation that approximates the Signed Distance Function (SDF) of a given shape by using a set of local grids spread near the shape's boundary. The M-SDF representation is fast to compute for each shape individually making it readily parallelizable; it is parameter efficient as it only covers the space around the shape's boundary; and it has a simple matrix form, compatible with Transformer-based architectures. We demonstrate the efficacy of the M-SDF representation by using it to train a 3D generative flow model including class-conditioned generation with the ShapeNetCore-V2 (3D Warehouse) dataset, and text-to-3D generation using a dataset of about 600k caption-shape pairs.

关键词： Training Point cloud compression Solid modeling Three-dimensional displays Shape Computational modeling computer architecture

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MV-Adapter: Multimodal Video Transfer Learning for Video Text Retrieval

MV-Adapter: Multimodal Video Transfer Learning for Video Tex...

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conference on computer vision and pattern recognition (CVPR)

作者： Xiaojie Jin Bowen Zhang Weibo Gong Kai Xu Xueqing Deng Peng Wang Zhao Zhang Xiaohui Shen Jiashi Feng Bytedance Inc. Hefei University of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

State-of-the-art video-text retrieval (VTR) methods typically involve fully fine-tuning a pre-trained model (e.g. CLIP) on specific datasets. However, this can result in significant storage costs in practical applications as a separate model per task must be stored. To address this issue, we present our pioneering work that enables parameter-efficient VTR using a pre-trained model, with only a small number of tunable parameters during training. Towards this goal, we propose a new method dubbed Multimodal Video Adapter (MV-Adapter) for efficiently transferring the knowledge in the pre-trained CLIP from image-text to video-text. Specifically, MV-Adapter utilizes bottleneck structures in both video and text branches, along with two novel components. The first is a Temporal Adaptation Module that is incorporated in the video branch to introduce global and local temporal contexts. We also train weights calibrations to adjust to dynamic variations across frames. The second is Cross Modality Tying that generates weights for video/text branches through sharing cross modality factors, for better aligning between modalities. Thanks to above innovations, MV-Adapter can achieve comparable or better performance than standard full fine-tuning with negligible parameters overhead. Notably, MV-Adapter consistently outperforms various competing methods in V2T/T2V tasks with large margins on five widely used VTR benchmarks (MSR-VTT, MSVD, LSMDC, DiDemo, and ActivityNet). Codes will be released.

关键词： Training Adaptation models Technological innovation computer vision Costs Transfer learning Question answering (information retrieval)

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GLACE: Global Local Accelerated Coordinate Encoding

GLACE: Global Local Accelerated Coordinate Encoding

引用

conference on computer vision and pattern recognition (CVPR)

作者： Fangjinhua Wang Xudong Jiang Silvano Galliani Christoph Vogel Marc Pollefeys Department of Computer Science ETH Zurich Microsoft Mixed Reality & AI Zurich Lab

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Scene coordinate regression (SCR) methods are a family of visual localization methods that directly regress 2D-3D matches for camera pose estimation. They are effective in small-scale scenes but face significant challenges in large-scale scenes that are further amplified in the absence of ground truth 3D point clouds for supervision. Here, the model can only rely on reprojection constraints and needs to implicitly triangulate the points. The challenges stem from a fundamental dilemma: The network has to be invariant to observations of the same landmark at different viewpoints and lighting conditions, etc., but at the same time discriminate unrelated but similar observations. The latter becomes more relevant and severe in larger scenes. In this work, we tackle this problem by introducing the concept of co-visibility to the network. We propose GLACE, which integrates pre-trained global and local encodings and enables SCR to scale to large scenes with only a single small-sized network. Specifically, we propose a novel feature diffusion technique that implicitly groups the reprojection constraints with co-visibility and avoids overfitting to trivial solutions. Additionally, our position decoder parameterizes the output positions for large-scale scenes more effectively. Without using 3D models or depth maps for supervision, our method achieves state-of-the-art results on large-scale scenes with a low-map-size model. On Cambridge landmarks, with a single model, we achieve 17% lower median position error than Poker, the ensemble variant of the state-of-the-art SCR method ACE. Code is avail-able at: https://***/cvg/glace.

关键词： Point cloud compression Location awareness Solid modeling Visualization Three-dimensional displays Pose estimation Lighting

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FocSAM: Delving Deeply into Focused Objects in Segmenting Anything

FocSAM: Delving Deeply into Focused Objects in Segmenting An...

引用

conference on computer vision and pattern recognition (CVPR)

作者： You Huang Zongyu Lan Liujuan Cao Xianming Lin Shengchuan Zhang Guannan Jiang Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Intelligent Manufacturing Department Contemporary Amperex Technology Co. Limited (CATL)

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The Segment Anything Model (SAM) marks a notable milestone in segmentation models, highlighted by its robust zero-shot capabilities and ability to handle diverse prompts. SAM follows a pipeline that separates interactive segmentation into image preprocessing through a large encoder and interactive inference via a lightweight decoder, ensuring efficient real-time performance. However, SAM faces stability issues in challenging samples upon this pipeline. These issues arise from two main factors. Firstly, the image preprocessing disables SAM to dynamically use image-level zoom-in strategies to refocus on the target object during interaction. Secondly, the lightweight decoder struggles to sufficiently integrate interactive information with image embeddings. To address these two limitations, we propose FocSAM with a pipeline redesigned on two pivotal aspects. First, we propose Dynamic Window Multi-head Self-Attention (Dwin-MSA) to dynamically refocus SAM's image embeddings on the target object. Dwin-MSA localizes attention computations around the target object, enhancing object-related embeddings with minimal computational overhead. Second, we propose Pixel-wise Dynamic ReLU (P-DyReLU) to enable sufficient integration of interactive information from a few initial clicks that have significant impacts on the overall segmentation results. Experimentally, FocSAM augments SAM's interactive segmentation performance to match the existing state-of-the-art method in segmentation quality, requiring only about 5.6% of this method's inference time on CPUs. Code is available at https://***/YouHuang67/focsam.

关键词： Image segmentation computer vision Codes Pipelines Stability analysis Real-time systems Decoding

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Mask Grounding for Referring Image Segmentation

Mask Grounding for Referring Image Segmentation

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conference on computer vision and pattern recognition (CVPR)

作者： Yong Xien Chng Henry Zheng Yizeng Han Xuchong Qiu Gao Huang Department of Automation BNRist Tsinghua University Bosch Corporate Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Referring Image Segmentation (RIS) is a challenging task that requires an algorithm to segment objects referred by free-form language expressions. Despite significant progress in recent years, most state-of-the-art (SOTA) methods still suffer from considerable language-image modality gap at the pixel and word level. These methods generally 1) rely on sentence-level language features for languageimage alignment and 2) lack explicit training supervision for fine-grained visual grounding. Consequently, they exhibit weak object-level correspondence between visual and language features. Without well-grounded features, prior methods struggle to understand complex expressions that require strong reasoning over relationships among multiple objects, especially when dealing with rarely used or ambiguous clauses. To tackle this challenge, we introduce a novel Mask Grounding auxiliary task that significantly improves visual grounding within language features, by explicitly teaching the model to learn fine-grained correspondence between masked textual tokens and their matching visual objects. Mask Grounding can be directly used on prior RIS methods and consistently bring improvements. Furthermore, to holistically address the modality gap, we also design a cross-modal alignment loss and an accompanying alignment module. These additions work synergistically with Mask Grounding. With all these techniques, our comprehensive approach culminates in MagNet (Mask-grounded Network), an architecture that significantly outperforms prior arts on three key benchmarks (Ref CO CO, RefCOCO+ and G-Ref), demonstrating our method's effectiveness in addressing current limitations of RIS algorithms. Our code and pre-trained weights will be released.

关键词： Training Visualization Image segmentation computer vision Grounding Magnetic resonance imaging Benchmark testing

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DiffForensics: Leveraging Diffusion Prior to Image Forgery Detection and Localization

DiffForensics: Leveraging Diffusion Prior to Image Forgery D...

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conference on computer vision and pattern recognition (CVPR)

作者： Zeqin Yu Jiangqun Ni Yuzhen Lin Haoyi Deng Bin Li School of Computer Science and Engineering Sun Yat-sen University School of Cyber Science and Technology Sun Yat-sen University Department of New Networks Peng Cheng Laboratory Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

As manipulating images may lead to misinterpretation of the visual content, addressing the image forgery detection and localization (IFDL) problem has drawn serious public concerns. In this work, we propose a simple assumption that the effective forensic method should focus on the mesoscopic properties of images. Base on the assumption, a novel two-stage self-supervised framework leveraging the diffusion model for IFDL task, i.e., DiffForensics, is proposed in this paper. The DiffForensics begins with self-supervised denoising diffusion paradigm equipped with the module of encoder-decoder structure, by freezing the pre-trained encoder (e.g., in ADE-20K) to inherit macroscopic features for general image characteristics, while encour-aging the decoder to learn microscopic feature represen-tation of images, enforcing the whole model to focus the mesoscopic representations. The pre-trained model as a prior, is then further fine-tuned for IFDL task with the customized Edge Cue Enhancement Module (ECEM), which progressively highlights the boundary features within the manipulated regions, thereby refining tampered area local-ization with better precision. Extensive experiments on several public challenging datasets demonstrate the effectiveness of the proposed method compared with other state-of-the-art methods. The proposed DiffForensics could significantly improve the model's capabilities for both accurate tamper detection and precise tamper localization while con-currently elevating its generalization and robustness.

关键词： Location awareness Visualization Image edge detection Noise reduction Refining Multitasking Forgery

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GeoChat:Grounded Large vision-Language Model for Remote Sensing

GeoChat:Grounded Large Vision-Language Model for Remote Sens...

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conference on computer vision and pattern recognition (CVPR)

作者： Kartik Kuckreja Muhammad Sohail Danish Muzammal Naseer Abhijit Das Salman Khan Fahad Shahbaz Khan Mohamed bin Zayed University of AI Birla Institute of Technology & Science Hyderabad Australian National University Linkoping University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent advancements in Large vision-Language Models (VLMs) have shown great promise in natural image domains, allowing users to hold a dialogue about given visual content. However, such general-domain VLMs perform poorly for Remote Sensing (RS) scenarios, leading to inaccurate or fabricated information when presented with RS domain-specific queries. Such a behavior emerges due to the unique challenges introduced by RS imagery. For example, to handle high-resolution RS imagery with diverse scale changes across categories and many small objects, region-level reasoning is necessary alongside holistic scene inter-pretation. Furthermore, the lack of domain-specific multimodal instruction following data as well as strong back-bone models for RS make it hard for the models to align their behavior with user queries. To address these limitations, we propose GeoChat - the first versatile remote sensing VLM that offers multitask conversational capabilities with high-resolution RS images. Specifically, GeoChat can not only answer image-level queries but also accepts region inputs to hold region-specific dialogue. Further-more, it can visually ground objects in its responses by referring to their spatial coordinates. To address the lack of domain-specific datasets, we generate a novel RS multimodal instruction-following dataset by extending image-text pairs from existing diverse RS datasets. We establish a comprehensive benchmarkfor RS multitask conversations and compare with a number of baseline methods. GeoChat demonstrates robust zero-shot performance on various RS tasks, e.g., image and region captioning, visual question answering, scene classification, visually grounded conversations and referring detection. Our code is available here.

关键词： Visualization Scene classification Grounding Oral communication Object detection Benchmark testing Data models

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