ReMatching Dynamic Reconstruction Flow

• URL: http://arxiv.org/abs/2411.00705v1
• Date: Fri, 01 Nov 2024 16:09:33 GMT
• Title: ReMatching Dynamic Reconstruction Flow
• Authors: Sara Oblak, Despoina Paschalidou, Sanja Fidler, Matan Atzmon,
• Abstract summary: We introduce the ReMatching framework, designed to improve generalization quality by incorporating deformation priors into dynamic reconstruction models. The framework is highly adaptable and can be applied to various dynamic representations. Our evaluations on popular benchmarks involving both synthetic and real-world dynamic scenes demonstrate a clear improvement in reconstruction accuracy of current state-of-the-art models.
• Score: 55.272357926111454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstructing dynamic scenes from image inputs is a fundamental computer vision task with many downstream applications. Despite recent advancements, existing approaches still struggle to achieve high-quality reconstructions from unseen viewpoints and timestamps. This work introduces the ReMatching framework, designed to improve generalization quality by incorporating deformation priors into dynamic reconstruction models. Our approach advocates for velocity-field-based priors, for which we suggest a matching procedure that can seamlessly supplement existing dynamic reconstruction pipelines. The framework is highly adaptable and can be applied to various dynamic representations. Moreover, it supports integrating multiple types of model priors and enables combining simpler ones to create more complex classes. Our evaluations on popular benchmarks involving both synthetic and real-world dynamic scenes demonstrate a clear improvement in reconstruction accuracy of current state-of-the-art models.

Related papers

• ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer [58.49950218437718]
  We present ReCoM, an efficient framework for generating high-fidelity and generalizable human body motions synchronized with speech. The core innovation lies in the Recurrent Embedded Transformer (RET), which integrates Dynamic Embedding Regularization (DER) into a Vision Transformer (ViT) core architecture. To enhance model robustness, we incorporate the proposed DER strategy, which equips the model with dual capabilities of noise resistance and cross-domain generalization.
  arXiv  Detail & Related papers  (2025-03-27T16:39:40Z)
• DEALing with Image Reconstruction: Deep Attentive Least Squares [15.746202603806852]
  We propose a data-driven reconstruction method inspired by the classic Tikhonov regularization. Our approach iteratively refines intermediate reconstructions by solving a sequence of quadratic problems. Our method achieves performance on par with leading plug-and-play and learned regularizer approaches.
  arXiv  Detail & Related papers  (2025-02-06T13:43:28Z)
• Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos [101.48581851337703]
  We present BTimer, the first motion-aware feed-forward model for real-time reconstruction and novel view synthesis of dynamic scenes. Our approach reconstructs the full scene in a 3D Gaussian Splatting representation at a given target ('bullet') timestamp by aggregating information from all the context frames. Given a casual monocular dynamic video, BTimer reconstructs a bullet-time scene within 150ms while reaching state-of-the-art performance on both static and dynamic scene datasets.
  arXiv  Detail & Related papers  (2024-12-04T18:15:06Z)
• Adaptive and Temporally Consistent Gaussian Surfels for Multi-view Dynamic Reconstruction [3.9363268745580426]
  AT-GS is a novel method for reconstructing high-quality dynamic surfaces from multi-view videos through per-frame incremental optimization. We reduce temporal jittering in dynamic surfaces by ensuring consistency in curvature maps across consecutive frames. Our method achieves superior accuracy and temporal coherence in dynamic surface reconstruction, delivering high-fidelity space-time novel view synthesis.
  arXiv  Detail & Related papers  (2024-11-10T21:30:16Z)
• TFS-NeRF: Template-Free NeRF for Semantic 3D Reconstruction of Dynamic Scene [25.164085646259856]
  This paper introduces a 3D semantic NeRF for dynamic scenes captured from sparse or singleview RGB videos. Our framework uses an Invertible Neural Network (INN) for LBS prediction, the training process. Our approach produces high-quality reconstructions of both deformable and non-deformable objects in complex interactions.
  arXiv  Detail & Related papers  (2024-09-26T01:34:42Z)
• Simultaneous Map and Object Reconstruction [66.66729715211642]
  We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR. We take inspiration from recent novel view synthesis methods and pose the reconstruction problem as a global optimization. By careful modeling of continuous-time motion, our reconstructions can compensate for the rolling shutter effects of rotating LiDAR sensors.
  arXiv  Detail & Related papers  (2024-06-19T23:53:31Z)
• Enhanced Event-Based Video Reconstruction with Motion Compensation [26.03328887451797]
  We propose warping the input intensity frames and sparse codes to enhance reconstruction quality. A CISTA-Flow network is constructed by integrating a flow network with CISTA-LSTC for motion compensation. Results demonstrate that our approach achieves state-of-the-art reconstruction accuracy and simultaneously provides reliable dense flow estimation.
  arXiv  Detail & Related papers  (2024-03-18T16:58:23Z)
• SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes [75.9110646062442]
  We propose SceNeRFlow to reconstruct a general, non-rigid scene in a time-consistent manner. Our method takes multi-view RGB videos and background images from static cameras with known camera parameters as input. We show experimentally that, unlike prior work that only handles small motion, our method enables the reconstruction of studio-scale motions.
  arXiv  Detail & Related papers  (2023-08-16T09:50:35Z)
• IRGen: Generative Modeling for Image Retrieval [82.62022344988993]
  In this paper, we present a novel methodology, reframing image retrieval as a variant of generative modeling. We develop our model, dubbed IRGen, to address the technical challenge of converting an image into a concise sequence of semantic units. Our model achieves state-of-the-art performance on three widely-used image retrieval benchmarks and two million-scale datasets.
  arXiv  Detail & Related papers  (2023-03-17T17:07:36Z)
• RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection [66.08293086254851]
  We propose a reciprocal learning framework to reinforce the learning of a RefSR network. The newly proposed module aligns reference-input images at multi-scale feature spaces and performs reference-aware feature selection. We empirically show that multiple recent state-of-the-art RefSR models can be consistently improved with our reciprocal learning paradigm.
  arXiv  Detail & Related papers  (2022-11-08T12:39:35Z)
• Insights from Generative Modeling for Neural Video Compression [31.59496634465347]
  We present newly proposed neural video coding algorithms through the lens of deep autoregressive and latent variable modeling. We propose several architectures that yield state-of-the-art video compression performance on high-resolution video. We provide further evidence that the generative modeling viewpoint can advance the neural video coding field.
  arXiv  Detail & Related papers  (2021-07-28T02:19:39Z)
• Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model [58.17021225930069]
  We explain the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) We propose a more efficient EAT model, and design task-related heads to deal with different tasks more flexibly. Our approach achieves state-of-the-art results on the ImageNet classification task compared with recent vision transformer works.
  arXiv  Detail & Related papers  (2021-05-31T16:20:03Z)
• Improving Sequential Latent Variable Models with Autoregressive Flows [30.053464816814348]
  We propose an approach for improving sequence modeling based on autoregressive normalizing flows. Results are presented on three benchmark video datasets, where autoregressive flow-based dynamics improve log-likelihood performance.
  arXiv  Detail & Related papers  (2020-10-07T05:14:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.