Fixing the Perspective: A Critical Examination of Zero-1-to-3

• URL: http://arxiv.org/abs/2411.15706v1
• Date: Sun, 24 Nov 2024 04:21:51 GMT
• Title: Fixing the Perspective: A Critical Examination of Zero-1-to-3
• Authors: Jack Yu, Xueying Jia, Charlie Sun, Prince Wang,
• Abstract summary: We investigate Zero-1-to-3's cross-attention mechanism within the Spatial Transformer of the diffusion 2D-conditional UNet. We propose two significant improvements: (1) a corrected implementation that enables effective utilization of the cross-attention mechanism, and (2) an enhanced architecture that can leverage multiple conditional views simultaneously.
• Score: 0.0
• License:
• Abstract: Novel view synthesis is a fundamental challenge in image-to-3D generation, requiring the generation of target view images from a set of conditioning images and their relative poses. While recent approaches like Zero-1-to-3 have demonstrated promising results using conditional latent diffusion models, they face significant challenges in generating consistent and accurate novel views, particularly when handling multiple conditioning images. In this work, we conduct a thorough investigation of Zero-1-to-3's cross-attention mechanism within the Spatial Transformer of the diffusion 2D-conditional UNet. Our analysis reveals a critical discrepancy between Zero-1-to-3's theoretical framework and its implementation, specifically in the processing of image-conditional context. We propose two significant improvements: (1) a corrected implementation that enables effective utilization of the cross-attention mechanism, and (2) an enhanced architecture that can leverage multiple conditional views simultaneously. Our theoretical analysis and preliminary results suggest potential improvements in novel view synthesis consistency and accuracy.

Related papers

• MultiDiff: Consistent Novel View Synthesis from a Single Image [60.04215655745264]
  MultiDiff is a novel approach for consistent novel view synthesis of scenes from a single RGB image. Our results demonstrate that MultiDiff outperforms state-of-the-art methods on the challenging, real-world datasets RealEstate10K and ScanNet.
  arXiv  Detail & Related papers  (2024-06-26T17:53:51Z)
• Zero-to-Hero: Enhancing Zero-Shot Novel View Synthesis via Attention Map Filtering [16.382098950820822]
  We propose Zero-to-Hero, a novel test-time approach that enhances view synthesis by manipulating attention maps. We modify the self-attention mechanism to integrate information from the source view, reducing shape distortions. Results demonstrate substantial improvements in fidelity and consistency, validated on a diverse set of out-of-distribution objects.
  arXiv  Detail & Related papers  (2024-05-29T00:58:22Z)
• Consistent123: Improve Consistency for One Image to 3D Object Synthesis [74.1094516222327]
  Large image diffusion models enable novel view synthesis with high quality and excellent zero-shot capability. These models have no guarantee of view consistency, limiting the performance for downstream tasks like 3D reconstruction and image-to-3D generation. We propose Consistent123 to synthesize novel views simultaneously by incorporating additional cross-view attention layers and the shared self-attention mechanism.
  arXiv  Detail & Related papers  (2023-10-12T07:38:28Z)
• Consistent-1-to-3: Consistent Image to 3D View Synthesis via Geometry-aware Diffusion Models [16.326276673056334]
  Consistent-1-to-3 is a generative framework that significantly mitigates this issue. We decompose the NVS task into two stages: (i) transforming observed regions to a novel view, and (ii) hallucinating unseen regions. We propose to employ epipolor-guided attention to incorporate geometry constraints, and multi-view attention to better aggregate multi-view information.
  arXiv  Detail & Related papers  (2023-10-04T17:58:57Z)
• Consistent View Synthesis with Pose-Guided Diffusion Models [51.37925069307313]
  Novel view synthesis from a single image has been a cornerstone problem for many Virtual Reality applications. We propose a pose-guided diffusion model to generate a consistent long-term video of novel views from a single image.
  arXiv  Detail & Related papers  (2023-03-30T17:59:22Z)
• Zero-1-to-3: Zero-shot One Image to 3D Object [30.455300183998247]
  We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. Our conditional diffusion model uses a synthetic dataset to learn controls of the relative camera viewpoint. Our method significantly outperforms state-of-the-art single-view 3D reconstruction and novel view synthesis models by leveraging Internet-scale pre-training.
  arXiv  Detail & Related papers  (2023-03-20T17:59:50Z)
• Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction [95.5735805072852]
  We propose a novel density-variational learning framework to improve the robustness of the image dehzing model. Specifically, the dehazing network is optimized under the consistency-regularized framework. Our method significantly surpasses the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-03-29T08:11:04Z)
• IMAGINE: Image Synthesis by Image-Guided Model Inversion [79.4691654458141]
  We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images. We leverage the knowledge of image semantics from a pre-trained classifier to achieve plausible generations. IMAGINE enables the synthesis procedure to simultaneously 1) enforce semantic specificity constraints during the synthesis, 2) produce realistic images without generator training, and 3) give users intuitive control over the generation process.
  arXiv  Detail & Related papers  (2021-04-13T02:00:24Z)
• Consistency Guided Scene Flow Estimation [159.24395181068218]
  CGSF is a self-supervised framework for the joint reconstruction of 3D scene structure and motion from stereo video. We show that the proposed model can reliably predict disparity and scene flow in challenging imagery. It achieves better generalization than the state-of-the-art, and adapts quickly and robustly to unseen domains.
  arXiv  Detail & Related papers  (2020-06-19T17:28:07Z)

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.