Learning 3D Photography Videos via Self-supervised Diffusion on Single Images

• URL: http://arxiv.org/abs/2302.10781v1
• Date: Tue, 21 Feb 2023 16:18:40 GMT
• Title: Learning 3D Photography Videos via Self-supervised Diffusion on Single Images
• Authors: Xiaodong Wang, Chenfei Wu, Shengming Yin, Minheng Ni, Jianfeng Wang, Linjie Li, Zhengyuan Yang, Fan Yang, Lijuan Wang, Zicheng Liu, Yuejian Fang, Nan Duan
• Abstract summary: 3D photography renders a static image into a video with appealing 3D visual effects. Existing approaches typically first conduct monocular depth estimation, then render the input frame to subsequent frames with various viewpoints. We present a novel task: out-animation, which extends the space and time of input objects.
• Score: 105.81348348510551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D photography renders a static image into a video with appealing 3D visual effects. Existing approaches typically first conduct monocular depth estimation, then render the input frame to subsequent frames with various viewpoints, and finally use an inpainting model to fill those missing/occluded regions. The inpainting model plays a crucial role in rendering quality, but it is normally trained on out-of-domain data. To reduce the training and inference gap, we propose a novel self-supervised diffusion model as the inpainting module. Given a single input image, we automatically construct a training pair of the masked occluded image and the ground-truth image with random cycle-rendering. The constructed training samples are closely aligned to the testing instances, without the need of data annotation. To make full use of the masked images, we design a Masked Enhanced Block (MEB), which can be easily plugged into the UNet and enhance the semantic conditions. Towards real-world animation, we present a novel task: out-animation, which extends the space and time of input objects. Extensive experiments on real datasets show that our method achieves competitive results with existing SOTA methods.

Related papers

• DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features [65.8738034806085]
  DistillNeRF is a self-supervised learning framework for understanding 3D environments in autonomous driving scenes. Our method is a generalizable feedforward model that predicts a rich neural scene representation from sparse, single-frame multi-view camera inputs.
  arXiv  Detail & Related papers  (2024-06-17T21:15:13Z)
• ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models [65.22994156658918]
  We present a method that learns to generate multi-view images in a single denoising process from real-world data. We design an autoregressive generation that renders more 3D-consistent images at any viewpoint.
  arXiv  Detail & Related papers  (2024-03-04T07:57:05Z)
• Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives [70.32817882783608]
  We present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. Unlike existing primitive decomposition methods that rely on 3D input data, our approach operates directly on images. We show that the resulting textured primitives faithfully reconstruct the input images and accurately model the visible 3D points.
  arXiv  Detail & Related papers  (2023-07-11T17:58:31Z)
• Blocks2World: Controlling Realistic Scenes with Editable Primitives [5.541644538483947]
  We present Blocks2World, a novel method for 3D scene rendering and editing. Our technique begins by extracting 3D parallelepipeds from various objects in a given scene using convex decomposition. The next stage involves training a conditioned model that learns to generate images from the 2D-rendered convex primitives.
  arXiv  Detail & Related papers  (2023-07-07T21:38:50Z)
• ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections [71.46546520120162]
  Estimating 3D articulated shapes like animal bodies from monocular images is inherently challenging. We propose ARTIC3D, a self-supervised framework to reconstruct per-instance 3D shapes from a sparse image collection in-the-wild. We produce realistic animations by fine-tuning the rendered shape and texture under rigid part transformations.
  arXiv  Detail & Related papers  (2023-06-07T17:47:50Z)
• FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow [26.528667940013598]
  Reconstruction of 3D neural fields from posed images has emerged as a promising method for self-supervised representation learning. Key challenge preventing the deployment of these 3D scene learners on large-scale video data is their dependence on precise camera poses from structure-from-motion. We propose a method that jointly reconstructs camera poses and 3D neural scene representations online and in a single forward pass.
  arXiv  Detail & Related papers  (2023-05-31T20:58:46Z)
• RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation [68.06991943974195]
  We present RenderDiffusion, the first diffusion model for 3D generation and inference, trained using only monocular 2D supervision. We evaluate RenderDiffusion on FFHQ, AFHQ, ShapeNet and CLEVR datasets, showing competitive performance for generation of 3D scenes and inference of 3D scenes from 2D images.
  arXiv  Detail & Related papers  (2022-11-17T20:17:04Z)
• Using Adaptive Gradient for Texture Learning in Single-View 3D Reconstruction [0.0]
  Learning-based approaches for 3D model reconstruction have attracted attention owing to its modern applications. We present a novel sampling algorithm by optimizing the gradient of predicted coordinates based on the variance on the sampling image. We also adopt Frechet Inception Distance (FID) to form a loss function in learning, which helps bridging the gap between rendered images and input images.
  arXiv  Detail & Related papers  (2021-04-29T07:52:54Z)

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.