DINAR: Diffusion Inpainting of Neural Textures for One-Shot Human Avatars

• URL: http://arxiv.org/abs/2303.09375v4
• Date: Sun, 10 Dec 2023 11:09:47 GMT
• Title: DINAR: Diffusion Inpainting of Neural Textures for One-Shot Human Avatars
• Authors: David Svitov, Dmitrii Gudkov, Renat Bashirov, Victor Lempitsky
• Abstract summary: We present an approach for creating realistic rigged fullbody avatars from single RGB images. Our method uses neural textures combined with the SMPL-X body model to achieve photo-realistic quality of avatars. In the experiments, our approach achieves state-of-the-art rendering quality and good generalization to new poses and viewpoints.
• Score: 7.777410338143783
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present DINAR, an approach for creating realistic rigged fullbody avatars from single RGB images. Similarly to previous works, our method uses neural textures combined with the SMPL-X body model to achieve photo-realistic quality of avatars while keeping them easy to animate and fast to infer. To restore the texture, we use a latent diffusion model and show how such model can be trained in the neural texture space. The use of the diffusion model allows us to realistically reconstruct large unseen regions such as the back of a person given the frontal view. The models in our pipeline are trained using 2D images and videos only. In the experiments, our approach achieves state-of-the-art rendering quality and good generalization to new poses and viewpoints. In particular, the approach improves state-of-the-art on the SnapshotPeople public benchmark.

Related papers

• HR Human: Modeling Human Avatars with Triangular Mesh and High-Resolution Textures from Videos [52.23323966700072]
  We present a framework for acquiring human avatars that are attached with high-resolution physically-based material textures and mesh from monocular video. Our method introduces a novel information fusion strategy to combine the information from the monocular video and synthesize virtual multi-view images. Experiments show that our approach outperforms previous representations in terms of high fidelity, and this explicit result supports deployment on common triangulars.
  arXiv  Detail & Related papers  (2024-05-18T11:49:09Z)
• NECA: Neural Customizable Human Avatar [36.69012172745299]
  We introduce NECA, an approach capable of learning versatile human representation from monocular or sparse-view videos. The core of our approach is to represent humans in complementary dual spaces and predict disentangled neural fields of geometry, albedo, shadow, as well as an external lighting.
  arXiv  Detail & Related papers  (2024-03-15T14:23:06Z)
• Human Gaussian Splatting: Real-time Rendering of Animatable Avatars [8.719797382786464]
  This work addresses the problem of real-time rendering of photorealistic human body avatars learned from multi-view videos. We propose an animatable human model based on 3D Gaussian Splatting, that has recently emerged as a very efficient alternative to neural radiance fields. Our method achieves 1.5 dB PSNR improvement over the state-of-the-art on THuman4 dataset while being able to render in real-time (80 fps for 512x512 resolution)
  arXiv  Detail & Related papers  (2023-11-28T12:05:41Z)
• Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization [91.52882218901627]
  We propose a novel method for constructing implicit 3D morphable face models that are both generalizable and intuitive for editing. Our method improves upon photo-realism, geometry, and expression accuracy compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-04T17:58:40Z)
• Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling [28.242591786838936]
  We present a method that enables novel views and novel poses of arbitrary human performers from sparse multi-view images. A key ingredient of our method is a hybrid appearance blending module that combines the advantages of the implicit body NeRF representation and image-based rendering.
  arXiv  Detail & Related papers  (2023-04-10T23:53:28Z)
• RANA: Relightable Articulated Neural Avatars [83.60081895984634]
  We propose RANA, a relightable and articulated neural avatar for the photorealistic synthesis of humans. We present a novel framework to model humans while disentangling their geometry, texture, and also lighting environment from monocular RGB videos.
  arXiv  Detail & Related papers  (2022-12-06T18:59:31Z)
• MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images [60.56518548286836]
  To generate realistic cloth deformations from novel input poses, watertight meshes or dense full-body scans are usually needed as inputs. We propose an approach that can quickly generate realistic clothed human avatars, represented as controllable neural SDFs, given only monocular depth images.
  arXiv  Detail & Related papers  (2021-06-22T17:30:12Z)
• Neural Re-Rendering of Humans from a Single Image [80.53438609047896]
  We propose a new method for neural re-rendering of a human under a novel user-defined pose and viewpoint. Our algorithm represents body pose and shape as a parametric mesh which can be reconstructed from a single image.
  arXiv  Detail & Related papers  (2021-01-11T18:53:47Z)
• SMPLpix: Neural Avatars from 3D Human Models [56.85115800735619]
  We bridge the gap between classic rendering and the latest generative networks operating in pixel space. We train a network that directly converts a sparse set of 3D mesh vertices into photorealistic images. We show the advantage over conventional differentiables both in terms of the level of photorealism and rendering efficiency.
  arXiv  Detail & Related papers  (2020-08-16T10:22:00Z)

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.