BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesis

• URL: http://arxiv.org/abs/2311.05521v2
• Date: Tue, 28 Nov 2023 15:31:46 GMT
• Title: BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesis
• Authors: Hao-Bin Duan, Miao Wang, Jin-Chuan Shi, Xu-Chuan Chen and Yan-Pei Cao
• Abstract summary: We introduce BakedAvatar, a novel representation for real-time neural head avatar. Our approach extracts layered meshes from learned isosurfaces of the head and computes expression-, pose-, and view-dependent appearances. Experimental results demonstrate that our representation generates photorealistic results of comparable quality to other state-the-art methods.
• Score: 7.485318043174123
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthesizing photorealistic 4D human head avatars from videos is essential for VR/AR, telepresence, and video game applications. Although existing Neural Radiance Fields (NeRF)-based methods achieve high-fidelity results, the computational expense limits their use in real-time applications. To overcome this limitation, we introduce BakedAvatar, a novel representation for real-time neural head avatar synthesis, deployable in a standard polygon rasterization pipeline. Our approach extracts deformable multi-layer meshes from learned isosurfaces of the head and computes expression-, pose-, and view-dependent appearances that can be baked into static textures for efficient rasterization. We thus propose a three-stage pipeline for neural head avatar synthesis, which includes learning continuous deformation, manifold, and radiance fields, extracting layered meshes and textures, and fine-tuning texture details with differential rasterization. Experimental results demonstrate that our representation generates synthesis results of comparable quality to other state-of-the-art methods while significantly reducing the inference time required. We further showcase various head avatar synthesis results from monocular videos, including view synthesis, face reenactment, expression editing, and pose editing, all at interactive frame rates.

Related papers

• GaussianHeads: End-to-End Learning of Drivable Gaussian Head Avatars from Coarse-to-fine Representations [54.94362657501809]
  We propose a new method to generate highly dynamic and deformable human head avatars from multi-view imagery in real-time. At the core of our method is a hierarchical representation of head models that allows to capture the complex dynamics of facial expressions and head movements. We train this coarse-to-fine facial avatar model along with the head pose as a learnable parameter in an end-to-end framework.
  arXiv  Detail & Related papers  (2024-09-18T13:05:43Z)
• 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)
• FaceFolds: Meshed Radiance Manifolds for Efficient Volumetric Rendering of Dynamic Faces [21.946327323788275]
  3D rendering of dynamic face is a challenging problem. We present a novel representation that enables high-quality rendering of an actor's dynamic facial performances.
  arXiv  Detail & Related papers  (2024-04-22T00:44:13Z)
• FLARE: Fast Learning of Animatable and Relightable Mesh Avatars [64.48254296523977]
  Our goal is to efficiently learn personalized animatable 3D head avatars from videos that are geometrically accurate, realistic, relightable, and compatible with current rendering systems. We introduce FLARE, a technique that enables the creation of animatable and relightable avatars from a single monocular video.
  arXiv  Detail & Related papers  (2023-10-26T16:13:00Z)
• Learning Neural Duplex Radiance Fields for Real-Time View Synthesis [33.54507228895688]
  We propose a novel approach to distill and bake NeRFs into highly efficient mesh-based neural representations. We demonstrate the effectiveness and superiority of our approach via extensive experiments on a range of standard datasets.
  arXiv  Detail & Related papers  (2023-04-20T17:59:52Z)
• GM-NeRF: Learning Generalizable Model-based Neural Radiance Fields from Multi-view Images [79.39247661907397]
  We introduce an effective framework Generalizable Model-based Neural Radiance Fields to synthesize free-viewpoint images. Specifically, we propose a geometry-guided attention mechanism to register the appearance code from multi-view 2D images to a geometry proxy.
  arXiv  Detail & Related papers  (2023-03-24T03:32:02Z)
• Cascaded and Generalizable Neural Radiance Fields for Fast View Synthesis [35.035125537722514]
  We present CG-NeRF, a cascade and generalizable neural radiance fields method for view synthesis. We first train CG-NeRF on multiple 3D scenes of the DTU dataset. We show that CG-NeRF outperforms state-of-the-art generalizable neural rendering methods on various synthetic and real datasets.
  arXiv  Detail & Related papers  (2022-08-09T12:23:48Z)
• DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars [92.37436369781692]
  We present DRaCoN, a framework for learning full-body volumetric avatars. It exploits the advantages of both the 2D and 3D neural rendering techniques. Experiments on the challenging ZJU-MoCap and Human3.6M datasets indicate that DRaCoN outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T17:59:15Z)
• HVTR: Hybrid Volumetric-Textural Rendering for Human Avatars [65.82222842213577]
  We propose a novel neural rendering pipeline, which synthesizes virtual human avatars from arbitrary poses efficiently and at high quality. First, we learn to encode articulated human motions on a dense UV manifold of the human body surface. We then leverage the encoded information on the UV manifold to construct a 3D volumetric representation.
  arXiv  Detail & Related papers  (2021-12-19T17:34:15Z)
• Neural Head Avatars from Monocular RGB Videos [0.0]
  We present a novel neural representation that explicitly models the surface geometry and appearance of an animatable human avatar. Our representation can be learned from a monocular RGB portrait video that features a range of different expressions and views.
  arXiv  Detail & Related papers  (2021-12-02T19:01:05Z)
• Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction [9.747648609960185]
  We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face. Especially, for telepresence applications in AR or VR, a faithful reproduction of the appearance including novel viewpoints or head-poses is required.
  arXiv  Detail & Related papers  (2020-12-05T16:01:16Z)

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.