Related papers: Bringing Your Portrait to 3D Presence

Bringing Your Portrait to 3D Presence

URL: http://arxiv.org/abs/2511.22553v1
Date: Thu, 27 Nov 2025 15:42:07 GMT
Title: Bringing Your Portrait to 3D Presence
Authors: Jiawei Zhang, Lei Chu, Jiahao Li, Zhenyu Zang, Chong Li, Xiao Li, Xun Cao, Hao Zhu, Yan Lu,
Abstract summary: We present a unified framework for reconstructing animatable 3D human avatars from a single portrait across head, half-body, and full-body inputs.<n>Our method tackles three bottlenecks: pose- and framing-sensitive feature representations, limited scalable data, and unreliable proxy-mesh estimation.
Score: 46.11577347349078
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We present a unified framework for reconstructing animatable 3D human avatars from a single portrait across head, half-body, and full-body inputs. Our method tackles three bottlenecks: pose- and framing-sensitive feature representations, limited scalable data, and unreliable proxy-mesh estimation. We introduce a Dual-UV representation that maps image features to a canonical UV space via Core-UV and Shell-UV branches, eliminating pose- and framing-induced token shifts. We also build a factorized synthetic data manifold combining 2D generative diversity with geometry-consistent 3D renderings, supported by a training scheme that improves realism and identity consistency. A robust proxy-mesh tracker maintains stability under partial visibility. Together, these components enable strong in-the-wild generalization. Trained only on half-body synthetic data, our model achieves state-of-the-art head and upper-body reconstruction and competitive full-body results. Extensive experiments and analyses further validate the effectiveness of our approach.

Related papers

InpaintHuman: Reconstructing Occluded Humans with Multi-Scale UV Mapping and Identity-Preserving Diffusion Inpainting [64.42884719282323]
InpaintHuman is a novel method for generating high-fidelity, complete, and animatable avatars from occluded monocular videos.<n>Our approach employs direct pixel-level supervision to ensure identity fidelity.
arXiv Detail & Related papers (2026-01-05T13:26:02Z)
TriaGS: Differentiable Triangulation-Guided Geometric Consistency for 3D Gaussian Splatting [2.441486089588484]
3D Gaussian Splatting is crucial for real-time novel view synthesis due to its efficiency and ability to render images.<n>This paper introduces a novel method that improves reconstruction by enforcing global geometry consistency through constrained multi-view triangulation.<n>We demonstrate the effectiveness of our method across multiple photorealistic datasets, achieving state-of-the-art results.
arXiv Detail & Related papers (2025-12-06T03:45:39Z)
HBSplat: Robust Sparse-View Gaussian Reconstruction with Hybrid-Loss Guided Depth and Bidirectional Warping [11.035994094874141]
HBSplat is a framework that seamlessly integrates robust structural cues, virtual view constraints, and occluded region completion.<n> HBSplat sets a new state-of-the-art, achieving up to 21.13 dB PSNR and 0.189 LPIPS, while maintaining real-time inference.
arXiv Detail & Related papers (2025-09-29T15:03:31Z)
A Controllable 3D Deepfake Generation Framework with Gaussian Splatting [6.969908558294805]
We propose a novel 3D deepfake generation framework based on 3D Gaussian Splatting.<n>It enables realistic, identity-preserving face swapping and reenactment in a fully controllable 3D space.<n>Our approach bridges the gap between 3D modeling and deepfake synthesis, enabling new directions for scene-aware, controllable, and immersive visual forgeries.
arXiv Detail & Related papers (2025-09-15T06:34:17Z)
Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
arXiv Detail & Related papers (2024-06-17T07:43:53Z)
ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling [96.87575334960258]
ID-to-3D is a method to generate identity- and text-guided 3D human heads with disentangled expressions. Results achieve an unprecedented level of identity-consistent and high-quality texture and geometry generation.
arXiv Detail & Related papers (2024-05-26T13:36:45Z)
High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization [51.878078860524795]
We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views. Our approach enables high-fidelity 3D rendering from a single image, which is promising for various applications of AI-generated 3D content.
arXiv Detail & Related papers (2022-11-28T18:59:52Z)
RiCS: A 2D Self-Occlusion Map for Harmonizing Volumetric Objects [68.85305626324694]
Ray-marching in Camera Space (RiCS) is a new method to represent the self-occlusions of foreground objects in 3D into a 2D self-occlusion map. We show that our representation map not only allows us to enhance the image quality but also to model temporally coherent complex shadow effects.
arXiv Detail & Related papers (2022-05-14T05:35:35Z)
LASOR: Learning Accurate 3D Human Pose and Shape Via Synthetic Occlusion-Aware Data and Neural Mesh Rendering [3.007707487678111]
We propose a framework that synthesizes silhouette and 2D keypoints data and directly regress to the SMPL pose and shape parameters. A neural 3D mesh is exploited to enable silhouette supervision on the fly, which contributes to great improvements in shape estimation. We are among state-of-the-art on the 3DPW dataset in terms of pose accuracy and evidently outperform the rank-1 method in terms of shape accuracy.
arXiv Detail & Related papers (2021-08-01T02:09:16Z)

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