arXiv Computer Vision
Photorealistic Novel View Synthesis of Human Faces using Next-Scale Transformers
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Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multi-View Captures
arXiv:2605. 04035v3 Announce Type: replace-cross Abstract: We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups.
arXiv Computer Vision
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FiCA: Feed-forward instant Gaussian Codec Avatars from a Single Portrait Image
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PoseDreamer: Scalable and Photorealistic Human Data Generation Pipeline with Diffusion Models
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arXiv Computer Vision
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An Evaluation Framework for Generating Multi-View Images of a Person in a Scene
The paper introduces a framework for generating multi‑view images of a person within a natural scene, addressing the scarcity of paired multi‑view datasets for human subjects. It evaluates existing diffusion‑based image‑editing models and finds they often hallucinate head‑turn angles, leading to inconsistent backgrounds. To overcome this, the authors propose the Head Scene Rotation Difference (HSRD) metric, which separates camera movement from head pose changes and enables reliable assessment of 3D spatial parallax for constructing high‑quality synthetic datasets.
arXiv Computer Vision
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GTA: Advancing Image-to-3D World Generation via Geometry Then Appearance Video Diffusion
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TIGER: Taming Identity, Geometry, and Generative Priors for High-Quality Face Video Restoration
Face Video Restoration (FVR) aims to recover high-fidelity facial videos from degraded input while preserving identity and semantic consistency across frames. Existing methods often struggle to simultaneously address three key challenges: identity shift, viewpoint-entangled guidance, and perceptual realism.