HumanOrbit: 3D Human Reconstruction as 360° Orbit Generation

Fuente: arXiv
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Hauptverfasser: Suzuki, Keito, Chen, Kunyao, Wang, Lei, Du, Bang, Li, Runfa Blark, Liu, Peng, Bi, Ning, Nguyen, Truong
Format: Preprint
Veröffentlicht: 2026
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author Suzuki, Keito
Chen, Kunyao
Wang, Lei
Du, Bang
Li, Runfa Blark
Liu, Peng
Bi, Ning
Nguyen, Truong
author_facet Suzuki, Keito
Chen, Kunyao
Wang, Lei
Du, Bang
Li, Runfa Blark
Liu, Peng
Bi, Ning
Nguyen, Truong
contents We present a method for generating a full 360° orbit video around a person from a single input image. Existing methods typically adapt image-based diffusion models for multi-view synthesis, but yield inconsistent results across views and with the original identity. In contrast, recent video diffusion models have demonstrated their ability in generating photorealistic results that align well with the given prompts. Inspired by these results, we propose HumanOrbit, a video diffusion model for multi-view human image generation. Our approach enables the model to synthesize continuous camera rotations around the subject, producing geometrically consistent novel views while preserving the appearance and identity of the person. Using the generated multi-view frames, we further propose a reconstruction pipeline that recovers a textured mesh of the subject. Experimental results validate the effectiveness of HumanOrbit for multi-view image generation and that the reconstructed 3D models exhibit superior completeness and fidelity compared to those from state-of-the-art baselines.
format Preprint
id arxiv_https___arxiv_org_abs_2602_24148
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle HumanOrbit: 3D Human Reconstruction as 360° Orbit Generation
Suzuki, Keito
Chen, Kunyao
Wang, Lei
Du, Bang
Li, Runfa Blark
Liu, Peng
Bi, Ning
Nguyen, Truong
Computer Vision and Pattern Recognition
We present a method for generating a full 360° orbit video around a person from a single input image. Existing methods typically adapt image-based diffusion models for multi-view synthesis, but yield inconsistent results across views and with the original identity. In contrast, recent video diffusion models have demonstrated their ability in generating photorealistic results that align well with the given prompts. Inspired by these results, we propose HumanOrbit, a video diffusion model for multi-view human image generation. Our approach enables the model to synthesize continuous camera rotations around the subject, producing geometrically consistent novel views while preserving the appearance and identity of the person. Using the generated multi-view frames, we further propose a reconstruction pipeline that recovers a textured mesh of the subject. Experimental results validate the effectiveness of HumanOrbit for multi-view image generation and that the reconstructed 3D models exhibit superior completeness and fidelity compared to those from state-of-the-art baselines.
title HumanOrbit: 3D Human Reconstruction as 360° Orbit Generation
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2602.24148