Review of 3D Human Reconstruction Methods Empowering VR/AR

doi:10.16182/j.issn1004731x.joss.25-1056

Abstract

Abstract:

3D human reconstruction is critical for VR/AR. Early methods relied on multi-view cameras and depth sensors but were costly. Mid-term approaches using parametric human models enabled efficient single-image reconstruction, while implicit neural representations improved fidelity yet suffered from low efficiency. Currently, 3D Gaussian Splatting achieves high accuracy and real-time rendering as a new paradigm. Challenges include detail distortion and limited generalization, and future development will focus on VR/AR integration.

Key words: 3D human reconstruction, VR/AR, parametric human model, implicit neural representation, 3D Gaussian splatting (3DGS) technology

CLC Number:

TP319.9

Zhang Lisha, Huo Yuchi, Ye Qi, Chen Anjun, Guo Shihui, Chen Jiming. Review of 3D Human Reconstruction Methods Empowering VR/AR[J]. Journal of System Simulation, 2026, 38(3): 545-562.

Figures/Tables 6

Fig. 1

Fig. 2

Table 1

Classification and comparison of core technical systems and typical methods for 3D human reconstruction

类别	典型方法	具体案例	核心技术特点	性能指标 (基于H36M/3DPW数据集)
类别	典型方法	具体案例	核心技术特点	PA-MPJPE	MPJPE
基于优化的建模	轮廓对齐方法	基于SCAPE模型的轮廓对齐	通过三维模型投影与图像剪影的几何一致性优化
	人体参数优化	SMPLify^[25]	迭代优化SMPL模型的姿态与形状参数	82.3 (H36M)
	多视觉线索融合	HoloPose^[27]	融合DensePose稠密姿态估计、关键点与分割掩码	50.56 (H36M)	64.28 (H36M)
	深度学习辅助优化	ExemplarFine-Tuning (EFT)^[29]	利用预训练回归器生成隐式先验，在参数邻域内开展无正则项的少量样本微调	44.0 (H36M) 51.6 (3DPW)
	逆运动学方法	HybrIK	将关节旋转解耦为摆动与扭转分量	33.6 (H36M) 45.0 (3DPW)	55.4 (H36M) 74.1 (3DPW)
基于学习的建模	参数化学习	Neural Body Fitting	端到端回归参数化模型参数(如SMPL的姿态θ与形状β参数)	59.9 (H36M)
	非参数化学习	I2L-MeshNet	基于线像素一维热图的高效顶点定位	41.7 (H36M) 58.6 (3DPW)	55.7 (H36M) 93.2 (3DPW)
	回归概率分布	ProHMR	采用条件归一化流，对姿态参数的条件概率分布进行建模	41.2 (H36M) 59.8 (3DPW)
	回归中间表示	DecoMR^[20]	通过IUV图像建立图像与3D网格的密集对应，迁移局部特征并回归位置图	39.3 (H36M) 68.5 (3DPW)	60.6 (H36M)
混合与新兴方法	隐式表示方法	PIFu^[7]	像素对齐隐式函数，从单张图像推断三维表面及纹理
	显隐混合表示	D³-Human	SMPL显式模板+隐式hmSDF分割服装与身体
	3DGS	SplattingAvatar	优化离散高斯元的几何与外观参数，实时渲染
	全身重建	ExPose	预测SMPL-X参数，高分辨率区域裁剪细化，融合专项数据集知识	60.7 (3DPW)	93.4 (3DPW)
网络结构设计	单阶段框架	HMR	基于ResNet全局特征提取 + 迭代误差反馈(IEF)机制优化	56.8 (H36M) 81.3 (3DPW)	88.0 (H36M) 130.0 (3DPW)
	多阶段框架	Zanfir et al.	多阶段框架结合语义信息中间表示，以归一化流实现弱监督三维人体姿态与形状重建	57.1 (3DPW)	90.0 (3DPW)
	多分支框架	DaNet	全局或局部流任务拆分，IUV全局参数预测 + 关键点RoI池化局部优化	42.9 (H36M) 54.8 (3DPW)	54.6 (H36M) 85.5 (3DPW)

Table 1

Fig. 3

Table 2

Table 3

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类型	数据集	帧数	场景数	人数	人数/帧	真实场景	标注类型
渲染数据集	SURREAL^[91]	6.5×10⁶	2 607	145	1		SMPL
	GTA-Human^[92]	1.4×10⁶		>600	1		SMPL
	AGORA^[93]	1.7×10⁴	>350	4 240	5~15		SMPL-X
	THUman2.0^[94]			200	1		SMPL-X
	MultiHuman^[95]			50	1~3		SMPL-X
标记/传感器	HumanEva^[102]	8.0×10⁴	1	4	1
	Human3.6M^[96]	3.6×10⁶	1	11	1		SMPL
	Total Capture^[103]	1.9×10⁶	1	5	1
	3DPW^[97]	>5.1×10⁴	60	7	1~2	􀳫	SMPL
无标记多视角	CMUPanoptic^[99]	1.5×10⁶	1	40	3~8
	MPI-INF-3DHP^[104]	>1.3×10⁶	1	8	1		SMPL
	3DOH50K^[21]	5.16×10⁴	1	-	1		SMPL
	Mirrored-Human^[105]	1.8×10⁶	>200	>200	≥1		SMPL
	MTC^[106]	8.34×10⁶	1	40	1
	EHF^[6]	1.0×10²	1	1	1		SMPL-X
	HUMBI^[100]	1.73×10⁷	1	772	1		SMPL
	ZJU-MoCap^[59]		1	9	1		SMPL-X
伪3D标签	LSP^[107]	2.0×10³			1	􀳫	SMPL
	LSP-Extended^[108]	1.0×10⁴			1	􀳫	SMPL
	MSCOCO^[109]	3.8×10⁴			≥1	􀳫	SMPL
	MPII^[110]	2.492×10⁴	3 913	>40 000	≥1	􀳫	SMPL
	PoseTrack^[111]	6.637 4×10⁴	550	550	>1	􀳫	SMPL
	OCHuman^[112]	4.731×10³		8 110	>1	􀳫	SMPL
	Ubody^[50]	>1.050×10⁶			≥1	􀳫	SMPL-X

传感器比较	主动传感器				被动传感器
传感器比较	运动捕捉系统	深度相机	激光雷达	毫米波雷达	触觉传感器	IMU	RGB相机
探测距离	近	较近	较远	远	很近	很近	一般
室外工作能力	弱	弱	强	强	强	强	强
夜间工作能力	强	强	强	强	强	强	弱
烟雾环境工作能力	弱	弱	弱	强	强	强	一般
雨雪环境工作能力	弱	弱	一般	强	强	强	一般
温度稳定性	一般	强	强	强	弱	弱	强
速度测量能力	弱	弱	弱	强	弱	强	弱
测量精度	很高	高	高	低	低	低	高