HumGen3D Rigged Character Avatars

• HumGen3D Rigged Character systems are advanced generative models that produce fully animatable, high-fidelity 3D human avatars by disentangling pose and appearance in a canonical space.
• They employ precise methodologies including SMPL-guided inverse skinning, signed-distance field geometry with rigorous regularization, and tri-plane rendering for detailed appearance and geometry.
• Applications span single-view reconstruction, re-animation, and real-time rendering, though challenges remain in fine expression control and extreme pose handling.

HumGen3D Rigged Character systems designate a class of generative models capable of producing fully animatable, high-fidelity 3D human avatars directly from 2D observations. These systems—deriving from AvatarGen (Zhang et al., 2022, Zhang et al., 2022)—coherently integrate SMPL-guided canonical mapping, signed-distance field (SDF) geometry proxy, neural deformation networks, adversarial training protocols, and explicit rigging schemes. They depart fundamentally from earlier rigid-body or direct voxel-based methodologies by disentangling human pose and appearance in a canonical space, thereby enabling precise skeletal and skinning extraction suitable for downstream animation, re-targeting, and real-time rendering.

1. Canonical Mapping and SMPL Proxy

HumGen3D pipelines utilize SMPL parametric human models as geometric priors to map 3D query points from observation (posed) space, via a two-stage process:

• Inverse Linear Blend Skinning (LBS): For any query $x$, compute its coarse alignment to canonical space using nearest-neighbor lookups on the SMPL mesh for skin weights $s^*$ and joint transforms $(R_j, t_j)$:

$$x' = T_{\text{IS}}(x, s^*, p) = \sum_j s^*_j (R_j x + t_j)$$

• Residual Deformation: Augment $x'$ with a nonlinear residual $\Delta x$ predicted by an MLP, conditioning on positional embedding, style code from the latent vector $z$, and SMPL parameters $p=(\theta, \beta)$:

$\bar{x} = x' + \Delta x$

This mapping places clothing, identity, and appearance consistently in canonical space, facilitating decoding via tri-plane representations and StyleGAN-like backbones.

2. Signed-Distance Field Geometry and Regularization

Instead of direct volumetric densities, HumGen3D systems predict SDF values as residuals over SMPL mesh distances:

• Coarse SDF Computation: For posed mesh $M=T_{\text{SMPL}}(p)$, calculate $d_0(x|p)$ as the SDF to $M$.
• Residual Prediction: $\Delta d = \mathrm{MLP}_d(F(\bar{x}), d_0)$
• Final SDF: $d(x|z, c, p) = d_0(x|p) + \Delta d$

Rigorous regularization enforces geometric prior consistency, eikonal (unit gradient) constraint, and minimal-surface penalty. The prior loss is:

$$L_{\text{prior}} = \frac{1}{|R|} \sum_{x \in R} w(x|p) \|d(x) - d_0(x|p)\|$$

with $w(x|p)=\exp(-d_0(x|p)^2/\kappa)$ controlling surface localization (Zhang et al., 2022).

3. Tri-Plane Rendering and Differentiable Volume Synthesis

In canonical space, a tri-plane feature field (256×256, 96 channels) encodes appearance and geometry. For each camera ray $R$:

• Sample $N=48$ points $x_i$ along $R$.
• Map $x_i \rightarrow \bar{x}_i$ via canonical transformation.
• Query tri-plane features at $\bar{x}_i$, decode to $(f_i, d_i)$ (appearance, SDF).
• Convert SDF $d_i$ to volume density $$\sigma_i = \frac{1}{\alpha} \mathrm{Sigmoid}(-d_i/\alpha)$$.
• Compositing via volume rendering:

$$I(R) = \sum_i \left( \prod_{j < i} e^{-\sigma_j \Delta_j} \right) (1 - e^{-\sigma_i \Delta_i}) f_i$$

• Final image super-resolved through StyleGAN2 decoder.

This yields high-resolution ($512^2$) outputs preserving cloth wrinkles, multi-view consistency, and smooth articulation.

4. Neural Deformation for Non-Rigid Dynamics

Modeling fine-grained geometric details and pose-dependent cloth dynamics proceeds via a deformation network:

• Use sinusoidal positional embedding, style latent, and SMPL parameters.
• Predict residual offsets $\Delta x$ for each point sampled in observation space.
• Impose a deformation regularizer to constrain residual magnitudes:

$L_{\text{deform}} = \sum_x \|\Delta x(x)\|_1$

This non-rigid extension is critical for plausible garment warping, hair motion, and realistic occlusions under animation.

5. Adversarial Training and Losses

End-to-end training combines multiple objectives:

• GAN Loss: Non-saturating, dual-branch discriminator conditioned on $(c, p)$—one on low-res features, another on high-res images.
• R1 Regularization: Gradient penalty on real images to stabilize learning.
• Eikonal and Minimal Surface Losses: Enforce geometrical regularity and suppress ghost surfaces.
• SMPL Prior Regularization: Drives generated geometry toward SMPL proxy expectation near surfaces.
• Face Discriminator: Cropped patch discriminator at $80\times80$ improves facial details (Zhang et al., 2022).

Total loss aggregates all terms with calibrated $\lambda$-weights: $$L_{\text{total}} = L_{\text{GAN}} + \lambda_\text{Reg}L_\text{Reg} + \lambda_\text{eik}L_\text{eik} + \lambda_\text{mins}L_\text{mins} + \lambda_\text{prior}L_\text{prior} + \lambda_\text{deform}L_\text{deform}$$.

6. Rigging, Animation, and Applications

Rigging is realized by transferring SMPL skinning weights to mesh vertices through nearest-neighbor assignment post–isosurface extraction (Marching Cubes) over the SDF field. Animation proceeds by:

• Sampling new $(\theta', \beta')$ parameters to drive pose and shape.
• Applying the mapping $T(x|p')$ for arbitrary viewpoint $c'$ and pose changes.
• Ensuring identity and appearance consistency as they are encoded in canonical space.

Applications demonstrated include single-view reconstruction, re-animation, text-guided editing, and export to real-time rendering systems. Quantitative results (DeepFashion, MPV, UBC, SHHQ) confirm strong performance: FID = 7.68 (vs. StyleNeRF ≈ 15, EG3D ≈ 14.4), FaceFID = 8.76, depth-MSE = 0.433, PCK ≈ 99.2% (Zhang et al., 2022).

7. Limitations and Prospective Extensions

While HumGen3D establishes state-of-the-art for generative rigged human avatars, several constraints remain:

• Dependence on accurate SMPL estimation; upstream 2D pose errors propagate to avatar geometry.
• SMPL lacks fine expression and hand articulation; upgrading to SMPL-X or MANO can ameliorate micro-expression control.
• Extreme poses and non-static garments (e.g., skirts, capes) may require additional blend-shape networks or physics priors.
• Temporal coherence in video animations may benefit from recurrent deformation networks or explicit smoothness penalties.

The design enables integration of refinement loops for SMPL parameter estimation, multi-modal mesh generation, and robust animation control, supporting further research into expressive, production-quality human avatar synthesis.