ImHead: Implicit 3D Head Modeling

• ImHead is an implicit morphable 3D head modeling framework that utilizes a compact global latent vector and region-specific codes for detailed avatar synthesis.
• It employs a deep neural architecture with spatial weighting and blending networks to fuse local features and accurately reconstruct complex head geometries.
• Its training uses a large-scale dataset and multi-term loss functions to achieve superior reconstruction metrics and enable localized facial editing.

ImHead is an implicit morphable 3D head modeling framework characterized by a deep neural architecture enabling expressive avatar synthesis and localized face editing. It builds upon recent advances in implicit functions and large-scale 3D datasets to overcome traditional morphable models’ limitations in topology and linearity. By adopting a compact latent space for global identity and introducing region-specific latent representations, ImHead offers interpretable and efficient generation, manipulation, and application of full-head 3D avatars.

1. Architectural Foundations and Implicit Representation

ImHead utilizes a compact, entangled global identity latent vector $z_{id}$ and an intermediate decomposition into region-specific latent codes, distinguishing itself from prior approaches that segment the latent space directly and require large, disjoint latent codes for each part. Formally, the decomposition is given by

$$\{ z_{id}^{(j)} \}_{j=0}^K = \mathcal{T}_\theta(z_{id}),$$

where $\mathcal{T}_\theta$ is the decomposition network, and $K$ indexes regions (e.g., nose, chin, ears). Each local code $z_{id}^{(j)}$ conditions its respective region’s implicit network $g_j$, yielding a feature vector for input position $x$ relative to landmark $k_j$: $f_x^{(j)} = g_j(x - k_j, z_{id}^{(j)}).$ A spatial weighting function

$$w(x, k_j) = \frac{\exp(-\| x - k_j \| / \sigma)}{\sum_j \exp(-\| x - k_j \| / \sigma)}$$

fuses local features: $\hat{f}_x = \sum_j w(x, k_j) \cdot f_x^{(j)}.$ The fused result, together with $x$, enters a blending network that regresses the signed distance function (SDF), supporting high-resolution geometry and topology adaptability. An expression deformer $\mathcal{E}_\theta$ further enables backward warping of observed points for canonicalization: $$\Delta x = \mathcal{E}_\theta(x_{obs}, z_{id}, z_{exp}),\quad x_{can} = x_{obs} + \Delta x.$$ This architectural design captures global and fine-local variations with low latent dimensionality and supports direct, interpretable manipulation of facial sub-regions.

2. Dataset Curation and Training Protocols

The authors curated a dataset of 4,000 distinct identities and approximately 50,000 completed full-head scans, drawing from MimicMe and parametric model fitting tools such as FLAME and NPHM. This represents a tenfold increase over prior implicit head datasets, providing diversity across age, gender, expressions, and head shapes. For each scan, dense 3D geometry and key landmarks are acquired.

The training regime involves the minimization of several supervised losses:

• Signed Distance Function reconstruction loss $\mathcal{L}_{rec}$
• Eikonal loss $\mathcal{L}_{eik}$ for smoothness and correct normal computation
• Landmark regression loss $\mathcal{L}_{kpt}$ for correspondence accuracy
• Optional symmetry $\mathcal{L}_{sym}$ and regularization $\mathcal{L}_{reg}$ penalties

The total training objective is

$$\mathcal{L} = \mathcal{L}_{rec} + \mathcal{L}_{eik} + \lambda_{kpt} \mathcal{L}_{kpt} + \lambda_{sym} \mathcal{L}_{sym} + \lambda_{reg} \mathcal{L}_{reg}.$$

This multi-term energy ensures global structure, local detail, and morphable correspondence.

3. Reconstruction Accuracy and Comparative Performance

ImHead demonstrates superior performance in reconstructing identity and expression geometry relative to previous methods. Quantitatively, it achieves lower Chamfer distances, higher normal consistency, and improved F-scores versus parametric and implicit baselines such as NPHM, monoNPHM, NPM, and imFace. Latent space compression (up to $8.5\times$ smaller) is achieved without loss of accuracy.

Experiments on unseen, wild datasets support robustness and generalizability, attributed to dataset scale and diversity. Qualitative results show faithful representation of both coarse head shape and subtle facial features, as well as rich support for extreme expressions and head topology variants.

4. Localized Editing and Interpretable Control

The decomposition into region-specific latent codes and feature fusion enables localized modification of facial and cranial regions. For example, to alter the nose, one adjusts $z_{id}^{(nose)}$, sampling from its latent distribution while holding others fixed. The result is propagated by FusionNet with spatial weights, preserving smooth transitions and avoiding global entanglement.

This facilitates targeted facial editing (Editor’s term: "local latent sculpting") such as region swapping, cross-identity feature transfer, or independent expression control. Applications include cosmetic simulation, digital makeup, stylization, and constrained avatar personalization unattainable with unified global codes.

5. Applications and Practical Implications

ImHead’s interpretable, localized framework and reconstruction fidelity underlie several significant applications:

• Virtual Reality and Gaming: Supports real-time avatar generation and region-based animation, enhancing immersion and expressivity.
• Film and Animation: Enables manageable, efficient facial feature edits for character design, leveraging interpretable control to streamline workflows.
• Clinical and Cosmetic Tools: Permits simulated surgical modification, digital fitting, or targeted facial analysis.
• Research in Expression Synthesis and Morphing: Assists facial motion capture, morphable rigging, and chapter-level face swapping by supporting granular control.

The robustness to diverse inputs and head geometries, together with the large-scale data backing, increase the reliability and adoption potential in production, telepresence, and biomedical fields.

6. Methodological Context and Limitations

ImHead is positioned as a departure from prior strict-topology parametric 3DMMs and implicit models with entangled or excessively large latent codes. Its intermediate region-based latent decomposition establishes a balance between global identity coherence and local editability.

A plausible implication is that expanding the framework to support time-varying identity codes or further hierarchical decomposition may yield finer animation or medical tracking tools. Limitations include the need for high-quality keypoint detection, and the region partitioning may restrict seamless cross-boundary edits if regional topology is not sufficiently flexible.

7. Future Directions

The capacity for regional editing and scalable avatar synthesis points toward integrating multi-modal data (e.g., texture, semantic labels, and temporal dynamics), refining the granularity of region-specific codes, and extending training to larger, more diverse cohorts or in-the-wild populations.

Possible further research focuses include adaptive region definition, unsupervised landmark discovery, or coupling with generative texture models. The approach may also inform advances in personalized neural rendering, affective computing, and domain-adaptive avatar transfer.

In summary, ImHead represents an advancement in large-scale 3D head modeling, characterized by deep implicit functions, compact and interpretable global-to-local latent representations, and support for both accurate synthesis and editable facial control. These technical contributions influence entertainment, research, and clinical applications wherein localized morphable modeling is required.