InfiniHumanGen: Infinite 3D Human & Genome Models

Updated 25 November 2025

InfiniHumanGen is a framework that integrates 3D avatar synthesis, genetic simulation, and spatial gene-expression modeling to generate infinite human variations.
It employs a conditional diffusion pipeline with multi-modal annotations and cross-attention mechanisms to achieve precise control and high visual fidelity.
Benchmarking and simulations demonstrate its state-of-the-art performance in geometric consistency, runtime efficiency, and long-term genetic sustainability.

InfiniHumanGen refers to a set of methodologies, models, and frameworks designed to enable the scalable, controllable, and precise synthesis, simulation, and analysis of human beings in digital and computational domains. The term spans three principal areas: (1) multi-modal 3D human avatar synthesis with fine-grained attribute control, (2) long-term genetic management and simulation of multi-generational human populations, and (3) conditional super-resolution modeling of gene expression and other high-dimensional biological properties. The concept is instantiated in leading frameworks for 3D avatar generation, genome-informed demographic forecasting, and spatial gene expression modeling, all supporting effectively "infinite" diversity and control.

1. Conditional Diffusion Pipeline for 3D Human Generation

InfiniHumanGen, as introduced in "InfiniHuman: Infinite 3D Human Creation with Precise Control" (Xue et al., 13 Oct 2025), employs a conditional diffusion pipeline to generate high-quality, controllable 3D human avatars. The core technical specification is a forward noising process: $q(x_t|x_{t-1}) = \mathcal{N}\bigl(x_t; \sqrt{\alpha_t}\,x_{t-1},\,\beta_t I\bigr),\qquad \bar\alpha_t = \prod_{s=1}^t \alpha_s$ Accompanied by a reverse, conditioned denoising process: $p_\theta(x_{t-1}|x_t, c) = \mathcal{N}\bigl(x_{t-1}; \mu_\theta(x_t, c),\,\tilde\beta_t I\bigr)$ with the mean given as: $\mu_\theta(x_t, c) = \tfrac{1}{\sqrt{\alpha_t}}\Bigl(x_t - \frac{\beta_t}{\sqrt{1-\bar\alpha_t}}\,\epsilon_\theta(x_t, c)\Bigr)$ The standard denoising score matching loss is extended with specialized objectives to enable modality-specific conditioning and improved controllability:

Virtual-TryOff loss for garment extraction
Multi-View Diffusion (MVD) loss for spatial consistency across views
High-resolution flow-matching loss (Gen-HRes) for 2D-to-3D geometric fidelity

Two primary architectures are implemented:

Gen-Schnell: SD-style U-Net integrated with text (CLIP), shape (SMPL normal-map via VAE), and clothing (VAE) embeddings, using channelwise concatenation and zero-initialization for stability.
Gen-HRes: Transformer-based OminiControl2 network with spatial–nonspatial cross-attention and dynamic positional tokens, outputting orthographic high-resolution views for mesh reconstruction.

A key algorithmic feature is the integration of 3D Gaussian Splat (“3DGS”) rendering into the diffusion reverse process, conferring geometric consistency across views and modalities.

InfiniHumanData, the foundation for InfiniHumanGen's generative capacity, comprises 111,000 automatically generated human identities spanning a broad distribution of ethnicity, age, clothing, and shape. Each identity is richly annotated with:

10 multigranularity text descriptions (ranging from full-sentence to five-word summaries) produced via hierarchical GPT-4o summarization.
Uniformly lit, orthographic multi-view RGB renders produced by fine-tuned FLUX and MV-Diffusion models.
High-resolution garment images generated by Instruct-Virtual-TryOff and selected for quality using an LLM-based judge.
SMPL shape and pose parameters refined by NLF and OpenPose.

Negative garment samples are explicitly filtered in a four-output sampling and LLM selection loop. The aggregate pipeline leverages foundation models, achieving per-identity synthesis at a practical cost of ≈$0.03 (Xue et al., 13 Oct 2025). The dataset’s realism is validated by a blinded paper with 765 of 1,511 votes classifying synthetic images as indistinguishable from scan renderings.

3. Quantitative Performance and Comparative Analysis

Benchmarking demonstrates that InfiniHumanGen achieves state-of-the-art results across multiple axes: visual quality, attribute alignment, runtime, and fidelity. Representative metrics measured on both the Gen-Schnell and Gen-HRes pipelines versus prior baseline methods are shown in the following table (rounded reproductions):

Method	Quality↑ (%)	FID↓	CLIP↑	Runtime
Gen-HRes	92.4	82.3	30.43	4 min
Gen-Schnell	77.1	100.4	30.82	12.9 s
MVDream	20.8	141.3	30.37	2.8 s
SPAD	2.0	150.4	28.58	13.9 s
HumanNorm	2.5	101.8	28.30	117 min
DreamAvatar	1.3	151.6	28.42	384 min

Gen-HRes establishes leading performance in user-rated realism, fidelity, and attribute controllability (Xue et al., 13 Oct 2025). The pipeline achieves 12 s end-to-end for 3D Gaussian-splat avatars (Gen-Schnell) and approximately 4 min for full-resolution texture mesh (Gen-HRes).

4. Precise Attribute Control, Scalability, and Modality Fusion

A central distinguishing property of InfiniHumanGen is the ability to generate avatars with precise control of clothing, shape, text-described identity, and other attributes. Direct image-level cloth conditioning enables true identity-preserving virtual try-on. SMPL parameters provide explicit control over geometry, while multi-granularity captions enable fine, editable variation in age, ethnicity, and accessories through cross-attention and seed control (Xue et al., 13 Oct 2025).

Infinite scalability is provided by the fully automated data pipeline: new identities, styles, and body shapes can be synthesized without manual effort. The modeling framework supports the ingestion of arbitrary text, shape, and clothing modalities—made possible by model fusion strategies such as zero-initialized input expansion and cross-attention blocks.

5. Genetic and Population Simulation via InfiniHumanGen Principles

In contrast to the generative diffusion frameworks, “InfiniHumanGen” also addresses the challenge of genetically managing multi-generational human populations within confined environments, such as interstellar spacecraft. Here, the HERITAGE Monte-Carlo agent-based simulation represents each diploid genome as a vector of 2,110 integer loci, incorporating:

Poisson-modeled chromosomal crossover during meiosis,
Chronic radiation-induced mutagenesis with exponential-saturation doseresponse,
Unilateral gene conversion events,
Recombinant fusion of gametes (Marin et al., 2021).

Key simulation results demonstrate maintenance of <2% heterozygosity loss over 600 years (N≥100), minimal differentiation (Nei’s distance ⟨D_A⟩≈0.005) for Earth-level radiation (d=0.3 mSv/yr), and the requirement of N_e≳500 for truly indefinite missions. Engineering guidelines specify annual dose caps, genome-wide health checks, and controlled cryo-repository interventions as operating margins for “infinite” genetic sustainability.

6. Modeling High-Dimensional Human Properties: Conditional Implicit Representation

InfiniHumanGen as a concept is further reflected in spatial gene-expression modeling frameworks that map arbitrarily resolved measures (e.g., gene expression) across the 3D human brain via implicit neural representations (INRs) (Yu et al., 11 Jun 2025). The salient features are:

Input: 3D location, anatomical class, gene descriptor
Architecture: 12-layer SIREN MLP with high-frequency sinusoidal input encoding
Output: predicted normalized expression at micron–millimeter precision
Loss: mean square error over observed microarray sites
Conditioning: through Laplacian-eigenvector-encoded gene descriptors and multi-modal anatomical information

A plausible implication is that a fully realized InfiniHumanGen system could accept arbitrary biological or phenotypic descriptors as conditioning vectors for synthesizing plausible high-dimensional “tissue” or “organism” realizations, beyond visual appearance.

7. Innovations, Practical Implications, and Future Directions

InfiniHumanGen establishes a generalizable paradigm for human avatar synthesis, genetic/demographic simulation, and high-dimensional biophysical field modeling. The principal technical advances across these implementations are:

Modality fusion with cross-attention and channelwise zero-initialized inputs
Multi-view and geometric consistency losses, particularly via 3D Gaussian Splat integration
Flow-matching losses for image- and volume-based generation
Automated, foundation-model-driven data annotation pipelines enabling effective “infinite” scale

The implications for synthetic biology, creative media, virtual try-on, and human population forecasting are substantive, with released codebases and pipelines (e.g., https://yuxuan-xue.com/infini-human, https://github.com/vsingh-group/gene-expression-inr) providing reproducibility and extensibility. Future work is likely to focus on extending modality coverage (e.g., physiological, behavioral, or pathological attributes), integrating richer causal and dynamical models, and resolving open questions in the interpretability and biorealism of generated outputs (Xue et al., 13 Oct 2025, Marin et al., 2021, Yu et al., 11 Jun 2025).