3DFroMLLM: Language-Driven 3D Modeling

Updated 8 July 2026

3DFroMLLM is a research program that merges language models with direct 3D data processing for scene understanding, object generation, and geometric reasoning.
It employs diverse strategies—from encoder-free tokenization to text-serialized 3D meshes—to tackle challenges like unordered point clouds and long token sequences.
Key systems such as Fase3D, CG-MLLM, MeshLLM, and FloraForge illustrate trade-offs in efficiency, fidelity, and control, shaping future 3D-language integration.

In the cited literature, “3DFroMLLM” denotes the broader effort to make language-centered models generate, perceive, ground, or reason over 3D structure directly, rather than treating 3D as a purely external module. The term is used for encoder-free 3D scene LMMs, native multimodal 3D generators, single-image 3D perception systems, text-conditioned RGBD diffusion, text-serialized mesh models, LLM-assisted procedural modeling, and multimodal representation-transfer systems (Mei et al., 26 Feb 2026, Huang et al., 29 Jan 2026, Yang et al., 2024, Stan et al., 2023, Fang et al., 2 Aug 2025, Hadadi et al., 11 Dec 2025, Ji et al., 2023). This suggests that 3DFroMLLM functions less as a single architecture than as a research program spanning scene understanding, object generation, geometry regression, captioning, grounding, and analysis-ready modeling.

1. Conceptual scope

Across recent work, 3DFroMLLM is motivated by a common mismatch between the inductive biases of LLMs or MLLMs and the structure of 3D data. Point clouds are unordered sets and may contain thousands of elements, so naive serialization breaks permutation invariance and quadratic self-attention becomes prohibitive. Meshes preserve topology but become long token streams when serialized into text. Monocular 3D perception requires numeric geometric outputs and robustness to camera intrinsics, especially focal length variation. Procedural geometric modeling offers editability but usually requires specialized expertise in geometric programming. These recurring obstacles define the field’s problem space rather than any single benchmark or modality (Mei et al., 26 Feb 2026, Fang et al., 2 Aug 2025, Yang et al., 2024, Hadadi et al., 11 Dec 2025).

The literature therefore treats 3DFroMLLM in several distinct but related senses. In one sense, it means building a 3D LMM that consumes point clouds or scene tokens directly, as in Fase3D and JM3D-LLM. In another, it means embedding 3D generation natively inside a multimodal LLM, as in CG-MLLM and MeshLLM. In a broader “3D from language” sense, it also includes RGBD diffusion systems such as LDM3D and application-specific co-design systems such as FloraForge, even when they do not use an LLM as the core generative engine (Mei et al., 26 Feb 2026, Huang et al., 29 Jan 2026, Stan et al., 2023, Hadadi et al., 11 Dec 2025).

A common misconception is that 3DFroMLLM refers only to text-to-3D mesh generation. The cited work shows a much wider range of outputs: compact scene tokens for QA, 3D boxes and depth estimates, RGBD images, watertight meshes, OBJ-like vertex-face streams, and hierarchical B-spline plant surfaces (Mei et al., 26 Feb 2026, Yang et al., 2024, Stan et al., 2023, Huang et al., 29 Jan 2026, Fang et al., 2 Aug 2025, Hadadi et al., 11 Dec 2025).

2. Representational regimes

A central axis of variation is the representation of 3D structure. Some systems preserve geometric continuity through latent spaces or spline surfaces; others reduce 3D to token sequences that can be handled by standard decoders. The design choice determines which inductive biases must be supplied explicitly: locality-preserving serialization, graph smoothing, virtual-camera normalization, or procedural constraints.

System	Representation or output	Characteristic mechanism
Fase3D	Structured superpoints and compact 3D tokens	SFC serialization, FFT/DCT mixing, graph-based token merging, Fourier-augmented LoRA
CG-MLLM	Spatial-VAE latent sequence decoded to watertight meshes	Mixture-of-Transformer with TokenAR and BlockAR
LLMI3D	3D box center, dimensions, orientation, and depth	3D query token, regression heads, virtual-camera depth normalization
LDM3D	6-channel RGBD image split into RGB and 16-bit depth	KL-regularized latent diffusion with frozen CLIP-text encoder
MeshLLM	OBJ-like text-serialized vertices and faces	Primitive-Mesh decomposition, vertex-to-face prediction, local mesh assembly
FloraForge	Hierarchical B-spline surfaces and SMESH metadata	Plant Refinements, Procedural Plant Generator, Plant Descriptor
JM3D-LLM	Point tokens projected into Vicuna token space	SMO, JMA, projector MLP

These regimes expose a major fault line in the field. Some approaches seek “native” 3D language modeling by aligning a language backbone to a specialized 3D latent space, as in CG-MLLM. Others insist on compatibility with a standard tokenizer and decoder-only LLM, as in MeshLLM. Still others preserve a separate geometric representation and use the LLM primarily as an alignment or reasoning layer, as in JM3D-LLM, LLMI3D, and FloraForge (Huang et al., 29 Jan 2026, Fang et al., 2 Aug 2025, Ji et al., 2023, Yang et al., 2024, Hadadi et al., 11 Dec 2025).

3. Point-cloud scene reasoning and multimodal alignment

One major strand of 3DFroMLLM targets scene-level reasoning from raw 3D input. Fase3D explicitly asks how to design an LMM that tokenizes unordered 3D data effectively and efficiently without a cumbersome encoder, and answers with an encoder-free Fourier-based 3D scene LMM. The pipeline computes point-level tokens from a shallow MLP and non-parametric Fourier positional embeddings, pools them into structured superpoints, serializes superpoint centers with Hilbert and Morton/Z-order curves plus transposed variants, applies windowed FFT/DCT mixing with learnable non-negative gates, constructs a sparse superpoint graph via window voting and Euclidean re-ranking, and merges tokens through normalized adjacency smoothing, low-rank SVD, and entropy-regularized optimal transport. The FFT enhancer is written as

$S'(\pi_i)=\mathrm{iFFT}\!\left(\mathrm{FFT}(S(\pi_i))\odot G\right),$

with multi-curve fusion followed by residual addition. The resulting tokenizer operates at $O(M \log M)$ per curve, while the practical windowed variant reduces to $O(M)$ per curve; empirically, the tokenizer activates approximately $2.04$ or $2.09$ FLOP, versus $37.75$ to $163.38$ for encoder-based baselines, while using only $10.54\text{M}$ or $12.11\text{M}$ vision-side parameters. On ScanQA val, Fase3D with Qwen2.5-3B reaches $R\ 42.56$ , $O(M \log M)$ 0, $O(M \log M)$ 1, $O(M \log M)$ 2, $O(M \log M)$ 3, and with Vicuna-7B reaches $O(M \log M)$ 4, $O(M \log M)$ 5, $O(M \log M)$ 6, $O(M \log M)$ 7, $O(M \log M)$ 8, remaining comparable to 3D-LLaVA at much lower visual parameter and FLOP budgets (Mei et al., 26 Feb 2026).

The same branch also includes representation-transfer systems that keep a 3D encoder but improve how its features are aligned to language. JM3D argues that naive CLIP-style transfer degrades information by relying on single-view images and generic texts, and addresses this with the Structured Multimodal Organizer and Joint Multi-modal Alignment. SMO builds a Continuous Image Sequence from RGB and depth renderings at $O(M \log M)$ 9 intervals and a Hierarchical Text Tree with parent and subcategory labels; JMA then aligns 3D features to a joint image-text target

$O(M)$ 0

JM3D-LLM projects 64 point tokens into the Vicuna 7B embedding space and inserts them at a special placeholder token. On ModelNet40 with PointMLP, JM3D improves zero-shot top-1 from $O(M)$ 1 to $O(M)$ 2 on the All split, from $O(M)$ 3 to $O(M)$ 4 on Medium, and from $O(M)$ 5 to $O(M)$ 6 on Hard; on ScanObjectNN, PointMLP + JM3D reaches $O(M)$ 7 top-1 versus $O(M)$ 8 for PointMLP + ULIP. This line of work shows that 3DFroMLLM need not eliminate 3D encoders; it can also mean improving multimodal pretraining so that 3D features become more useful to an LLM (Ji et al., 2023).

A second misconception is that “encoder-free” and “language-native” are synonymous. Fase3D eliminates dedicated 3D encoders entirely, but does so by introducing strong geometric structure through superpoints, space-filling curves, spectral smoothing, and Fourier-domain adaptation. JM3D-LLM takes the opposite route, retaining point-cloud encoders and focusing on richer joint supervision before LLM insertion (Mei et al., 26 Feb 2026, Ji et al., 2023).

4. Native 3D generation inside multimodal LLMs

A distinct research line seeks to make 3D generation a first-class operation of an MLLM rather than an external downstream module. CG-MLLM is presented as the first system to perform end-to-end high-resolution 3D generation natively inside the LLM framework. Its backbone is a Mixture-of-Transformer architecture in which TokenAR handles token-level content and BlockAR handles block-level spatial content under a hybrid masking regime called Shared Causal–Parallel Self-Attention. The model is initialized from Qwen3-VL, integrates Hunyuan3D-2.1’s Spatial-VAE latent space, omits intra-block positional embeddings for 3D tokens to preserve permutation invariance, and progressively scales from 512 to 4096 spatial tokens. The spatial autoregressive factorization is

$O(M)$ 9

with within-block parallel prediction. At 4096 spatial tokens, BlockAR yields roughly a $2.04$0 speedup over purely token-level processing. Quantitatively, CG-MLLM reports best performance among the compared MLLM-based 3D generators with $2.04$1-FID $2.04$2, $2.04$3-KID $2.04$4, CLIP-IQA+ $2.04$5, MUSIQ $2.04$6, Uni3D $2.04$7, and CLIP $2.04$8; for image-input captioning it reports BLEU-1 $2.04$9, ROUGE-L $2.09$0, and METEOR $2.09$1 (Huang et al., 29 Jan 2026).

MeshLLM pursues a more literal language-modeling strategy by serializing meshes directly into text. A mesh is written as $2.09$2 with OBJ-like vertex and face lists, where vertex coordinates are quantized to integers in $2.09$3, vertices are sorted by $2.09$4–$2.09$5–$2.09$6, and faces are sorted by the smallest vertex index. The model avoids custom encoders and instead addresses long-sequence and topology-loss problems through Primitive-Mesh decomposition, vertex-to-face prediction, and local mesh assembly training. KNN-based decomposition produces more than $2.09$7 Primitive-Mesh samples, almost 50 times larger than previous methods tailored to LLM token limits; semantic decomposition yields $2.09$8 semantically labeled parts. MeshLLM fine-tunes all parameters of LLaMA-8B-Instruct with an 8192-token context window and standard language modeling objectives for four tasks: vertex-to-face prediction, mesh assembly, mesh understanding, and mesh generation. On Objaverse-XL understanding, it reports BLEU-1 $2.09$9 versus $37.75$0 for LLaMA-Mesh, CIDEr $37.75$1 versus $37.75$2, METEOR $37.75$3 versus $37.75$4, ROUGE $37.75$5 versus $37.75$6, and CLIP $37.75$7 versus $37.75$8; on ShapeNet generation it improves chair COV from $37.75$9 to $163.38$0 and FID from $163.38$1 to $163.38$2 (Fang et al., 2 Aug 2025).

These two systems define different notions of “native” 3D language modeling. CG-MLLM makes 3D native by learning over a specialized Spatial-VAE latent space with block-parallel autoregression, whereas MeshLLM makes 3D native by constraining meshes to a textual form that a standard LLM can decode directly. This suggests a persistent architectural tension between latent geometric fidelity and tokenizer compatibility (Huang et al., 29 Jan 2026, Fang et al., 2 Aug 2025).

5. Single-image 3D perception and RGBD generation

Another branch of 3DFroMLLM starts from 2D imagery and language, but targets explicitly 3D outputs. LLMI3D addresses monocular 3D perception with an MLLM that couples Spatial-Enhanced Local Feature Mining, 3D Query Token-Derived Info Decoding, and Geometry Projection-Based 3D Reasoning. The visual stack combines a low-resolution CLIP ViT branch with a high-resolution ConvNeXt branch and a depth-aware enhancement head, then uses cross-branch attention

$163.38$3

to align global ViT queries to local spatial grids. A special <pos> token activates a learnable 3D query token whose final hidden state feeds regression heads for normalized image center, dimensions, virtual-camera depth, and 6D allocentric rotation. The geometry module normalizes depth between a virtual and a real camera according to

$163.38$4

then converts back to the real depth and optionally averages with a 2D–3D height-based estimate. On IG3D-SUNRGBD, LLMI3D reports [email protected] $163.38$5, [email protected] $163.38$6, and DepthError $163.38$7; on IG3D-SUNRGBD-VQA it reports $163.38$8, $163.38$9, and $10.54\text{M}$ 0; on IG3D-Objectron it reports $10.54\text{M}$ 1, $10.54\text{M}$ 2, and improved open-vocabulary grounding relative to Mono3DVG and Text3D (Yang et al., 2024).

LDM3D occupies a different position. It belongs to the broader “3D from language” context but explicitly does not use an LLM. Instead, it is a KL-regularized latent diffusion model adapted from Stable Diffusion v1.4 that jointly generates an RGB image and a depth map from a text prompt. Depth maps are packed into RGB-like 3-channel arrays, concatenated with RGB images into a $10.54\text{M}$ 3 tensor, encoded by a modified KL-autoencoder to a $10.54\text{M}$ 4 latent, denoised by a U-Net conditioned by a frozen CLIP-text encoder, then decoded back to a 6-channel RGBD image and split into RGB plus a 16-bit grayscale depth map. On MS-COCO validation at $10.54\text{M}$ 5 with 50 DDIM steps, LDM3D reports FID $10.54\text{M}$ 6, IS $10.54\text{M}$ 7, and CLIP $10.54\text{M}$ 8, compared with SD v1.4 at FID $10.54\text{M}$ 9 and SD v1.5 at FID $12.11\text{M}$ 0; after alignment to ZoeDepth-N, it reports AbsRel $12.11\text{M}$ 1 and RMSE $12.11\text{M}$ 2, versus DPT-Large at AbsRel $12.11\text{M}$ 3 and RMSE $12.11\text{M}$ 4. The associated DepthFusion application projects RGB onto an equirectangular spherical polar object and uses depth values to manipulate sphere vertex distances, producing immersive 360-degree views in TouchDesigner (Stan et al., 2023).

This segment of the literature clarifies that 3DFroMLLM is not limited to free-form 3D asset synthesis. It also includes models that output metrically structured 3D quantities such as box centers, dimensions, orientations, and depth fields, and it includes systems where language conditioning is supplied by CLIP rather than by an LLM proper (Yang et al., 2024, Stan et al., 2023).

6. Procedural co-design and analysis-ready geometry

FloraForge shows that 3DFroMLLM can also mean LLM-assisted authoring of geometric programs rather than direct neural generation of shape tokens. The framework converts natural-language Plant Refinements into edits of a Procedural Plant Generator written in Python and a human-readable Plant Descriptor stored as YAML. The resulting geometry is continuous and parametric: stems, petioles, petiolules, blades, and leaflets are represented as tensor-product B-spline surfaces with explicit control points, knot vectors, degrees, and optional weights. The surface definition is

$12.11\text{M}$ 5

with NURBS specialization available when weights are used. Botanical morphology is encoded by explicit deformation functions for droop, camber, twist, V-fold, and localized hinge bending, and outputs are exported both as visualization meshes and as SMESH parametric meshes carrying per-organ metadata such as degrees, knots, control points, local transforms, and Plant Descriptor parameter stamps (Hadadi et al., 11 Dec 2025).

The workflow is explicitly two-stage. First, the LLM performs template co-design and code-level verification by synthesizing or editing the Procedural Plant Generator and seeding the Plant Descriptor. Second, the generator is fixed and the user performs manual instance fitting by adjusting only Plant Descriptor parameters, with agreement measured by symmetric Chamfer distance. For maize, manual Plant Descriptor tuning reduces Chamfer distance from approximately $12.11\text{M}$ 6 to approximately $12.11\text{M}$ 7, described as an approximately $12.11\text{M}$ 8 mean reduction across five genotypes; for three mung bean plants, the reported Chamfer distances are approximately $12.11\text{M}$ 9, $R\ 42.56$ 0, and $R\ 42.56$ 1. The framework is training-free, demonstrated on maize, soybean, and mung bean, and produces tessellated triangular meshes compatible with light simulation, computational fluid dynamics, and finite element analysis (Hadadi et al., 11 Dec 2025).

This line broadens the meaning of 3DFroMLLM substantially. Rather than asking a LLM to emit geometry directly, FloraForge uses the model as a co-designer of parameterized, botanically constrained geometric procedures. A plausible implication is that, in some scientific domains, editability and explicit control points are more central than end-to-end neural decoding (Hadadi et al., 11 Dec 2025).

7. Limitations, points of tension, and future directions

The present literature does not converge on a single solution to the core trade-offs of 3DFroMLLM. Encoder-free scene models reduce compute, but FFT mixing only approximates long-range interactions and may underperform on non-Euclidean, cross-object relations in cluttered scenes; Fase3D also notes serialization trade-offs, possible long-jump artifacts, and the fact that explicit stress tests on noise and occlusions are not separately reported (Mei et al., 26 Feb 2026). Native 3D MLLMs can generate high-fidelity assets, but CG-MLLM still trails specialized commercial 3D generators and attributes part of the gap to the Hunyuan3D-2.1 watertight thickening step and a token budget below 4k, while MeshLLM must simplify meshes and cap full-mesh representations at at most 800 faces to satisfy 8192-token limits, and it does not introduce explicit topology-validity losses for complex or non-manifold meshes (Huang et al., 29 Jan 2026, Fang et al., 2 Aug 2025).

Single-image systems preserve instruction following but inherit geometric assumptions. LLMI3D notes that extreme focal lengths, rolling shutter, severe lens distortions, textureless surfaces, and heavy occlusion can degrade performance, and its virtual-depth normalization assumes a specific white-box camera model (Yang et al., 2024). LDM3D reports a measurable decline in autoencoder reconstruction quality when depth is added, and the paper contains an unresolved parameter-count discrepancy: one section describes LDM3D as a “1.6 billion parameter KL-regularized diffusion model,” whereas an evaluation caption compares models “with the same number of parameters (1.06B)” (Stan et al., 2023). FloraForge depends on botanical priors and manual Plant Descriptor fitting, and explicitly notes that LLM hallucinations in code can occur, making code-level verification essential (Hadadi et al., 11 Dec 2025). JM3D and JM3D-LLM keep both SLIP and Vicuna frozen, which stabilizes training but may cap multimodal synergy; the paper identifies joint adapter-based tuning as a future direction (Ji et al., 2023).

Several future directions recur across these papers. They include larger 3D pretraining corpora, learned or adaptive serialization, hierarchical multi-resolution tokenization for ultra-large scenes, lightweight 3D-VAEs with higher precision, richer multimodal conditioning with images or point clouds, stronger 3D captioning data, and tighter integration of geometric constraints with language modeling (Mei et al., 26 Feb 2026, Huang et al., 29 Jan 2026, Fang et al., 2 Aug 2025, Ji et al., 2023). Taken together, the literature indicates that 3DFroMLLM is evolving along multiple fronts at once: efficient scene tokenization, geometry-aware regression, latent 3D autoregression, text-serialized topology modeling, and domain-specific procedural co-design. The unifying objective is not a single output format, but the relocation of 3D structure into the operational core of language-centered models.